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Genetic diversity and population structure of Puccinia striiformis f. sp. tritici reveal its migration from central to eastern China
Journal Pre-proof Genetic diversity and population structure of Puccinia striiformis f. sp. tritici reveal its migration from central to eastern China Cuicui Wang, Leifu Li, Bingbing Jiang, Keyu Zhang, Bingyao Chu, Yong Luo, Zhanhong Ma PII:
S0261-2194(19)30320-5
DOI:
https://doi.org/10.1016/j.cropro.2019.104974
Reference:
JCRP 104974
To appear in:
Crop Protection
Received Date: 18 January 2019 Revised Date:
3 September 2019
Accepted Date: 1 October 2019
Please cite this article as: Wang, C., Li, L., Jiang, B., Zhang, K., Chu, B., Luo, Y., Ma, Z., Genetic diversity and population structure of Puccinia striiformis f. sp. tritici reveal its migration from central to eastern China, Crop Protection (2019), doi: https://doi.org/10.1016/j.cropro.2019.104974. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
1
Genetic diversity and population structure of Puccinia striiformis f.
2
sp. tritici reveal its migration from central to eastern China
3
Cuicui Wang, Leifu Li, Bingbing Jiang, Keyu Zhang, Bingyao Chu, Yong Luo* and Zhanhong
4
Ma**
5
Department of Plant Pathology, China Agricultural University, Beijing, 100193, China
6
*Corresponding author.
7
**Corresponding author.
8
Email addresses: [email protected], (Y. Luo), [email protected] (Z. Ma).
9 10 11
ABSTRACT This study focused on Puccinia striiformis f. sp. tritici (Pst), the causal pathogen
12
of wheat stripe rust in China. The central and eastern China are the main
13
wheat-producing areas in the country, and the occurrence of wheat stripe rust can
14
cause severe yield losses. To determine the population genetic structure and potential
15
pathways of migration among regional populations of the pathogen, 264 isolates were
16
sampled during spring 2017 and genotyped using 12 simple sequence repeat (SSR)
17
loci. The highest genetic diversity was detected in Hubei Province, indicating its
18
potential role as one of the source populations for the rust epidemics. A high gene
19
flow was detected among regional populations, supporting migration of Pst among
20
regions. The significant decrease in genotypic and genetic diversity from Hubei to
21
Shandong suggested that the migration direction was from central to eastern China.
22
Analysis of molecular variance (AMOVA), Bayesian assignment, and principle
23
coordinate analysis all indicated two major groups, one was mainly distributed in 1
24
Hubei and the another was mainly in the eastern region. Shared genotypes, gene flow
25
among regions, and Bayesian assignment tests revealed that the migration of Pst from
26
central to eastern of China during spring occurred mainly through stepwise
27
movement.
28
Keywords: Genotyping, SSR, Wheat tripe rust, Migration, Genetic structure
29 30 31
1. Introduction Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a
32
destructive disease in the world (Line, 2002; Wan et al., 2007). China is one of the
33
largest wheat-producing and -consuming countries in the world, where wheat stripe
34
rust occasionally caused yield losses up to millions of metric tons (Li and Zeng, 2002;
35
Wan et al., 2004b). This disease in China demonstrated unique features of
36
inter-regional epidemics, that is, disease spreading through long-distance dispersal of
37
the pathogen across large geographical regions (Zeng and Luo, 2006). Recently,
38
outbreaks occurred in the growing season of 2017 in 18 provinces, and the occurrence
39
area reached to 555.66 hm2 (Huang et al., 2018).
40
Although the sexual stage of Pst was found in Gansu, Shaanxi, and Tibet in
41
China, aeciospores produced on the alternative host Berberis spp. could infect wheat
42
only in nearby fields (Zhao et al., 2013). Therefore, with aeciospores dispersal limited
43
to short distance, large-scale epidemic of wheat stripe rust is mainly caused by
44
asexual reproduction via urediniospores through long-distance migration. Many
45
studies on epidemics of wheat stripe rust from field to regional levels had been carried
46
out in the past few decades (Xie et al., 1986; 1988; 1993; Zeng and Luo, 2006). 2
47
Wheat cropping regions in China were classified based on the features of interregional
48
stripe rust epidemics, including the regions where pathogen can over-summer
49
(urediniospores survival during summer) or over-winter (urediniospores survival
50
during winter), and the spring disease epidemic regions (Fig. 1A) (Zeng and Luo,
51
2006). The stripe rust pathogen can over-summer on wheat and volunteer plants at
52
high altitudes of mountainous areas. After summer, the urediniospores of Pst could
53
disperse to plants at low altitudes in the same region and serve as an inoculum source
54
for both neighboring areas and other distant regions, especially in the pathogen’s
55
over-wintering regions under the northwest wind (Xie et al., 1993). Urediniospores
56
can propagate continuously on wheat due to warmer winter, or survive in mycelial
57
form in wheat leaves when meet low temperature (average value < 1°C in January)
58
(Li and Zeng, 2002; Pan et al., 2016). Rust disease often burst on susceptible wheat in
59
the spring epidemic regions when met the appropriate climatic conditions, large
60
amount of urediniospores being dispersed from the over-wintering regions (Chen et
61
al., 2013).
62
According to previous observations, disease in pathogen over-wintering region is
63
highly associated with that in the eastern region (Fig. 1A) (Chen et al., 2013). In
64
addition to Sichuan Basin, northwestern Hubei is another main over-wintering region,
65
which serves as a “bridge” for the migration of the pathogen from over-summering
66
regions (e.g., Gansu and Shaanxi) to spring epidemic regions (e.g., Henan, Shandong,
67
and Hebei) with a geographical point of view (Fig. 1A) (Li and Zeng, 2002). Wheat is
68
planted from late-September to mid-October, and stripe rust occurs usually in
69
late-February in the northwest area of Hubei. Because of the high temperature in
70
winter, wheat can grow continuously, and urediniospores can over-winter and the 3
71
inoculum can be accumulated. Thus, other regions may receive the source of Pst from
72
the northwestern Hubei in the early spring (Li and Zeng, 2002).
73
Eastern spring epidemic regions, including Henan, Shandong, and Hebei
74
Provinces, are the main wheat-producing regions in China covering huge plain areas.
