Race composition and genetic diversity of a Puccinia striiformis f. sp. tritici population from Yunnan and Guizhou epidemiological regions in China in 2018

Abstract

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is an extremely destructive wheat disease worldwide. Yunnan and Guizhou provinces are the main overwintering regions for Pst and inoculum sources for the disease in China. Surveillance of race dynamics of the Pst population is essential for managing the disease in local and other wheat-growing regions of China. However, Pst population dynamics in this region is not monitored yearly. In this study, a Pst population of 113 isolates from Yunnan and Guizhou in 2018 were phenotyped on the two wheat differential sets and analyzed by 12 simple sequence repeat (SSR) markers. As a result, 25 races were identified from the Pst population on Chinese differentials and predominant races was CYR32 with a frequency of 17.70%. None of the isolates was virulent to Yr5 and Yr15. Only a few isolates showed virulence to YrTr1 and differentials Zhong 4. The Kosman index (K) of the Pst population was 0.292 on the Chinese differentials, and 0.274 on single Yr gene lines and additional differentials, respectively. In total, 64 MLGs were detected among 113 isolates and the expected heterozygosity (He) was 0.424. A close genetic relationship was detected between Lufeng (Yunnan) / Nayong (Guizhou), Fuyuan (Yunnan) / Bijie (Guizhou), and Shizong (Yunnan) / Xingyi (Guizhou). This study provided useful information on population structure and virulence to Yr genes in the Yunnan and Guizhou epidemiological regions, and will be used to guide the control of wheat stripe rust and targeted wheat breeding in this region.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42161-022-01273-1.

Introduction

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a serious windborne pandemic fungal disease of wheat in the world, especially in cool and humid regions. The disease usually results in huge yield loss of wheat worldwide in severe epidemic incidents, even crop failure as early infection on highly susceptible wheat cultivars (Chen 2005; Li and Zeng 2002). Development and utilization of resistant wheat varieties is the most economic and effective way for the control of stripe rust (Chen 2005; Li and Zeng 2002; Line 2002). However, the frequent appearance of new races often overcomes the resistance of wheat varieties becoming susceptible, threatening subsequent use of the wheat varieties. So far, in China severe nationwide stripe rust epidemics have occurred due to the emergence and rapid accumulation of new virulent races, resulting in the replacement of major cultivated wheat varieties across the country eight times (Li and Zeng 2002; Liu et al. 2017). Likewise, similar stripe rust epidemic incidents have also been reported in the U.S. (Chen 2005), Australia (Wellings and McIntosh 1990), and other countries (Wellings 2011).

In recent years, a new race, provisionally named G22-9 with virulence to wheat cultivar Chuanmai 42 (Yr26, Yr26 = Yr24) (Liu et al. 2010), has developed to be a predominant race in the Chinese Pst population after undergoing a rapid increasing, and was designated as CYR34 (Liu et al. 2017). The race and its lineage races have led to the breakdown of the resistance of more than 90% of major cultivated cultivars in China (Liu et al. 2017), becoming a serious threat to wheat production regions nationwide.

Wheat varieties with major resistance genes are vulnerable to attack by Pst to break down the resistance, and the pathogen evolves to counteract other control strategies (Li and Zeng 2002; Bayles et al. 2000; Tian et al. 2016). Monitoring of race dynamics, and prediction of Pst races are essential for making effective disease management strategies by anticipatory resistance breeding and timely detection of new virulent races (McIntosh and Brown 1997). Importantly, the knowledge of the genetic diversity and population structure of plant pathogen are important for understanding the dynamics of genetic variability and evolutionary potential (McDonald and Linde 2002).

Wheat stripe rust is an important disease affecting wheat production in Yunnan and Guizhou which are neighbour each other. In this area, complex geographic features, mild weather conditions, and overlapping of wheat growth influence the disease with unique epidemic characteristics distinguished from other main epidemiological regions in China (Liu et al. 2011; Li and Zeng 2002; Zeng and Luo 2006; Wang et al. 2007). Local epidemics of wheat stripe rust can cause a yield reduction of 20%-30%, even more in this area (Li and Zeng 2002; Li 2004; Zuo et al. 2011). Epidemiological regions of Yunnan and Guizhou, belonging to the southwestern epidemiological region of wheat stripe rust in China, is an independent epidemiological region. In this area, Pst can over-summer and over-winter to complete the disease cycle. Currently, wheat stripe rust has been an increasing concern in both provinces due to more frequent severe epidemics (Chen et al. 2016; Li 2004; Li et al. 2009, 2018). However, discontinuity of monitoring Pst populations in Yunnan and Guizhou resulted in the absence for understanding population structure and race dynamics in this region. Race surveillances demonstrated that the frequencies of various races in Yunnan differed from those in main epidemic regions of China (Yang and Wu 1990; Wu et al. 1993). However, in recent years, studies on both provincial Pst populations are limited for better understanding virulence patterns, genetic structure, affecting the control of wheat stripe rust via utilization of wheat resistance varieties in this region. Thus, the main objectives of this study were to identify the virulence patterns, race composition, occurrence frequencies, and virulence diversity of Pst isolates collected from Yunnan and Guizhou provinces of China in 2018, and to analyze genetic structure of the Pst population based on genetic diversity, genetic variation and migration.

Materials and methods

Sample collection

Stripe rust-diseased wheat leaves with uredinia were collected from thirteen counties of Yunnan and Guizhou provinces, including ten counties of Guizhou and three of Yunnan, from February to March 2018 (Table S1). In each county, stripe rust samples were collected from 1 to 4 sites, and the interspace distance between two sampling sites was at least 10 km. Samples obtained from each county were regarded as a subpopulation. Stripe rust samples were maintained at room temperatures until completely dry, packaged in a paper bag, and then kept in a desiccator with silica gels at 4 °C for later use.

Obtainment of pure Pst isolates

Seeds of wheat cultivar Mingxian 169, highly susceptible to all Chinese Pst races reported previously, were grown in a small plastic pot filled with commercial potting mix (Inner Mongolia Mengfei Biotech Co., Ltd., Huhhot, Inner Mongolia, China). All pots were moved to the trays and placed in a rust-free growth chamber in a greenhouse for the growth of seedlings at a dual temperature and photoperiod regime of 16 °C for 16 h in the light and 13 °C for 8 h in the darkness for 10 d prior to inoculation. Leaf samples of stripe rust were taken out of the desiccator and flushed with tap water, and put on 2–3 layers of wetted filter paper in a Petri dish. The leaf samples were transferred into an incubator for hydration of fresh urediniospores on uredial sori in the dark at 4 °C in a freezer for 2 to 3 h. A single uredinium was picked by an unfolded office pin onto the surface of 10-day-old wheat Mingxian 169 seedlings. Picking was repeated three to five times to ensure successful inoculation. Inoculated plants were covered using a transparent plastic cylinder to avoid cross contamination and incubated for 24 h at 10 °C in darkness in a dew chamber (ARC-36D-I, Percival, IA, U.S.A.). After incubation, plants were transferred to a rust-free, condition-controlled growth chamber in which the regime of a diurnal cycle of temperatures at 13 ℃ for 8 h of darkness and 16 °C for 16 h in daytime was used. For each sample, as flecks appeared on inoculated leaves, one infected leaf was kept until sporulation and the others were cut off. Fresh urediniospores were collected from an individual infected wheat leaf into a glass tube by gently tapping. The increase of urediniospores was conducted by re-inoculation.