75
Pst could not over-summer in these areas owing to hot summer, and epidemics in
76
spring were occasional. Thus, the epidemics of wheat stripe rust in the central and
77
eastern regions mainly depend on Pst sources from other areas (Li and Zeng, 2002).
78
Drought is another factor affecting occurrence of epidemics in spring, which restricts
79
infection and accumulation of urediniospores. However, the disease’s prevalence rate
80
would be extremely high once favorable environmental conditions such as
81
temperature ranging from 7 to 12°C and rainfall in early spring occur (Chen et al.,
82
2014; Li and Zeng, 2002). Initial inoculum is a prerequisite for disease epidemics in
83
central and eastern China, although it is not known whether urediniospores of Pst
84
could be dispersed directly from other regions towards eastern China. Furthermore,
85
Hubei was hypothesized as an over-wintering region to provide inoculum for Pst for
86
eastern China. Whether the migration of Pst from Hubei to eastern China can be
87
through one-step long-distance dispersal or through stepwise dispersal is yet to be
88
identified. This could contribute to the understanding of the development of disease
89
epidemics at a spatial scale. Furthermore, the population genetic structure of the
90
pathogen in the central and eastern regions was rarely reported which will be useful to
91
understand the interregional gene flow.
92
Studies on migration pathway or direction of pathogen dispersal would
93
contribute to the estimation of disease epidemic risk, formulating comprehensive
94
prevention and control strategy of the rust disease among regions (Chen et al., 2013). 4
95
To achieve this goal, phylogeographical and population structure analyses could
96
greatly improve our ability to infer the dispersal routes of pathogens (Archie et al.,
97
2009; Xhaard et al., 2012). Inferring historical migration by using Bayesian clustering
98
methods, nonparametric clustering methods, and approximate Bayesian computation,
99
indicated that Himalayan and neighboring regions were the putative center of origin
100
of Pst in the world, and Europe was confirmed as the source population contributing
101
migrants to the South American, North American, and Australian populations (Ali et
102
al. 2014a). The Mediterranean-Central Asian populations were the origin of South
103
African populations (Ali et al., 2014a). Phylogeographical and population structure
104
analyses provide accurate information for inferring migration at a small (provincial)
105
scale during limited time. Asymmetric migration was confirmed from Gansu Province
106
to Sichuan Basin in China in the 2009 and 2010 fall seasons (Liang et al., 2015), and
107
an inverse direction of migration existed in fall and spring between these two regions
108
(Liang et al., 2013).
109
Analyzing disease epidemic patterns will help us understand the geographic
110
movement of pathogen populations. The objectives of this study were to i) understand
111
the pathogen population in genetic structure, ii) determine the migration direction, and
112
iii) infer the major migration pathways of Pst from central to eastern China.
113 114
2. Materials and Methods
115
2.1 Sampling, isolation and reproduction of Pst isolates
116 117
In the present study, populations of Pst were sampled from different geographic locations in Hubei, Henan, Shandong, and Hebei Provinces during spring 2017, a 5
118
severe epidemic year (Fig. 1B). From April and June of 2017, diseased leaves in the
119
fields showing sporulation of Pst were collected in 3 counties of Hubei Province, 2
120
counties in Henan Province, 7 counties in Shandong Province, and 2 counties in Hebei
121
Province (Fig. 1, Table 1). Five to ten sampling sites with distances of at least 50 m
122
apart from each other were identified, and approximately 10-20 diseased leaves were
123
collected in each field. Leaf samples were packed using dried absorbent paper and
124
stored at 4 . Isolates collected from the same county were considered as one
125
subpopulation, and fourteen subpopulations containing a total of 264 isolates were
126
collected. These subpopulations were assigned as follows: FC (Fancheng),
127
YY(Yunyang), YX (Yunxi), XP (Xiping), LY (Linying), DY (Daiyue), ZC (Zhoucun),
128
AQ (Anqiu), XJ (Xiajin), DOM (Dongming), CW (Chengwu), LC (Laicheng), BY
129
(Boye), and DAM (Daming) (Fig. 1, Table 1).
130
A single pustule was collected from each leaf sampled from Hubei to obtain pure
131
isolates. Purification was carried out according to Liang et al. (2013). The spores were
132
harvested, transferred into a 0.5 mL Eppendorf tube, dried in desiccators at 4
133
to 4 days, and stored at -20°C for DNA extraction. The infected leaves in the fields in
134
Henan, Shandong and Hebei bore a single lesion, which was considered presumably
135
resulted from a single spore infection (Ali et al., 2011). Therefore, the procedure of
136
propagation was removed and the DNA was exacted using the mixture of leaf and
137
urediniospores (Ali et al. 2011) sampled in Henan, Shandong and Hebei.
6
for 3
138 139
Figure 1. Geographic locations where the 264 isolates of Puccinia striiformis f. sp. tritici were collected from
140
central to eastern China in spring 2017.
141
Footnote: (A) Three main epidemic regions (pathogen over-summering, over-wintering, and spring epidemic
142
regions) of rust disease are divided according to the altitude, climate, and the wheat-producing features etc. The
143
provincial (or city’s) names are shown in abbreviated form: QH = Qinghai, GS = Gansu, NX = Ningxia, SC =
144
Sichuan, YN = Yunnan, GZ = Guizhou, SX = Shaanxi, HB = Hubei, ShX = Shanxi, HN = Henan, AH = Anhui, JS
145
= Jiangsu, HeB = Hebei, SD = Shandong, BJ = Beijing, TJ = Tianjin. (B) The distribution of sampling sites in the
146
present study.
147
Subpopulations named with abbreviation of counties’ names. FC = Fancheng, YY = Yunyang, YX = Yunxi, XP =
148
Xiping, LY = Linying, DAM = Daming, DOM = Dongming, CW = Chengwu, XJ = Xiajin, DY = Daiyue, LC =
149
Laicheng, ZC = Zhoucun, AQ = Anqiu, BY = Boye.
150 151 152
Table 1
153
Information on Puccinia striiformis f. sp. tritici isolate collections from Hubei, Henan, Shandong, and Hebei
154
provinces in spring 2017.
Province
County
Code
No. of isolates
Cultivars
Elevation (m)
Sampling time in 2017
Hubei
Fancheng
FC
24
Unknown
Unknown
April, 20
Yunyang
YY
44
Echun series
205 - 680
April, 29
7
Henan
Shandong
Hebei
Total
Yunxi
YX
28
Mianyang 31
Unknown
May, 1
Xiping
XP
15
Huaichuan916, 9817, Aikang58, Xinong207, Xinong979
Unknown
May, 15
Linying
LY
20
Unknown
Unknown
May, 14
Daiyue
DY
14
Unknown
Unknown
June, 1
Zhoucun
ZC
16
Unknown
Unknown
May, 15
Anqiu
AQ
11
Jimai22
Unknown
May, 11
Xiajin
XJ
10
Shannong21
Unknown
May, 17
Dongming
DOM
27
Unknown
Unknown
May, 16
Chengwu
CW
13
Monong99, Jimai22
Unknown
May, 16
Laicheng
LC
11
Jimai22
Unknown
May, 22
Boye
BY
11
Unknown
Unknown
May, 23
Daming
DAM
20
Nongda399, Shimai18
Unknown
May, 18
264
155 156 157
2.2 DNA extraction and SSR amplification CTAB method was used to extract the whole genomic DNA of Pst (Wan et al.,
158
2015) for all 264 samples. Among them, 96 DNA samples in Hubei were extracted
159
from urediniospores, and the left 168 DNA samples in Henan, Shandong and Hebei
160
were extracted from the infected leaf tissues (approximately 1-2 cm) (Ali et al. 2011;
161
Wan et al., 2015). A NanoDrop 2000 spectrophotometer (Gene Company Limited,
162
China) was used to determine the DNA concentrations and qualities with UV
163
absorption at wavelengths 260 and 280 nm. The DNA samples were then diluted to
164
the same concentration of 20-80 ng/µL for SSR amplification.
165
Twelve published SSR primer pairs (Bahri et al., 2009; Chen et al., 2009;
166
Enjalbert et al., 2002; Zhan et al., 2015) were used to genotype each isolate (Table 2),
167
and these SSR primers were labeled with FAM, ROX, TAMRA, or HEX fluorescence 8
168
at the 5’ end (Table 2). All PCRs were carried out according to corresponding
169
references (Bahri et al., 2009; Chen et al., 2009; Enjalbert et al., 2002; Zhan et al.,
170
2015). All SSR amplifications were performed in a thermal cycler (Eppendorf AG).
171
Subsequent separation of the PCR products was done using an ABI 3730 DNA
172
Analyzer (Applied Biosystems, Carlsbad, CA, USA) by Beijing Tsingke Biotech Co.,
173
Ltd.). A DNA maker GS500 (35-500 bp) was used as the internal standard.
9
174 175
Table 2
176
Information of the SSR primers for Puccinia striiformis f. sp. tritici used in this study. Locus
Repeat motif
CPS8
(CAG)14
CPS13
(GAC)6
CPS27
(TTC)4
CPS34
(TC)9
RJO3
(TGG)8
RJO20
(CAG)4
RJ3N
(CT)9
RJ5N
(CT)8
RJ6N
(AAC)9
RJ8N
(GAT)8
RJ13N
(ACG)6
WSR44
(GT)6
Primer sequence (5’-3’) F: FAM-GATAAGAAACAAGGGACAGC R: CAGTGAACCCAATTACTCAG F: FAM-TCCAGGCAGTAAATCAGACGC R: ATCAGCAGGTGTAGCCCCATC F: TAMRA-GATGGGGAAAAGTAAGAAGT R: GGTGGGGGATGTAAGTATGTA F: TAMRA-GTTGGCTACGAGTGGTCATC R: TAACACTACAAAAGGGGTC F: FAM- GCAGCACTGGCAGGTGG R: GATGAATCAGGATGGCTCC F: HEX-AGAAGATCGACGCACCCG R: CCTCCGATTGGCTTAGGC F: ROX-TGGTGGTGCTCCTCTAGTC R: AGGGGTCTTGTAAGATGCTC F: ROX-AACGGTCAACAGCACTCAC R: AGTTGGTCGCGTTTTGCTC F: TAMRA-CAATCTGGCGGACAGCAAC R: CACCTAGGATACCACCGCC F: FAM-ACTGGGCAGACTGGTCAAC R: TCGTTTCCCTCCAGATGGC F: HEX-TTAGCTCAGCCGGTTCCTC R: CAGGTGTAGCCCCATCTCC F: HEX-AGGCCCCAGGAACACAAAAA R: TCACACACGCTCCACAGTAC
10
Ta (℃ ℃)
No. of allele (size)
55
5 (200–212)
58
2 (125–128)
57
2 (225–228)
55
5 (104-114)
52
4 (201-212)
52
3 (283-289)
52
4 (335-343)
52
3 (223-229)
52
4 (309-318)
52
6 (301-330)
52
2 (149-152)
56
2 (188-190)
Reference Chen et al. 2009 Chen et al. 2009 Chen et al. 2009 Chen et al. 2009 Enjalbert et al. 2002 Enjalbert et al. 2002 Bahri et al. 2009 Bahri et al. 2009 Bahri et al. 2009 Bahri et al. 2009 Bahri et al. 2009 Zhan et al. 2015
177 178
2.3 Data analysis
179
Genetic diversity
180
In the process of analyzing shared genotypes among subpopulations of different
181
locations, the following 5 sub-regions (P1-P5, Fig. 2A) were defined according to
182
geographic distribution from the southwest to northeast in the current study: P1
183
contained YY, YX, and FC in Hubei Province; P2 contained LY and XY in Henan
184
Province; P3 contained DOM and CW in Shandong Province and DAM in Hebei
185
Province; P4 contained XJ, DY, and LC in Shandong Province; and P5 contained BY
186
in Hebei Province and ZC and AQ in Shandong Province. The numbers of SSR
187
genotypes in overall population and in each subpopulation were calculated using the
188
GenClone 2.0 program (Arnaud-Haond and Belkhir, 2007). The number of common
189
genotypes was calculated according to the result of overall genotype data using the
190
GenClone 2.0 program. Subsequent calculations were performed with the
191
clone-corrected data to minimize the effects of clonal reproduction especially in the
192
spring epidemic season, which might bias the population genetic analyses. The
193
subsequent analysis was carried out for 14 single subpopulations, 5 sub-regional
194
subpopulations and the overall population, respectively. Gene diversity was calculated
195
with POPGENE version 1.3.1 (Yeh et al., 1999). Mean value of effective number of
196
alleles (Ne) and Shannon’s information index (I) of each locus were calculated (Nei,
197
1972), and the standard error among 12 loci divided by the square root of 12 was
198
determined. Gene flow (Nm) between any two studied populations was calculated
199
using POPGENE version 1.3.1 estimated as Nm = 0.25(1 - Fst)/Fst (Nei, 1972). When
200
1 < Nm < 4, it implied a considerable amount of gene flow between populations, and 11
201
when Nm > 4, it indicated a high level of gene flow (Slatkin and Barton, 1989).