Virulence test

The routine Chinese differential set, including 19 wheat genotype lines, was used to differentiate races of Pst isolates. A parallel experiment with 24 combined differential genotypes, including 17 single Yr gene lines and 7 additional genotype lines (Table 1), was used as additional lines for assessing avirulence and virulence of all isolate collections by phenotyping. Each of every four genotypes was grown in each of four corners of a plastic pot with along the clockwise direction of a cross (+), labelled by the given code of genotypes, and then cultivated under the same cultivation conditions as described above. Two-leaf stage wheat seedlings were inoculated with the mixture of urediniospores and talc at a ratio of 1:30 (vol. / vol.) and moved into a dew chamber for incubation after spraying deionized water to form water film. Mingxian 169 wheat seedlings was used as a positive check for successful inoculation at each of tests. Infection types (ITs) of genotype seedlings were scored at 20 to 22 days after inoculation based on a 0-to-9 scale described by Line and Qayoum (1992). ITs 0 to 6 were indicative of avirulence and those 7 to 9 were virulent.

Table 1.

Ninteen Chinese differentials wheat genotypes for differentiating Puccinia striiformis f. sp. tritici races, and seventeen single Yr-gene lines of wheat and seven additional genotypes lines for avirulence / virulence phenotyping

Chinese differentials		Yr single-gene lines		Additional genotype lines
Name	Yr gene	Name	Yr gene	Name	Yr gene
Trigo Eureica	Yr6	AvSYr1NILa	Yr1	Kalyansona	Yr2
Fulhard	Unknown	AvSYr5NIL	Yr5	Hugenoot	Yr25
Lutescenes 128	Unknown	AvSYr6NIL	Yr6	AvSYr28NIL	Yr28
Mentana	Unknown	AvSYr7NIL	Yr7	AvSYr29NIL	Yr29
Virigilio	YrVir1,YrVir2	AvSYr8NIL	Yr8	Vilmorin 23	Yr3a,Yr4a, YrV23
Abbondanza	Unknown	AvSYr9NIL	Yr9	AvSYrANIL	YrA
Early Piemium	Unknown	AvSYr10NIL	Yr10	92R137	Yr26 (= Yr24)
Funo	YrA, +	AvSYr15NIL	Yr15
Danish 1	Yr3	AvSYr17NIL	Yr17
Jubilejina 2	YrJu1,YrJu2,YrJu3,YrJu4	AvSYr27NIL	Yr27
Fengchan 3	Yr1	AvSYr32NIL	Yr32
Lovrin 13	Yr9, +	AvS/IDO377s (F3-41–1)	Yr43
Kangyin 655	Yr1,YrKy1,YrKy2	AvS/Zak (1–1-35-line1)	Yr44
Suwon 11	YrSu	AvSYrSPNIL	YrSP
Zhong 4	Unknown	AvSYrTres1NIL	YrTr1
Lovrin 10	Yr9	AvS/Exp1/1-1Line 74	YrExp2
Hybrid 46	Yr3b, Yr4b	Tyee	YrTye(Yr76)
Triticum spelta album	Yr5
Guinong 22	Yr10, Yr26

^a NIL = near isogenic lines with the wheat variety Avocet Susceptible (AvS) genetic background

DNA extraction

Genomic DNA of urediniospores of each of overall Pst isolates was extracted by the CTAB method described by Aljanabi and Martinez (1997) with modifications. Urediniospores (~ 25 mg dry weight) were placed into a 2-ml clean microcentrifuge tube and then put 3 stainless steel balls with a size of 3 mm in diameter into the tube. The tubes were fixed and urediospores were crushed to fine powder by high-speed shaking at a frequency of 25 Hz for 2 min using a Tissue Lyser II (Qiagen, Germany). After shaking, a volume of 600 μl of 2% CTAB solution (0.05 M CTAB, 0.14 M NaCl, 0.2 M Tris–HCl pH 8.0, and 20 mM EDTA pH 8.0) was added and the mixture were blended well. The mixture was incubated at 65 °C for 1 h in a water bath and reversed at intervals of 20 min. After incubation, an additional 600 μl of mixed solution of chloroform/isoamyl alcohol (vol.: vol. = 24: 1) was pipetted into the tube and mixed completely for 5 s using a vortex. The mixture was centrifuged for 10 min at 12,000 rpm at room temperatures. The upper was transferred into an autoclaved 1.5 ml microcentrifuge tube by a pipette and an extra 600 μl of chloroform was added. The mixture was blended completely using a vortex and centrifuged for 10 min at 12,000 rpm at room temperatures. The aqueous phase was pipetted into a clean 1.5 ml microcentrifuge tube and added with 600 μl of pre-cooled isopropanol at -20 °C. The mixture was incubated at -20 °C for overnight and then centrifuged for 10 min at 12,000 rpm. The supernatant was discarded and the precipitate was washed first with 75% (vol: vol) ethanol solution and then 100%. After drying for 2 h inside a clean bench at room temperatures, the precipitate was dissolved with adding a 50 μl of 1 × Tris–EDTA buffer (10 mM Tris–HCl and 1 mM EDTA, pH 8.0). DNA concentration was measured with a spectrophotometer (ND-1000, Bio-Rad Laboratories, Hercules, CA, U.S.A.), and then diluted to a concentration of 50 ng/μl by adding autoclaved double-distilled water for polymerase chain reaction (PCR) amplification.

Microsatellite genotyping

Twelve pairs of simple-sequence repeat (SSR) primers, RJ27 (Enjalbert et al. 2002), RJ10N, RJ11N, RJ2N, CPS27 (Bahri et al. 2009), PstP033 (Cheng et al. 2012), and SUNIPst15-30, SUNIPst13-42, SUNIPst15-26, SUNIPst10-48, SUNIPst11-04, and SUNIPst10-06 (Tian et al. 2016), were used to analyze the genetic structure of all Pst isolates. Fluorescence adapter was added to the 5’-end of forward primers (Shanghai Sangon Bio-tech Co., Ltd., Shanghai, China). The size of PCR products was detected and analyzed using a DNA Analyzer (3730XL, Applied Biosystems, Waltham, MA, U.S.A.). SSR amplicons were evaluated for genotypes using the software GeneMarker HID (Holland and Parson 2011).

Data analysis

Virulence phenotypes and virulence diversity of all isolates were determined using the software VAT 1.0 (Kosman and Leonard 2007). Virulence association was analyzed by Genepop software (Raymond and Rousset 1995) and the null hypothesis that the corresponding virulence are randomly distributed across all isolates was rejected with a high degree of confidence (P < 0.05). Polymorphism information content (PIC) value of per locus in the Pst population was confirmed using PIC-CALC 0.6 (Nagy et al. 2012). The multilocus genotypes (MLGs) were performed according to microsatellite marker loci using the Genclone software (Arnaud-haond and Belkhir 2007). Relationships among the individuals of overall isolates were visualized with minimum scanning networks (MSN) that was generated using the statistical language R packages igraph and Poppr (Csárdi and Nepusz 2006; Kamvar et al. 2014). Number of alleles per locus (Na), expected heterozygosity (He, genetic diversity), Shannon’s information index (I), analysis of molecular variance (AMOVA) at the level of probability of 0.001 (P < 0.001), and Mantel test between virulence phenotypes based on simple matching and genotype based on Nei’s genetic distance were performed using GenAlEx 6.5 (Peakall and Smouse 2012). Population sub-division was analyzed using the software STRUCTURE 2.2 (Pritchard et al. 2000). The Monte Carlo Markov Chain (MCMC) scheme was run as recommended by default and with K values ranging from 1 to 13 with at least 20 repetitions to check the convergence of likelihood value for each K value. The optimal number of population subdivisions was determined by plotting the graph of estimated values of logarithm likelihood for each K value, and by selecting the value of the maximum logarithm likelihood. For each K value, the Large K Greedy algorithm implemented in the program CLUMPP version 1.1 was used to search for the best alignment of multiple replicate cluster analyses (Jakobsson and Rosenberg 2007). The population structure was visualized using DISTRUCT version 1.1 (Rosenberg 2004).