202
Population genetic structure
203
Pair-wise genetic differentiation (Fst) among the 14 subpopulations was
204
calculated using the AMOVA program in GENALEX version 6.5 (Peakall and
205
Smouse, 2006; 2012). A principal coordinate plot based on Nei’s distances between all
206
pairs of SSR genotypes was generated with GENALEX 6.5 and was used to generate
207
a two-dimensional PCoA plot showing the distribution of genotypes among the
208
studied populations.
12
209 210
Figure 2. Shared genotypes among subpopulations. (A) Five subregions (P1-P5) were classified according to
211
geographic features. (B) Shared genotypes and frequencies among subregion populations were calculated using
212
GenClone software.
213
A model-based Bayesian method implemented in STRUCTURE 2.3 (Pritchard et
214
al., 2000) was used to identify genetic clusters and to evaluate the extent of admixture
215
among them. A model allowing admixture and independent allele frequencies among
216
populations was used. All the Pst isolates were assigned into K clusters ranging from
217
1 to 10 based on their SSR multi-locus genotypes with clone-corrected data. For each 13
218
simulated cluster K, 15 independent runs were performed with 40,000 iterations in
219
Monte Carlo Markov Chain replications and a burn-in period of 10,000. The best K
220
value was estimated based on ∆K (Evanno et al., 2005) through uploading the results
221
of STRUCTURE to STRUCTURE HARVESTER
222
(http://taylor0.biology.ucla.edu/structureHarvester). Indi files and pop files of 15 runs
223
corresponding to the best K were processed with CLUMPP 1.1.2 (Jakobsson et al.,
224
2007), and then, a bar plot was generated in Distruct 1.1 (Rosenberg, 2004) using the
225
output of CLUMPP 1.1.2 (Jakobsson et al., 2007). Results of the bar plot as pie charts
226
were generated for each location based on the membership fraction (Q) Q > 0.8
227
(Pritchard et al., 2000).
228
Another Bayesian clustering method implemented in R package GENELAND
229
version 4.0.4 (Guillot et al., 2005) was used to detect the geographic discontinuities
230
for microsatellite loci. Ten independent runs were performed with 1,000,000 MCMC
231
iterations, and it saved once for every 1000 interactions in GENELAND. The tested
232
number of genetic clusters (K) was set from 1 to 10. The largest posterior probability
233
of population membership among 10 runs was computed using a burn-in period length
234
of 200 iterations.
235 236
3. Results
237
3.1 Genetic diversity
238
Totally, 89 genotypes were obtained from 264 isolates using the 12 primer pairs,
239
with the overall genotypic diversity as 0.3371 (Table 3). The genotypic diversities
240
among 14 subpopulations ranged from 0.1818 to 0.7857, and the highest and the 14
241
lowest level of genotypic diversities were found in Hubei and Hebei, respectively. The
242
result implied that the direction of pathogen population movement could be from west
243
(Hubei) to east (Hebei) owing to the dilution of the genotypic diversity.
244
Subpopulations of FC, YY, and YX in Hubei all had a high level of genotypic
245
diversity, revealing that Hubei might be an origin of migration for Pst in 2017. In
246
general, subpopulations in southwest had high genotypic diversity than those in
247
northeast, although a few regions had some exception (e.g., genotypic diversity in
248
DAM was higher than that in DOM and LY, which were located in the southwest of
249
DAM). Therefore, the results suggested a genotype flow from the southwest to
250
northeast. There were 11 genotypes shared among 5 geographic populations (P1-P5)
251
(Fig. 2), 8 of which were common between P1 and other populations, implying that
252
the Hubei population (P1) had frequent genotypic exchange with other regions.
253
Among the 8 genotypes, 2, 5, 5, and 2 genotypes were shared between P1 and P2, P3,
254
P4, and P5, respectively, which suggested that genotypic flow occurred frequently
255
between P1 and P3 and between P1 and P4. P2 had the lowest number of shared
256
genotypes (G1, G4, and G10) with other populations, implying a weak effect on the
257
migration of Pst. There were 4 and 5 genotypes shared between P3 and P5 and
258
between P4 and P5, respectively (Fig. 2B). However, only 3 and 1 genotypes were
259
common between P1 and P5 and between P2 and P5, respectively. Comparison of
260
common genotypes among P1, P2, and P5 and among P3, P4, and P5 revealed that a
261
higher level of genotypic flow occurred from P3, P4, to P5 than from P1, P2, to P5
262
directly.
263
Effective number of alleles (Ne) and Shannon’s information index (I) showed a
264
similar trend of relationship among subpopulations. The highest and lowest levels of 15
265
Ne and I were all in YY and CW, such as Ne and I were 1.4028 and 0.4238 in YY
266
and1.1654 and 0.1436 in CW respectively (Table 3). Similarly, from the aspect of
267
sub-regional populations, P1 had the largest genotypic diversity and I, however, P5
268
had the lowest values. In addition, genotypic diversities decreased gradually from P1
269
to P5, implying a gene flow direction from the southwest to the northeast. Not
270
surprisingly, DAM had a high value of Ne and I compared to others except for
271
subpopulations in Hubei.
272
All pairwise Nm values were > 1 (data not shown), implying a certain degree of
273
gene flow existed in the current area. Gene flow mainly occurred between P1 (FC, YY,
274
and YX) and P3 (DOM, DAM, and CW) groups, as 4 of 9 pairwise Nm values were >
275
20 (Fig. 3A). Most of the values of Nm between P1 (FC, YY, and YX) and P5
276
subpopulations (ZC, AQ, and BY) were < 4 (Fig. 3A), except for between FC and AQ
277
and between FC and BY. Furthermore, most subpopulations in P3 (DOM and CW)
278
and P4 (XJ, LC, and DY) had high values of Nm compared with the subpopulations in
279
P5 (ZC, AQ, and BY) (Nm > 4). From the aspect of the overall sub-regional
280
populations, similar results were obtained. These results implied a possible migration
281
of Pst from the central-eastern (P3 and P4) to the eastern instead of direct migration
282
from Hubei (P1).
16
283
Table 3 Genotypic and genetic diversity of Puccinia striiformis f. sp. tritici collected from central and eastern China in spring 2017. Ne and I represent effective number of alleles and Shannon’s
284
information index.
285
No. of genotypes 13
Genotypic diversity 0.5417
No. of clone-corrected isolates
Ne
I
FC
No. of isolates 24
13
1.2824 ± 0.0845
0.3199 ± 0.0597
YY
44
27
0.6136
27
1.4028 ± 0.0977
0.4238 ± 0.0775
Province
County
Code
Hubei
Fancheng Yunyang
286
Yunxi
YX
28
22
0.7857
22
1.3573 ± 0.0990
0.3650 ± 0.0717
Henan
Xiping
XP
15
9
0.6000
9
1.1897 ± 0.0686
0.2338 ± 0.0723
Linying
LY
20
7
0.3500
7
1.1945 ± 0.0647
0.2459 ± 0.0652
Shandong
Daiyue
DY
14
5
0.3571
5
1.3075 ± 0.1272
0.2421 ± 0.0910
Zhoucun
ZC
16
6
0.3750
6
1.3152 ± 0.1394
0.2364 ± 0.1028
290
Anqiu
AQ
11
5
0.4545
5
1.2756 ± 0.1490
0.1934 ± 0.1032
Xiajin
XJ
10
3
0.3000
3
1.2782 ± 0.0994
0.2610 ± 0.0817
291
Dongming
DOM
27
6
0.2222
6
1.2887 ± 0.0937
0.2837 ± 0.0776
Chengwu
CW
13
3
0.2308
3
1.1654 ± 0.0913
0.1436 ± 0.0762
287 288 289
292 293
Hebei
Laicheng
LC
11
4
0.3636
4
1.2633 ± 0.1130
0.2252 ± 0.0850
Boye
BY
11
2
0.1818
2
1.2167 ± 0.1167
0.1624 ± 0.0853
Daming
DAM
20
15
0.5000
15
1.3072 ± 0.0960
0.3440 ± 0.0671
P1
96
53
0.5521
62
1.3623 ± 0.0902
0.3997 ± 0.0694
295
P2
35
13
0.3714
16
1.1867 ± 0.0515
0.2664 ± 0.0609
P3
60
21
0.3500
24
1.2952 ± 0.0763
0.3439 ± 0.0611
296
P4
35
11
0.3143
12
1.2974 ± 0.1122
0.2723 ± 0.0795
P5
38
11
0.2895
13
1. 3099± 0.1458
0.2405 ± 0.1058
264
89
0.3371
127
1.3258 ± 0.0858
0.3836 ± 0.0622
294
Total
17
297
3.2 Population genetic structure
298
Population differentiation among different subpopulations was estimated by Fst.
299
AMOVA showed that 57 of 91 of pairwise Fst values were not significant at P = 0.05
300
(Fig. 3B). Although significant genetic differentiation existed between several
301
population pairs, the value of Fst was very low, which indicated a high genetic
302
identity among these spatial subpopulations. Large values of pairwise Fst mainly
303
existed between the northeast subpopulations in Shandong (DOM, CW, DY, LW, ZC,
304
XJ, and AQ) and other subpopulations in Hubei (YX, YY, and FC), Henan (LY and
305
DOM), and the south of Hebei (DAM) (Fig. 3B). Compared to the genetic
306
differentiation between 14 subpopulations, similar results were obtained between 5
307
sub-regional populations, that values of Fst of P1 vs P2, and P1 vs P5 were much
308
higher than those of P3 vs P5, and P4 vs P5. P2 had a certain degree of genetic
309
differentiation with other sub-regional populations except for that with P3 (Fig. 3B).
310
The result of nonparametric PCoA showed that 14 spatial subpopulations were
311
not clearly separated in two-dimensional coordinates (Fig. 4A, B), with variance of
312
22.65% and 13.63% in horizontal and vertical coordinates, respectively. Among them,
313
genotypes in FC, YY, and YX of Hubei were distributed in four quartiles; several
314
genotypes in DAM, which overlapped with those in Hubei and other regions, were
315
densely distributed in the third and fourth quartiles (Fig. 4A). Similar with the result
316
of genetic differentiation, a certain percentage of genotypes of P3 and P4 were
317
covered with P1 and P5, however, there are few overlaps between P1 and P5.
18
318 319 320
Figure 3. Pairwise gene flow (Nm) and genetic differentiation (Fst) between 14 subpopulations and 5 sub-regional populations. (A) pairwise gene flow. (B) pairwise genetic differentiation. “X” indicated significant at P = 0.05. 19
321 322
Figure 4. Results of the principal coordinate analysis (PCoA) on 14 subpopulations (A) and 5 regional
323
subpopulations (B) of Puccinia striiformis f. sp. tritici sampled from central and eastern China in spring 2017.