Results

Race identification of Pst isolates

In 2018, 25 races were detected from 113 isolates collected from Yunnan and Guizhou provinces based on avirulence / virulence tests to the 19 Chinese differential genotypes set (Table 2). Race CYR32 was predominant with a frequency of 17.7%. Seven races, including Hy46-8 (5.31%), HY46-182 (2.65%), CYR33 (2.65%), Su11-14 (2.65%), Su11-388 (4.42%), CYR34 (2.65%) and Gui22-333 (4.42%), were regular and each had a frequency of above 2%. All isolates were categorized into Hybrid 46 group (HyG), Su11 group (SuG) and Guinong 22 group (G22G) according to virulence on differential genotypes Hybrid 46, cv. Suwon 11and cv. Guinong 22. Sixty isolates were identified as HyG with frequency of 53.10%, and thirty nine of the 60 isolates were designated as 10 races (CYR32, HY46-4, HY46-7, HY46-8, HY46-9, HY46-28, HY46-33, HY46-103, HY46-103, and HY46-182). Thirty one isolates were identified as SuG and nineteen ones were grouped into 9 races, including CYR33, Su11-14, Su11-41, Su11-68, Su11-139, Su11-159, Su11-192, Su11-254, and Su11-388. Twenty two isolates were identified as G22G with a frequency of 19.5% and fourteen of them were sorted into 7 races consisting of CYR34, G22-13, G22-14, G22-74, G22-333, G22-257 and G22-283. Among HyG, SuG and G22G, 41 isolates that have not reported previously in China, were new races.

Table 2.

Races of Puccinia striiformis f. sp. tritici population from Yunnan and Guizhou provinces in China in 2018 and their frequency, and avirulence (A) / virulence (V) on the Chinese differential genotypes

Pathotype groupa	Racesb	Frequency (%)	Avirulence (A) and virulence (V) of races on Chinese differential genotypesc
			1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
HyG	CYR32	17.7	V	V	V	V	V	V	V	V	V	V	V	V	V	V	A	V	V	A	A
	HY46-4–1	1.77	V	V	V	V	V	V	V	V	V	V	V	A	V	V	A	VA	V	A	A
	HY46-7	1.77	A	V	V	V	V	V	V	V	V	V	V	V	V	V	A	V	V	A	A
	HY46-8	5.31	V	V	V	V	VA	V	V	V	V	V	V	V	A	V	A	V	V	A	A
	HY46-9	0.88	V	V	V	V	V	V	V	V	V	V	V	A	V	V	A	A	V	A	A
	HY46-28	0.88	V	V	V	V	A	V	V	V	A	V	V	V	V	V	A	V	V	A	A
	HY46-33	1.77	V	V	V	V	A	V	V	V	V	V	V	V	V	V	A	V	V	A	A
	HY46-103	1.77	A	V	A	V	A	V	A	V	A	V	V	V	V	V	A	V	V	A	A
	HY46-182	2.65	V	V	V	V	V	V	V	V	A	V	V	V	A	V	A	V	V	A	A
SuG	CYR33	2.65	V	V	V	V	V	V	V	V	V	V	V	V	V	V	A	V	A	A	A
	Su11-14	2.65	A	V	V	V	A	V	V	V	V	V	V	V	V	V	A	V	A	A	A
	Su11-41	1.77	V	V	V	V	V	V	V	V	V	V	V	V	A	V	A	V	A	A	A
	Su11-68	0.88	V	V	V	V	V	V	V	V	A	V	V	V	V	V	A	V	A	A	A
	Su11-139	0.88	A	V	V	V	V	V	V	V	V	V	V	A	V	V	A	V	A	A	A
	Su11-159	0.88	V	V	V	V	A	V	V	V	V	V	V	V	V	V	A	V	A	A	A
	Su11-192	0.88	V	V	V	V	V	V	V	V	V	V	V	V	V	V	A	A	A	A	A
	Su11-254	1.77	A	V	V	V	A	V	V	V	V	V	V	A	V	V	A	V	A	A	A
	Su11-388	4.42	A	V	V	V	V	V	V	V	V	V	V	V	V	V	A	V	A	A	A
G22G	CYR34	2.65	V	V	V	V	V	V	V	V	V	V	V	V	V	V	A	V	V	A	V
	G22-13	1.77	A	V	V	V	V	V	V	V	V	V	V	V	A	V	A	V	V	A	V
	G22-14	0.88	V	V	V	V	V	V	V	V	A	V	V	V	V	V	A	V	V	A	V
	G22-74	0.88	V	V	V	V	V	V	V	V	V	V	V	A	V	V	A	V	V	A	V
	G22-257	0.88	A	V	V	V	V	V	V	V	A	V	V	V	V	V	A	V	V	A	V
	G22-283	0.88	A	V	V	V	A	V	V	V	V	V	V	V	V	V	A	V	A	A	V
	G22-333	4.42	A	V	V	V	V	V	V	V	V	V	V	V	V	V	A	V	V	A	V

^a HyG = Pathotype group with virulence to differential genotype Hybrid 46. SuG = Pathotype group with virulence to differential genotype Suwon 11. G22G = Pathotype group with virulence to differential genotype Guinong 22

^b CYR = Chinese Yellow Rust. Hy = Isolates virulent to differential genotype Hybrid 46 (Hy). Su = Isolates virulent to differential genotype Suwon 11. G22 = Isolates virulent to differential genotype Guinong 22

^c Chinese differential genotypes: 1 = Trigo Eureka, 2 = Fulhard, 3 = Lutescens 128, 4 = Mentana, 5 = Virgilio, 6 = Abbondanza, 7 = Early Premium, 8 = Funo, 9 = Danish 1, 10 = Jubilejina 2, 11 = Fengchan 3, 12 = Lovrin 13, 13 = Kangyin 655, 14 = Shuiyuan 11, 15 = Zhong 4, 16 = Lovrin 10, 17 = Hybrid 46, 18 = Triticum spelta album, and 19 = Guinong 22. V = virulent, A = avirulent, and AV and VA indicate varied reactions

Simultaneously, 66 virulence patterns (VPs), VP1-VP66, were detected based on phenotypes of 113 isolates on the 17 single Yr gene lines of wheat and 7 additional wheat genotypes (Table 3). Among these VPs, VP29 has the narrowest virulence spectrum with virulence to 10 of the 24 Yr genes. While VP2 exhibited the widest virulence spectrum showing virulence to 20 of these Yr genes. VP1, showing the highest rate of 17.70%, was virulent to 17 resistance genes including Yr1, Yr6, Yr7, Yr9, Yr17, Yr27, Yr43, Yr44, YrSP, YrExp2, YrTye (Yr76), Yr2, Yr25, Yr28, Yr29, Vilmorin 23 (Yr3a, Yr4a, YrV23) and YrA (Yr74, Yr75), and avirulent to Yr5, Yr8, Yr10, Yr15, Yr32, YrTr1 and Yr26 (Table 3).

Table 3.

Races, frequencies, virulence complex, and distribution of Puccinia striiformis f. sp. tritici detected in Guizhou and Yunnan of China in 2018 based on single Yr gene lines and additional genotype lines

Virulence patterns	Virulence or avirulence formula on Yr genes	Virulence complex	Frequency (%)
VP1	1,6,7,9,17,27,43,44,SP,Exp2,Tye,2,25,28,29,3,A/5,8,10,15,32,76,26	17	17.7
VP2	1,6,7,9,10,17,27,32,43,44,SP,Exp2,Tye,2,25,28,29,3,A,26/5,8,15,76,	20	3.5
VP3	1,7,9,10,17,27,32,44,Exp2,Tye,2,25,28,29,3,A,26/5,6,8,15,43,SP,76	17	3.5
VP4	1,7,8,9,17,27,44,SP,Tye,2,25,28,29,3,A/5,6,10,15,32,43,76,Exp2,26	15	3.5
VP5	6,7,9,17,27,43,44,SP,Exp2,Tye,2,25,28,29,3,A/1,5,8,10,15,32,76,26	16	3.5
VP6	1,6,7,8,9,17,27,43,44,Exp2,2,25,28,29,A/5,10,15,32,SP,76,Tye,3,26	15	3.5
VP7	1,8,9,17,27,44,SP,Tye,2,25,28,29,3,A/5,6,7,10,15,32,43,76,Exp2,26	14	2.7
VP8	1,6,7,9,17,27,44,SP,Exp2,Tye,2,25,28,29,3,A/5,8,10,15,32,43,76,26	16	2.7
VP9	1,6,7,9,17,27,43,44,SP,Tye,2,25,28,29,3,A/5,8,10,15,32,76,Exp2,26	16	2.7
VP10	1,6,7,9,44,Exp2,2,25,28,29,3,A/5,8,10,15,17,27,32,43,SP,76,Tye,26	12	1.8
VP11	1,7,8,9,10,17,27,32,44,SP,Exp2,Tye,2,25,28,29,3,A,26/5,6,15,43,76	19	1.8
VP12	1,6,7,9,17,27,43,44,SP,Exp2,Tye,2,28,29,3,A/5,8,10,15,32,76,25,26	16	1.8
VP13	1,6,7,9,43,44,SP,Exp2,Tye,2,25,28,29,3,A/5,8,10,15,17,27,32,76,26	15	1.8
VP14	1,6,7,9,10,17,27,32,43,44,SP,Exp2,Tye,2,25,29,3,A,26/5,8,15,76,28	19	1.8
VP15	1,6,7,9,27,43,44,SP,Exp2,Tye,2,25,28,29,3,A/5,8,10,15,17,32,76,26	16	1.8
VP16	1,7,9,10,17,27,32,44,Exp2,2,25,28,29,3,A/5,6,8,15,43,SP,76,Tye,26	15	1.8
VP17	6,8,9,27,44,SP,Tye,28,29,3,A/1,5,7,10,15,17,32,43,76,Exp2,2,25,26	11	0.9
VP18	1,6,7,9,17,27,43,44,SP,Exp2,Tye,2,25,29,A/5,8,10,15,32,76,28,3,26	15	0.9
VP19	1,6,7,8,17,27,43,44,SP,Exp2,Tye,2,25,28,29,A/5,9,10,15,32,76,3,26	16	0.9
VP20	1,6,7,9,10,17,27,32,44,SP,Exp2,2,28,29,3,A/5,8,15,43,76,Tye,25,26	16	0.9
VP21	1,6,7,9,10,17,27,43,44,SP,Exp2,Tye,2,25,29,A,26/5,8,15,32,76,28,3	17	0.9
VP22	1,7,9,10,17,27,32,44,Exp2,Tye,2,25,28,29,A,26/5,6,8,15,43,SP,76,3	16	0.9
VP23	1,6,7,9,17,27,43,44,Exp2,2,25,28,29,A/5,8,10,15,32,SP,76,Tye,3,26	14	0.9
VP24	1,7,9,10,17,32,44,Exp2,2,25,28,29,3,A,26/5,6,8,15,27,43,SP,76,Tye	15	0.9
VP25	1,6,7,9,17,27,43,44,SP,Exp2,Tye,2,25,28,29,3,A,26/5,8,10,15,32,76	18	0.9
VP26	1,6,7,8,9,17,44,SP,Tye,2,25,28,3,A/5,10,15,27,32,43,76,Exp2,29,26	14	0.9
VP27	6,9,17,27,43,44,SP,Exp2,Tye,2,25,28,29,3,A/1,5,7,8,10,15,32,76,26	15	0.9
VP28	1,7,8,9,17,27,44,SP,Exp2,Tye,25,28,29,A/5,6,10,15,32,43,76,2,3,26	14	0.9
VP29	1,8,9,17,44,SP,25,28,29,A/5,6,7,10,15,27,32,43,76,Exp2,Tye,2,3,26	10	0.9
VP30	1,6,7,9,17,27,44,SP,2,25,28,29,A/5,8,10,15,32,43,76,Exp2,Tye,3,26	13	0.9
VP31	1,6,7,8,9,17,27,44,SP,2,25,28,29,A/5,10,15,32,43,76,Exp2,Tye,3,26	14	0.9
VP32	1,6,7,9,10,17,27,32,44,SP,Exp2,2,25,28,29,3,A,26/5,8,15,43,76,Tye	18	0.9
VP33	1,7,8,9,17,27,SP,Tye,2,25,28,29,3,A/5,6,10,15,32,43,44,76,Exp2,26	14	0.9
VP34	1,6,7,9,17,27,32,43,44,SP,Exp2,Tye,2,25,28,29,3,A/5,8,10,15,76,26	18	0.9
VP35	1,7,9,10,17,27,32,44,SP,Exp2,Tye,2,25,28,29,3,A,26/5,6,8,15,43,76	18	0.9
VP36	1,6,7,9,17,27,43,44,SP,Exp2,2,25,28,29,A/5,8,10,15,32,76,Tye,3,26	15	0.9
VP37	1,7,9,10,17,27,32,44,Exp2,2,25,28,29,3,A,26/5,6,8,15,43,SP,76,Tye	16	0.9
VP38	1,8,9,17,27,44,SP,Tye,2,25,28,29,A/5,6,7,10,15,32,43,76,Exp2,3,26	13	0.9
VP39	1,6,7,17,27,43,44,SP,Exp2,Tye,2,25,28,29,A/5,8,9,10,15,32,76,3,26	15	0.9
VP40	6,7,8,17,43,44,Exp2,2,25,28,29,A/1,5,9,10,15,27,32,SP,76,Tye,3,26	12	0.9
VP41	1,7,8,9,17,27,44,Exp2,Tye,2,25,28,29,A/5,6,10,15,32,43,SP,76,3,26	14	0.9
VP42	1,6,8,9,17,27,44,SP,Tye,2,25,28,29,A/5,7,10,15,32,43,76,Exp2,3,26	14	0.9
VP43	1,8,9,17,27,SP,Tye,2,25,28,29,3,A/5,6,7,10,15,32,43,44,76,Exp2,26	13	0.9
VP44	1,6,7,8,17,27,43,44,Exp2,2,25,28,29,A/5,9,10,15,32,SP,76,Tye,3,26	14	0.9
VP45	6,7,8,17,27,43,44,Exp2,2,25,28,29,A/1,5,9,10,15,32,SP,76,Tye,3,26	13	0.9
VP46	1,6,7,9,17,43,44,SP,Exp2,Tye,2,25,29,3,A/5,8,10,15,27,32,76,28,26	15	0.9
VP47	1,6,7,27,43,44,SP,Exp2,Tye,2,25,28,29,A/5,8,9,10,15,17,32,76,3,26	14	0.9
VP48	1,6,8,9,17,27,32,44,SP,Tye,2,25,28,A/5,7,10,15,43,76,Exp2,29,3,26	14	0.9
VP49	1,6,7,9,17,27,43,44,SP,76,Exp2,Tye,2,25,28,29,3,A/5,8,10,15,32,26	18	0.9
VP50	1,6,7,9,27,43,44,Exp2,2,25,28,29,A/5,8,10,15,17,32,SP,76,Tye,3,26	13	0.9
VP51	1,7,9,10,17,32,44,SP,Exp2,2,25,28,29,3,A,26/5,6,8,15,27,43,76,Tye	16	0.9
VP52	1,6,7,9,43,44,SP,76,Exp2,Tye,2,25,28,29,A/5,8,10,15,17,27,32,3,26	15	0.9
VP53	1,6,7,8,9,17,27,44,SP,Tye,2,25,28,A/5,10,15,32,43,76,Exp2,29,3,26	14	0.9
VP54	1,6,9,17,27,43,44,SP,Exp2,Tye,2,25,28,29,3,A/5,7,8,10,15,32,76,26	16	0.9
VP55	1,6,7,8,9,17,27,43,Exp2,2,25,28,29,A/5,10,15,32,44,SP,76,Tye,3,26	14	0.9
VP56	1,6,7,17,43,44,SP,Exp2,Tye,2,25,28,29,A/5,8,9,10,15,27,32,76,3,26	14	0.9
VP57	1,6,7,9,44,SP,Exp2,Tye,2,25,28,29,3,A/5,8,10,15,17,27,32,43,76,26	14	0.9
VP58	1,7,9,10,17,27,32,43,44,SP,Exp2,2,25,28,29,3,A,26/5,6,8,15,76,Tye	18	0.9
VP59	1,7,9,10,17,27,32,44,Exp2,2,25,28,29,3/5,6,8,15,43,SP,76,Tye,A,26	14	0.9
VP60	1,7,9,10,17,27,43,44,SP,Exp2,Tye,2,25,28,29,3,A/5,6,8,15,32,76,26	17	0.9
VP61	1,6,7,9,10,17,32,44,Exp2,2,25,28,29,3,A,26/5,8,15,27,43,SP,76,Tye	16	0.9
VP62	1,7,8,9,17,27,44,Tye,2,25,28,29,3,A/5,6,10,15,32,43,SP,76,Exp2,26	14	0.9
VP63	1,6,7,9,17,27,44,SP,76,Exp2,Tye,2,25,28,29,3,A/5,8,10,15,32,43,26	17	0.9
VP64	1,8,17,27,44,SP,Tye,2,25,28,29,A/5,6,7,9,10,15,32,43,76,Exp2,3,26	12	0.9
VP65	1,7,9,10,17,27,32,44,Exp2,Tye,2,25,28,29,3,A/5,6,8,15,43,SP,76,26	16	0.9
VP66	1,7,10,17,27,32,44,SP,Tye,2,25,28,29,3,A/5,6,8,9,15,43,76,Exp2,26	15	0.9