324 325 326 327 328 20
329
The model-based clustering method implemented in STRUCTURE identified an
330
optimal number of clusters (K) as 2 (Fig. S1). Isolates assigned into group 2 (white
331
genetic group, G2 in Fig. 5B), with proportion of 31.5% of all individuals (data not
332
shown), were mainly present in FC, YY, and YX in Hubei; LY in Henan; DOM in
333
Shandong; and DAM in Hebei at ratios of 0.23, 0.59, and 0.59, 0.14, 0.33 and 0.33
334
(Fig. 5, Table 4), respectively. This indicated a high level of genetic identity among
335
them. The number of isolates belonging to group 2 decreased from the southwest to
336
the northeast, although several subpopulations interrupted this rule (XP and LY)
337
(Table 4). In contrast, the proportion of individuals belonging to group 1 (gray group,
338
G1 in Fig. 5B), that is 44.88% of all individuals (data not shown), increased among
339
subpopulations from the southwest to the northeast. The Bayesian assignment in
340
sub-regional populations showed a similar trend with 14 subpopulations from the
341
southwest to northeast in geographic distribution. Therefore, the migration direction
342
was inferred from the southwest to the northeast, and the migration events occurred
343
mainly through stepwise movement instead of direct one-step dispersal.
344 345 346 347 348 349 350 21
351
Table 4
352
Bayesian assignments of Puccinia striiformis f. sp. tritici for 14 subpopulations and 5 sub-regional populations
Province Hubei
353
Code No. of isolates FC 13 YY 27 YX 22 Henan XP 9 LY 7 Shandong DY 5 ZC 6 AQ 5 XJ 3 DOM 6 CW 3 LC 4 Hebei BY 2 DAM 15 P1 62 P2 16 P3 24 P4 12 P5 13 collected from central and eastern China in spring 2017.
Group 1 0.46 0.15 0.18 0.56 0.71 1 1 1 0.67 0.67 1 0.5 1 0.27 0.23 0.63 0.50 0.75 1
354
Footnote: The Bayesian assignment was calculated according to the result of STRUCTURE with Q > 0.8. Group 1
355
represented the percentage of individuals divided into the gray group with Q > 0.8, Group 2 represented the
356
percentage of isolates assigned into the white group with Q > 0.8, and the Admixture represented isolates assigned
357
into admixture of Group 1 and Group 2 with 0.2 < Q < 0.8 as shown in Fig. 5.
358 359 360
22
Group 2 0.23 0.59 0.59 0 0.14 0 0 0 0 0.33 0 0 0 0.33 0.24 0.31 0.25 0.25 0
Admixture 0.31 0.26 0.23 0.44 0.14 0 0 0 0.33 0 0 0.5 0 0.4 0.53 0.06 0.25 0 0
361 362
Figure 5. Assignment of isolates and geographical distribution from microsatellite genotypes using STRUCTURE
363
software. (A) Each vertical line represents an individual whose genome is partitioned into K segments (here K = 2
364
genetic groups, in gray and white, respectively). (B) Each pie indicates the proportion of individuals belonging to
365
group 1 (gray), group 2 (white), and admixture (black) at each sampling location with Q > 0.8.
366
Assuming uncorrelated allelic frequencies between sites, GENELAND inferred 2
367
distinct genetic groups (Fig. S2). The first group contained isolates in all locations of
368
Hubei and 1 site of DAM (Fig. 6A), and the second group contained the rest sites of
369
DAM, and all other 10 subpopulations (Fig. 6B).
23
370 371
Figure 6. Maps of the posterior probabilities of population membership inferred by GENELAND. (A) Cluster 1
372
and (B) cluster 2 were inferred by using GENNLAND, and contour lines indicate the spatial position of the genetic
373
discontinuities. Lighter shading indicates higher probabilities of population membership.
374 375 376
4. Discussion The ancestral populations usually possess a higher genetic diversity than the
377
derived populations (Ali et al., 2014a; Savolainen et al., 2002). In the process of the
378
pathogen migration, genetic diversity would be reduced within a short period, and it 24
379
would be recovered after continuously recruiting (Bronnenhuber et al., 2011). Liang et
380
al. (2013) inferred the main migration direction of Pst from Ningxia to Gansu in the
381
fall season, as Ningxia had higher genotypic and genetic diversities. The Gansu
382
population had large influence on Qinghai and Xinjiang populations, which was
383
inferred based on the information of genotypic and genetic diversity (Wan et al.,
384
2015). The present study found that Hubei had the highest genotypic and genetic
385
diversities, which reduced toward the northeast. It implied that Hubei could be at least
386
one of sources of Pst in northeastern regions of China. Field investigation also
387
showed that the disease occurrence of wheat stripe rust in Hubei was the earliest
388
among 4 provinces during winter of 2016 to spring of 2017 (Huang et al., 2018). As
389
that Hubei played a vital role in the over-wintering of Pst, a large pathogen population
390
could migrate to eastern and northern wheat-producing areas promoted by southwest
391
winds. The disease developed rapidly owing to the warm spring there (Li and Zeng,
392
2002). Thus, the information of disease occurrence in the spring of Hubei could be
393
used to estimate the possible risk of disease epidemics in eastern and northern areas.
394
Shared genotypes can also be used to determine genotypic exchange among
395
populations (Hovmøller et al., 2008; Lu et al. 2011; Liang et al., 2013; 2015; Wan et
396
al., 2015). In our study, the P1 group (containing subpopulations in Hubei) had the
397
largest number of genotypes shared with other populations, which demonstrated a key
398
role of Hubei on genotype exchange among subpopulations in the present study. Gene
399
flow (Nm) implied the existence of migration of Pst mainly from Hubei to the central
400
area, serving as a “bridge” and then to the northeastern area.
401 402
According to our results, there was population subdivision as well as long-distance migration in investigated area. Several results supported this inference 25
403
based on gene flow, AMOVA, Bayesian and nonparametric clustering analysis. First,
404
values of pairwise Fst between subpopulations in Hubei and the south of Hebei were
405
not significant, and subpopulations above had a relatively high level of genetic
406
differentiation with others. Second, PCoA showed that genotypes in the central and
407
northeast areas gathered to be part of genotypic distribution of Hubei, and the south of
408
Hebei. Third, subpopulations in Hubei and the south of Hebei had similar Bayesian
409
assignment revealed by STRUCTURE. Finally, GENELAND suggested that the
410
populations of Hubei and that of 1 site of Hubei (DAM) belonged to the same group.
411
The northeast subpopulations had closer genetic relationships with those of the central
412
and western Shandong than with others.
413
We have demonstrated that genetic boundaries existed among 14 subpopulations
414
revealed by GENELAND. It was reported that the dispersal of Pst could be limited by
415
mountains < 3,000 m above sea level (a.s.l.) (Xie et al., 1993), and rivers are also
416
reported as dispersal barriers at the process of dispersal for airborne pathogens
417
(Xhaard et al., 2012). However, the genetic boundaries inferred in the present study
418
were not consistent to any geographic barriers of high mountains and rivers. One
419
explanation for spatial genetic differentiation was the founder effects linked to
420
colonization events (Carlier et al., 1996; Halkett et al., 2010). That means, combined
421
with the increasing number of individuals belonging to genetic group 1 and
422
decreasing diversity from Hubei to east regions, the migration of Pst in the central and
423
eastern China seemed to be achieved through a stepwise way from Hubei to Shandong
424
(except for DAM) and Hebei, instead of direct dispersal through long-distance
425
migration. Our inference was generally consistent with the migration routes inferred
426
based on the first occurrence time of the disease in different locations (Huang et al., 26
427 428
2018). The genetics of pathogen population is affected by alternative hosts. Continuous
429
migration and colonization in our study were different from that in Pakistan, as the
430
origin of Pst in the world (Ali et al., 2014b). Although all individuals were collected
431
in spring, large genetic divergence existed between spaced populations corresponding
432
to Berberis and non-Berberis zones, respectively, in Pakistan. This was because that
433
Berberis spp. served as an alternative host of Pst with sexual reproduction. However,
434
the non-Berberis zone received pathogens from the off-season Berberis zone (Ali et
435
al., 2014b; Jin et al., 2010; Zhao et al., 2013). In China, sexual reproduction was
436
found only in Gansu, Shaanxi, and Tibet regions (Wang et al., 2016; Zhao et al., 2013),
437
and no reports showed that Berberis spp. existed in the four provinces in our study.
438
Thus, the population genetic differentiation in space scale was caused by clonal
439
propagation.
440
In the progression of spore dispersal during disease outbreak, multiple disease
441
foci can be formatted, leading to a faster development of disease compared to a single
442
disease center (Li and Zeng, 2002). In view of having the high level of genetic
443
diversity and similar genetic structure with Hubei population in DAM of Hebei, DAM
444
seemed to be an early infection center through occasional migration from Hubei
445
Province directly through long-distance migration according to our results. This
446
process offered urediniospores to other regions, which accelerated the epidemics. On
447
the other hand, spores could blow to DAM from the northwest such as Gansu or
448
Ningxia by wind in the fall (Li and Zeng, 2002). Therefore, future studies should
449
focus on the potential migration from northwest China to DAM and the
450
over-wintering process of Pst in DAM, as the local subpopulation had a high level of 27
451
genotypic and genetic diversity, implying a vital role in the migration process.
452
Undeniably, the east regions might also receive the Pst from the northwest of
453
China in the fall season because of the main northwest winds (Li and Zeng, 2002).
454
Among the previous four epidemic years, namely 1950, 1964, 1990, and 2002, disease
455
incidence in the fall season was high, and the east of China received pathogens from
456
the northwest region (Wan et al., 2004b). The pathogen overwintered locally and
457
promoted disease outbreak in spring. However, the disease incidence in the northwest
458
regions in 2016 fall was very low, and even the disease did not occur on fall seedlings
459
in the east of China (Huang et al., 2018). Thus, it seemed that the epidemics in the
460
northwest regions had weak impact on the spring epidemics in the east regions.
461
However, the potential migration of Pst from the northwest to eastern regions still
462
needed to be monitored, as Pst in most of the east regions could overwinter.
463
It is important to obtain the evidence of pathogen migration through
464
long-distance dispersal when stripe rust occurred severely that covered multiple
465
geographic regions in a season in China. However, this situation rarely occurred,
466
especially in recent decades due to the comprehensive prevention and control of
467
wheat stripe rust (Chen et al. 2013). However, wheat stripe rust occurred severely and
468
expressed as interregional pandemics in 2017 (Huang et al 2018). This provides us
469
with a valuable opportunity to study the possible migration pathway(s) of stripe rust
470
pathogen over multiple geographic regions of China to confirm our hypothesis on
471
mechanisms of interregional disease epidemics.
472 473
Interactions among host, pathogens and environment were the major determinants of incidence and epidemics of a disease (Li and Zeng, 2002). The warm 28
474
winter (Liu and Han, 2017) and rainy spring (Huang et al., 2018) might be factors
475
causing epidemics of wheat stipe rust in spring 2017. The scale of disease epidemics
476
was mainly related to the growing area of susceptible cultivars (Li and Zeng, 2002).
477
In recent years, the race of CYR34 showed a strong virulence to wheat with high
478
frequency (Liu et al., 2017). Thus, monitoring of CYR34 was vital to design disease
479
management strategies at the regional level. Although the occurrence of the disease in
480
northwest (such as Gansu) was less severe in 2017, this area is the primary source for
481
the pathogen’s over-wintering and spring epidemics. These new races were often
482
emerged in the northwestern at the early stage; hence epidemics in the central and
483
eastern China might be more serious in northwest under favorable environmental
484
conditions.
485 486
Acknowledgments:
487
This study was supported by the National Key R&D Program of China
488
(2017YFD0200400 and 2016YFD0300702) and the Key Research and Development
489
Projects of Ningxia Hui Autonomous Region (2016BZ09, East and West Science and
490
Technology Cooperation Project).
491 492
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Zeng, S., Luo, Y., 2006. Long-distance spread and interregional epidemics of wheat stripe rust in China. Plant Dis. 90, 980-988. Zhan, G., Wang, F., Luo, H., Jiang, S., Zheng, W., Huang, L., Kang, Z., 2015.
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based on genomic sequence. J. Zhejiang Univ-sci. B. 16, 727-732.
623
Zhao, J., Wang, L., Wang, Z., Chen, X., Zhang, H., Yao, J., Zhan, G., Chen, W.,
624
Huang, L., Kang, Z., 2013. Identification of eighteen Berberis species as
625
alternate hosts of Puccinia striiformis f. sp. tritici and virulence variation in the
626
pathogen isolates from natural infection of barberry plants in China.