Virulence frequencies and associations

Virulence frequencies of the 113 Pst isolates presented a remarkable difference on each wheat genotype of the 19 Chinese differential genotype lines (Fig. 1). The virulence frequencies above 80%, were observed on differential genotypes including Fengchan 3, Funo, Fulhard, Abbondanza, Mentana, Suwon 11, Lovrin 10, Lutescenes 128, Jubilejina 2, Early Piemium, and Lovrin 13. Moderate virulence frequencies ranging from 50 to 80% were recorded on Danish 1, Virigilio, Hybrid 46, Kangyin 655, and Trigo Eureica. On the other hand, virulence frequencies less than 20% were detected on differential genotypes Guinong 22 and Zhong 4. All isolates were avirulent to Triticum spelta album (Yr5) (Fig. 1).

Fig. 1.

Virulence frequency of Pst isolates from Yunnan and Guizhou provinces to the 19 Chinese differential genotypes, 17 single Yr gene lines and 7 additional lines in Yunnan and Guizhou

For the Pst populations tested on additional genotypes lines, virulence frequencies were more than 80% on Yr27, Yr7, Yr17, Yr9, Yr1, Yr28, Yr25, Yr2, Yr44, Yr29, and YrA (Yr74, Yr75). Moderate virulence frequency ranging from 50 to 80% were identified on Yr43, Yr4, Yr6, Yr3a and Yr4a, YrSP, YrTye (Yr76), and YrExp2. Virulence frequencies were low on Yr26, Yr32, Yr10, Yr8, and YrTr1. None of the isolates was virulent to Yr5 and Yr15 (Fig. 1).

In term of phenotypes of the Pst population, strong positive virulence associations were detected between genes or genotypes, viz. Yr6, Yr8, Yr10, Yr26, Yr32, and Trigo Euraka (P < 0.01). Negative associations were detected for virulence to Yr1, Yr44, YrTr1, Yr25, YrA, and Yr28. Virulence to vilmorin 23 (Yr3a, Yr4a, YrV23) had a positive association with most of genes or genotypes, such as Yr8, Yr9, Yr10, YrSP, YrTye and Chinese differential genotypes Lutescenes 128, Early Piemium, Funo, Danish 1, Jubileji, and Lovrin 10. While virulence against Yr2 was only strongly correlated with Yr7. Virulence associations were observed between Yr27 and Yr17. Likewise, virulence against YrTye, and Yr29 was related with YrExp2, and Mentana. For genes / genotypes with low or moderate virulence frequencies, virulence to Yr43 was in relation to YrExp2, YrSP, Yr32, Yr10, Yr8, Yr7, Yr6, Hybrid 46 and Trigo Euraka.

Diversity within and relationships between populations

Estimations of the virulence diversity within the Pst population were performed (Table 4). Diversity of Pst isolates from Guizhou province was higher than that from Yunnan province regardless of the Chinese differential genotypes (K = 0.300, K = 0.259, respectively), or the single Yr gene lines and additional set (K = 0.283, K = 0.243, respectively), as well as the combination of both differentials (K = 0.291, K = 0.25, respectively). Among 13 counties, the highest virulence diversity (0.391) was observed in Guiyang population of Guizhou province based on the combination of the two differential genotypes. Meanwhile, the lowest diversity (0.122) was detected in Weining population of Guizhou province. Diversity comparison were made between the two differential genotypes to show that the difference was not significant (P = 0.732).

Table 4.

Measures of virulence diversity within population based on the Chinese differentials and single Yr-gene lines and the additional genotype lines

	Location	Kosman index (K) b
		Chinese differentials	Single Yr gene lines and additional genotype lines	All differential sets
Provinces	2	0.292	0.274	0.282
	Guizhou	0.300	0.283	0.291
	Yunnan	0.259	0.243	0.250
Counties	13	0.292	0.274	0.282
	Lufeng	0.228	0.139	0.178
	Fuyuan	0.158	0.15	0.153
	Shizong	0.25	0.229	0.238
	Qixingguan	0.211	0.167	0.186
	Nayong	0.346	0.226	0.279
	Hezhang	0.114	0.201	0.167
	Weining	0.132	0.115	0.122
	Guiyang	0.463	0.333	0.391
	Liuzhi	0.228	0.292	0.264
	Panzhou	0.301	0.298	0.299
	Qinglong	0.263	0.259	0.261
	Xingren	0.126	0.133	0.13
	Xingyi	0.175	0.306	0.248

^a Nei measure of virulence diversity H_S(G) =$\sum [1-{q_i}^2-\left(1,-,q_i,{)}^2\right]/k,$ 1$\le i\le k$

^b Kosman index K(G) =$\sum \min [2q_i,2\left(1,-,q_i,)\right]/k,$ 1$\le i\le k$. When a sample collected from population G, which consists of n individuals tested on k differentiating factors and represented by binary patterns. We denoted by q_i the frequency of appearance 1 at the i_th differentiating factor, i = 1,2, …, k. The number of individuals and frequency of genotype r in population P are denoted by n_rand p_r, r = 1, 2, …, s, respectively. Where “s” is the number of different genotypes in G

The unweighted pair-group method with arithmetic means (UPGMA) dendrogram established separation of the Pst population from 13 counties into two clusters (Fig. 2), one consisted of Pst isolates from Shizong in Yunnan, Xingyi and Panzhou in Guizhou, the other included those from the remaining 8 counties, viz. Lufeng and Fuyuan in Yunnan, and Nayong, Qinglong, Bijie, Hezhang, Xingren, Weining, and Guiyang in Guizhou. The identity coefficient index was higher than 0.9.

Fig. 2.

Clustering Pst isolates from different counties in Yunnan and Guizhou provinces in 2018. YN = Yunnan province. GZ = Guizhou province

Polymorphism information content

In total, 53 alleles were generated using the 12 SSR polymorphic markers and the number of detected alleles of each locus ranged from 2 (RJ27, SUNIPst15-26, and CPS27) to 8 (SUNIPst10-48) among Pst populations from all counties (Table 5). The polymorphism information content (PIC) of PstP033, RJ27, RJ10N, SUNIPst15-26, and CPS27 was less than 0.25. Those of RJ2N and SUNIPst11-04 ranged from 0.25 to 0.5. PIC of SUNIPst15-30, RJ11N, SUNIPst13-42, SUNIPst10-48, and SUNIPst10-06 was greater than 0.5 (Table 5). These PIC values indicated most selected markers were highly polymorphic.

Table 5.

Number of alleles and polymorphism information content (PIC) of per locus in Puccinia striiformis f. sp. tritici population from Yunnan and Guizhou in China in 2018

Marker loci	No. of alleles	PICa
PstP033	3	0.243
RJO27	2	0.201
SUNIPst15-30	3	0.513
RJN10	3	0.071
RJN11	7	0.713
RJN2	7	0.327
SUNIPst13-42	7	0.500
SUNIPst15-26	2	0.147
SUNIPst10-48	8	0.672
CPS15	2	0.179
SUNIPst11-04	3	0.308
SUNIPst10-06	6	0.617
Total	53

^a PIC = 1-${\sum }_{i=1}^m{p}_i^2$-${\sum }_{\mathrm{i}=1}^{\mathrm{m}-1}{\sum }_{i=1}^{m}2p_i^2{p}_j^2$, where the p_i indicates the frequency of i allele, p_j indicates the frequency of the j allele, and m indicates the number of alleles

Demographic pattern and genetic diversity

In total, 64 MLGs were detected among the Pst population from Yunnan and Guizhou provinces and 34 of which were unique (Table S4). Minimum spanning network (MSN) analysis showed that MLG 25 was most abundant and was detected in 22 isolates from 2 counties of Yunnan (Lufeng and Fuyuan) and 6 counties of Guizhou (Bijie, Hezhang, Weining, Guiyang, Panzhou, Qinglong, and Xingren) (Fig. 3). Meanwhile, MLG 25 had the maximum number of connections to other genotypes. MLG 59, the second most abundant MLG, was detected among 8 isolates from 2 counties of Yunnan (Fuyuan and Shizong) and 3 counties of Guizhou (Guiyang, Liuzhi, and Xingyi). The most genotypes were closely clustered by a genetic distance of 0.042.

Fig. 3.

Minimum spanning network (MSN) based on Bruvo’s genetic distance for Puccinia striiformis f. sp. tritici population from Yunnan and Guizhou provinces in 2018 with 12 microsatellite marker loci. Nodes (circles) represent individual multilocus genotypes containing the number of associated isolates and sized in proportion to isolates frequency. Nodes that were more closely related were indicated by darker and thicker edges, whereas those that were more distantly related were indicated by lighter and thinner edges or no edge

Genetic diversity of the Pst population of 113 Pst isolates was analyzed using the 12 SSR marker loci (Table 6). Overall, a high genetic diversity with Nei’s gene diversity (expected heterozygosity, He) value of 0.358 was detected in the Pst population from Yunnan and Guizhou provinces in 2018. Among 13 county subpopulations, Guiyang subpopulation showed the highest He with a value of 0.512. In contrast, Weining subpopulation represented the lowest He value (0.21). The He value of subpopulations from Guiyang, Qinglong and Xingyi of Guizhou, and Shizong of Yunnan were greater than 0.4, whereas those of Weining and Hezhang subpopulations of Guizhou was less than 0.3. Similarity to virulence diversity, the genetic diversity of subpopulations of northwestern Guizhou, including Bijie, Hezhang and Weining, was lower than those of other counties in Guizhou provinces due to He values ranging from 0.210 to 0.318 based on 12 SSR marker loci.

Table 6.

Mean of each of 13 Puccinia striiformis f. sp. tritici subpopulations from 13 counties in Guizhou and Yunnan provinces at 12 simple sequence repeat (SSR) marker loci

Treatment	No. of isolates	No. of MLGsa	Parametersb
			Na	Ne	I	Ho	He	UHe	F
Counties	13	64	2.622(0.112)	1.924(0.082)	0.630(0.039)	0.414 (0.030)	0.358(0.021)	0.384(0.022)	–0.110(0.039)
Lufeng	6	5	2.667(0.449)	1.954(0.354)	0.625(0.163)	0.375(0.122)	0.339(0.084)	0.370(0.091)	0.032(0.167)
Fuyuan	10	5	2.167(0.322)	1.663(0.189)	0.509(0.127)	0.408(0.131)	0.309(0.074)	0.325(0.078)	–0.248(0.191)
Shizong	8	8	2.917(0.434)	2.232(0.331)	0.785(0.145)	0.49(0.099)	0.449(0.072)	0.479(0.077)	–0.114(0.15)
Qixingguan	5	5	2.167(0.271)	1.709(0.208)	0.535(0.127)	0.333(0.102)	0.318(0.076)	0.354(0.084)	0.008(0.152)
Nayong	7	5	3.000(0.492)	1.956(0.327)	0.680(0.156)	0.369(0.1)	0.361(0.078)	0.388(0.084)	0.046(0.111)
Hezhang	12	9	2.417(0.434)	1.527(0.179)	0.445(0.122)	0.382(0.124)	0.254(0.072)	0.265(0.076)	–0.346(0.112)
Weining	8	6	1.750(0.305)	1.433(0.159)	0.331(0.119)	0.375(0.139)	0.210(0.076)	0.224(0.081)	–0.763(0.094)
Guiyang	15	12	3.667(0.466)	2.559(0.397)	0.954(0.141)	0.478(0.099)	0.512(0.062)	0.529(0.064)	0.057(0.138)
Liuzhi	6	6	2.583(0.417)	1.944(0.365)	0.595(0.163)	0.333(0.105)	0.322(0.085)	0.351(0.093)	–0.024(0.094)
Panzhou	7	6	2.417(0.313)	1.932(0.257)	0.630(0.135)	0.560(0.12)	0.377(0.076)	0.406(0.082)	–0.412(0.068)
Qinglong	18	13	3.417(0.499)	2.254(0.38)	0.813(0.148)	0.375(0.093)	0.438(0.070)	0.451(0.072)	0.131(0.12)
Xingren	5	4	2.167(0.207)	1.645(0.183)	0.517(0.104)	0.433(0.11)	0.315(0.066)	0.350(0.073)	–0.279(0.104)
Xingyi	6	6	2.750(0.329)	2.209(0.323)	0.780(0.127)	0.472(0.106)	0.455(0.064)	0.496(0.07)	0.027(0.151)
Overall	113	64	4.417(0.679)	2.110(0.308)	0.825(0.145)	0.415(0.102)	0.424(0.067)	0.426(0.067)	0.083(0.123)

^a MLGs = Multi locus genotypes

^b Na = Number of alleles per locus. Ne = Number of effective alleles and is calculated by 1/$\sum p_i^2$. I = Shannon’s Information Index and is calculated by -$\sum p_i$($\ln p_i$). Ho = Observed heterozygosity and is calculated by No. of heterozygotes/N. He = Expected heterozygosity is calculated by 1-$\sum p_{i.}^2$ UHe = Unbiased expected heterozygosity is calculated by 2 N(1-$\sum p_i^2$)/(2 N-1). F = Fixation index is calculated by (He-Ho)/He = 1-(Ho/He)

Genetic differentiation and variation

The overall Pst population was subdivided into two distinguished geographic subpopulations, viz. Yunnan population and Guizhou population. The former included Pst subpopulation of Lufeng, Fuyuan, and Shizong, and the latter included Pst subpopulation of Bijie, Nayong, Hezhang, Weining, Guiyang, Liuzhi, Panzhou, Qinglong, Xingren and Xingyi.

Based on Fst values generated from SSR data, analysis of molecular variance (AMOVA) showed that there were 92% of genetic variation within the Pst subpopulation and 8% among the Pst subpopulations. Whereas, there was no variation between Guizhou and Yunnan populations. According to Fst values shifted from virulence data on 19 Chinese differential genotypes, AMOVA revealed that 85% of total variation occurred within subpopulation, and 15% of that were detected among subpopulations. However, the variation between Yunnan and Guizhou provincial populations was null. Similarly, virulence variation of the whole subpopulations to the 17 single Yr gene lines and 7 additional genotype lines represented that the variation within subpopulations reached 84% of the total variation, and that among subpopulations contributed to 16%. The variation between two subpopulations from Yunnan and Guizhou provinces was zero.

All results indicated that variation mainly occurred within the Pst subpopulation rather than ones among subpopulation and regions in Yunnan and Guizhou provinces.

Estimation of genetic clusters

Structure analysis supported that the optimal number of clusters was K = 2 (Fig. 4a). Most of the Pst isolates sampled from different counties were assigned to two main genetic clusters (indicated by yellow or green) that was unrelated to geographical origin. Thus, the existence of Pst migration between counties of Yunnan and Guizhou provinces was confirmed (Fig. 4b).

Fig. 4.

STRUCTURE software results showing the assignment of Pst isolates to the two inferred genotypic groups on the basis of SSR polymorphism. Each color represents a different genotypic group

Relationship between Pst virulence patterns and genotypes

The relationship between SSR data and virulence patterns was compared by the correlation genetic distance based on shared genotype and virulence simple mismatch distance based on binary data in each pair of the individuals of 113 isolate. As the SSR distance matrix was compared with that generated by the 19 Chinese differential genotypes, the relation coefficient (R²) was 0.212. The SSR distance matrix was compared with that generated by the combined 24 wheat genotypes, and the relation coefficient (R²) was 0.291. The relation coefficient between the 19 Chinese differential genotypes compared with the 24 combined wheat genotypes was 0.125. Meanwhile, the SSR distance matrix was compared with that generated by the combination of 24 extra wheat genotypes and 19 Chinese differentials to show that the relation coefficient (R²) was 0.37. In these cases, the results indicated that there was a weak correlation between either virulence patterns and SSR marker loci or between virulence patterns based on the Chinese differential genotypes and virulence based on the additional 24 wheat genotypes.

Discussion

Race surveillance benefits our understanding of race dynamics of Pst populations and guides resistant wheat breeding. Monitoring the dynamics of Pst populations in China has lasted for more than 70 years. Generally, in China a new virulent race developed with an increasing occurrence frequency to be prevalent race and then designated as CYR (Chinese Yellow Rust) race. A race with virulence to key differential genotypes, such as Lovrin 10, Lovrin 13, Suwon11, Hybrid 46, Guinong 22, were regarded as pathotypes. Although race identification and geographic distribution of the Pst populations in Yunnan and Guizhou have been reported in the recent years (Chen et al. 2016; Li et al. 2009, 2018), the information regarding the change of Pst populations in this area has been lacking and should be strengthened.

In this study, 25 known races and 38 unknown virulence patterns were detected, and race CYR 32 was predominant in this area in 2018. The frequencies of nearly all races had relatively low frequencies of less than 5% in addition to race CYR32. CYR 32 was first found in 1994 and named as H46-3 until the new designation in 2002 (Wan et al. 2002). This race was predominant for the first time in 2001 and has been prevalent until 2014 in the Yunnan-Guizhou region (Wu et al. 2003). Although CYR33 has developed to be the predominant race from 2008 to 2011 across China, the frequency of CYR 33 was second to CYR32 from 2008 to now in the Yunnan-Guizhou region (Li et al. 2009; Zuo et al. 2011; Chen et al. 2016). Some previously-emerged physiological races (CYR17, CYR18, CYR20, CYR21, CYR24, CYR26, CYR27) and Lovrin pathogen groups with virulence to differential genotypes Lovrin 10 / 13 were still detected in the 2000s in this region (Wu et al. 2005; Li et al. 2009; Zuo et al. 2011; Chen et al. 2016), but not identified in this study. In addition, the new isolate virulent to Yr26 (= Yr24), G22-9, was first detected on wheat Chuanmai 42 (Yr26) in Sichuan in 2009 (Liu et al. 2010), subsequently spread to main wheat production regions, and developed rapidly to be a predominant race with occurrence frequencies ranging from 10.6% to 34.2% from 2015 to 2017 in China (Liu et al. 2017). Virulence on differential genotype Guinong 22 carrying Yr26 has not been reported in Yunnan and Guizhou before 2008 (Li et al. 2009), and races of Guinong 22 lineage (G22G) had an increasing virulence frequency ranging from 11.43% to 18.0% in Yunnan or Guizhou from 2014 to 2017 (Chen et al. 2016; Li et al. 2018; Jiang et al. 2018). In this study, race CYR34 was detected with a low frequency (2.7%). Some isolates with new virulence patterns distinguished from known races, and uncategorized races were identified, which hinted that the Pst population in the Yunnan-Guizhou region is continuously developing. In this study, race composition was different from those reported previously and many races had a frequency lower than found before in the Yunnan and Guizhou region. In comparison with other regions in the same year, the frequency of G22G reached 50.34% in Gansu in 2018. The first predominant race was CYR34 with a frequency of 38.51%, and CYR32, with a frequency of 15.20%, was second to CYR34 in this province (Jia et al. 2021). However, 9 races, including CYR32, CYR33, CYR34, HY46-4, HY46-8, Su11-41, Su11-192, G22-14, and G22-74, were found in both Yunnan-Guizhou and Gansu in 2018. This result indicated that Pst races in the Yunnan and Guizhou could be not in accordance with those in other wheat production regions.

Based on the virulence of isolates on near-isogenic lines (NILs) and additional differential genotypes set, we found that less than 30% of isolates had virulence for genes YrTr1, Yr26, Yr10, Yr32, Yr8. Yr5 and Yr15 were effective to all isolates tested in this study. To date, Yr5 and Yr15 have been used in the breeding programs in recent years in China (Zheng et al. 2017). While isolates virulent to Yr5 have been reported recently in China (Zhang et al. 2020). It is important to monitor races virulent to wheat cultivars with stripe rust-resistance in China. Races virulent to resistance genes YrTr1, Yr26 (= Yr24), Yr10, Yr32, Yr8 represented quite low virulence frequencies ranging from 0.74% to 11.76% in Yunnan in 2016, which was consistent with the present study (Li et al. 2018). Previously, there was no isolate with virulence to Yr10 and Yr32, and virulence frequencies of less than 12% to Yr8 and Yr17 in Yunnan in 2016 (Li et al. 2018). However, virulence frequencies of more than 20% to both Yr10 and Yr32, and those of more than 28.3% virulent to Yr8 have been detected in this study. Especially, a high virulence frequency of more than 80% to Yr17 was observed. Virulence dynamics showed that population structure of both provinces has changed.

In this study, AvS/Yr17NIL was susceptible to most of Pst races. However, a recent study reported that the distribution of Yr17 varied with different wheat groups, and AvS/Yr17NIL and ‘‘Chuanyu’’ breeding lines showed nonsignificant resistance to Pst instead of Chinese modern cultivars and introduced foreign germplasms (Zheng et al. 2017). Some Yr genes exhibited ineffective resistance or partial resistance to the current Pst races, and additive effects or epistatic effects occur between the resistance genes as multi-gene pyramided (Zheng et al. 2017). Thus, utilization of Yr 17 in wheat breeding is essential for re-evaluating in Yunnan and Guizhou provinces. In the future, pyramiding some effective genes including Yr5, Yr15, or other partial seedling genes, combining with adult plant resistance genes, or developing multi-gene lines are suggested to use for wheat breeding in the Yunnan and Guizhou region.

New races are usually detected in Pst populations from Yunnan and Guizhou in race surveillances. In this study, the 113 isolates collected from Yunnan and Guizhou provinces were identified as 66 races, including 25 known races and 38 new races, as well as unique 50 VPs. Detection of MLGs indicated that each can be detected from 1.77 isolates, representing a high genotypic polymorphism. The high Kosman diversity index K and expected heterozygosity He indicated that the Yunnan-Guizhou population had a high level of virulence and genetic diversity. Generally, in China virulence and genetic diversity of populations in the oversummering regions are relatively high. However, although Pst can oversummer in some counties of Yunnan and Guizhou provinces, northwestern of Guizhou (Hezhang, Weining and Bijie) had a relative lower virulence and genetic diversity and indicated that the potential variation could be low.

Mantel test based on genetic distance with 12 SSR marker loci, and virulence distance indicated that the correlation coefficient between the virulence and the SSR marker data was low (R² = 0.212 to 0.370), similar to those by Chen (1993), Zhan et al. (2015), and Zhan et al. (2016). In those studies, virulence and molecular marker data were less significantly correlated or had low correlations. Analysis of virulence association found that strong positive virulence associations were observed among Yr6, Yr8, Yr10, Yr26, Yr32 and Trigo Eruraka. The gene for gene relationship implies that the pathogenic genotype of a pathogen isolate can be determined through studying the avirulence / virulence on hosts harboring different resistance genes. Experimental studies are needed to determine whether genetic linkage of avirulence loci of Yr6, Yr8, Yr10, Yr26, and Yr32.

Liu et al. (2010) reported that the population of 150 Pst isolates collected from 9 counties of Yunnan province in 2008 was a clonal population. Currently, there was no evidence to support existence of sexual reproduction of Pst on susceptible Berberis species in this region. In this study, new races accounted for 33.63% in the Yunnan-Guizhou region and the gene diversity of Yunnan-Guizhou population was high. In this region, a few endemic Berberis species were identified to be highly susceptible for Pst (Zhao et al. 2013; Li et al. 2021). Under natural conditions rust infections on susceptible Berberis plants have been observed. Further studied are needed to confirm whether sexual reproduction of Pst poses a potential role in virulence and genetic diversity of Pst population in Yunnan and Guizhou for understanding race dynamics and genetic diversity and for making strategies to effectively manage wheat stripe rust.

Conclusions

In Yunnan and Guizhou regions, 25 races of Pst were identified using Chinese differential genotypes and CYR32 was predominant race. 66 virulence patterns (VPs), VP1 to VP66, were detected using the set of 17 Yr single gene lines and 7 additional genotypes. The VP1 with the highest frequency was virulent to resistance genes Yr1, Yr6, Yr7, Yr9, Yr17, Yr27, Yr43, Yr44, YrSP, YrExp2, YrTye (Yr76), Yr2, Yr25, Yr28, Yr29, Vilmorin 23 (Yr3a, Yr4a, YrV23), and YrA (Yr74, Yr75), but avirulent to Yr5, Yr8, Yr10, Yr15, Yr32, YrTr1, and Yr26. Resistance gene or genotypes Yr8, Yr10, Yr26, Yr32, YrTr1, Guinong 22 and Zhong 4 were effective to Pst races in this region and neither of isolates was virulent to Yr5 and Yr15. High virulence and genetic diversity and migration of Pst between the counties in both provinces were detected. This study will be benefit for understanding composition of Pst races, relationships of Pst, effectiveness of resistance genes to implement disease management strategies and wheat breeding programs in this region.

Supplementary Information

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Funding

This work was supported by [the National Key R&D Program of China] (2021YFD1401000), [National Natural Science Foundation of China] (31960524, 32072358, 32272507), [Open Fund for Key laboratory of Integrated Pest Management of Crops in Southwest China, Ministry of Agriculture] (2020-XN2d-01), [Guizhou Academy of Agricultural Sciences Post-subsidy Special Funds for National Natural Science Foundation of China] (2021–43), [the Natural Science Basic Research Plan in Shaanxi Province of China] (2020JZ-15), and [State Key Laboratory of Crop Stress Biology for Arid Areas, NWAFU (CSBAA202211).

Data availability

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Declarations

Conflict of interest

All authors declare that they have no conflict of interest.