627
Phytopathology 103, 927-934.
628 35
629
Acknowledgements: This study was supported by the National Key R&D Program of
630
China (2017YFD0200400, 2016YFD0300702) and the Key Research and
631
Development Projects of Ningxia Hui Autonomous Region (2016BZ09, East and
632
West Science and Technology Cooperation Project).
633 634
Author Contribution Statement: C.W., Y.L., and Z.M. conceived and designed the
635
experiments; C.W. performed the experiments, analyzed the data, and wrote the paper;
636
L.L., B.J., K.Z., and B.C. helped in collecting and propagating pathogens.
637 638
Competing Interest: The authors declare no competing interests.
639
36
S0261-2194(19)30320-5
DOI:
https://doi.org/10.1016/j.cropro.2019.104974
Reference:
JCRP 104974
To appear in:
Crop Protection
Received Date: 18 January 2019 Revised Date:
3 September 2019
Accepted Date: 1 October 2019
Please cite this article as: Wang, C., Li, L., Jiang, B., Zhang, K., Chu, B., Luo, Y., Ma, Z., Genetic diversity and population structure of Puccinia striiformis f. sp. tritici reveal its migration from central to eastern China, Crop Protection (2019), doi: https://doi.org/10.1016/j.cropro.2019.104974. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
1
Genetic diversity and population structure of Puccinia striiformis f.
2
sp. tritici reveal its migration from central to eastern China
3
Cuicui Wang, Leifu Li, Bingbing Jiang, Keyu Zhang, Bingyao Chu, Yong Luo* and Zhanhong
4
Ma**
5
Department of Plant Pathology, China Agricultural University, Beijing, 100193, China
6
*Corresponding author.
7
**Corresponding author.
8
Email addresses: [email protected], (Y. Luo), [email protected] (Z. Ma).
9 10 11
ABSTRACT This study focused on Puccinia striiformis f. sp. tritici (Pst), the causal pathogen
12
of wheat stripe rust in China. The central and eastern China are the main
13
wheat-producing areas in the country, and the occurrence of wheat stripe rust can
14
cause severe yield losses. To determine the population genetic structure and potential
15
pathways of migration among regional populations of the pathogen, 264 isolates were
16
sampled during spring 2017 and genotyped using 12 simple sequence repeat (SSR)
17
loci. The highest genetic diversity was detected in Hubei Province, indicating its
18
potential role as one of the source populations for the rust epidemics. A high gene
19
flow was detected among regional populations, supporting migration of Pst among
20
regions. The significant decrease in genotypic and genetic diversity from Hubei to
21
Shandong suggested that the migration direction was from central to eastern China.
22
Analysis of molecular variance (AMOVA), Bayesian assignment, and principle
23
coordinate analysis all indicated two major groups, one was mainly distributed in 1
24
Hubei and the another was mainly in the eastern region. Shared genotypes, gene flow
25
among regions, and Bayesian assignment tests revealed that the migration of Pst from
26
central to eastern of China during spring occurred mainly through stepwise
27
movement.
28
Keywords: Genotyping, SSR, Wheat tripe rust, Migration, Genetic structure
29 30 31
1. Introduction Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a
32
destructive disease in the world (Line, 2002; Wan et al., 2007). China is one of the
33
largest wheat-producing and -consuming countries in the world, where wheat stripe
34
rust occasionally caused yield losses up to millions of metric tons (Li and Zeng, 2002;
35
Wan et al., 2004b). This disease in China demonstrated unique features of
36
inter-regional epidemics, that is, disease spreading through long-distance dispersal of
37
the pathogen across large geographical regions (Zeng and Luo, 2006). Recently,
38
outbreaks occurred in the growing season of 2017 in 18 provinces, and the occurrence
39
area reached to 555.66 hm2 (Huang et al., 2018).
40
Although the sexual stage of Pst was found in Gansu, Shaanxi, and Tibet in
41
China, aeciospores produced on the alternative host Berberis spp. could infect wheat
42
only in nearby fields (Zhao et al., 2013). Therefore, with aeciospores dispersal limited
43
to short distance, large-scale epidemic of wheat stripe rust is mainly caused by
44
asexual reproduction via urediniospores through long-distance migration. Many
45
studies on epidemics of wheat stripe rust from field to regional levels had been carried
46
out in the past few decades (Xie et al., 1986; 1988; 1993; Zeng and Luo, 2006). 2
47
Wheat cropping regions in China were classified based on the features of interregional
48
stripe rust epidemics, including the regions where pathogen can over-summer
49
(urediniospores survival during summer) or over-winter (urediniospores survival
50
during winter), and the spring disease epidemic regions (Fig. 1A) (Zeng and Luo,
51
2006). The stripe rust pathogen can over-summer on wheat and volunteer plants at
52
high altitudes of mountainous areas. After summer, the urediniospores of Pst could
53
disperse to plants at low altitudes in the same region and serve as an inoculum source
54
for both neighboring areas and other distant regions, especially in the pathogen’s
55
over-wintering regions under the northwest wind (Xie et al., 1993). Urediniospores
56
can propagate continuously on wheat due to warmer winter, or survive in mycelial
57
form in wheat leaves when meet low temperature (average value < 1°C in January)
58
(Li and Zeng, 2002; Pan et al., 2016). Rust disease often burst on susceptible wheat in
59
the spring epidemic regions when met the appropriate climatic conditions, large
60
amount of urediniospores being dispersed from the over-wintering regions (Chen et
61
al., 2013).
62
According to previous observations, disease in pathogen over-wintering region is
63
highly associated with that in the eastern region (Fig. 1A) (Chen et al., 2013). In
64
addition to Sichuan Basin, northwestern Hubei is another main over-wintering region,
65
which serves as a “bridge” for the migration of the pathogen from over-summering
66
regions (e.g., Gansu and Shaanxi) to spring epidemic regions (e.g., Henan, Shandong,
67
and Hebei) with a geographical point of view (Fig. 1A) (Li and Zeng, 2002). Wheat is
68
planted from late-September to mid-October, and stripe rust occurs usually in
69
late-February in the northwest area of Hubei. Because of the high temperature in
70
winter, wheat can grow continuously, and urediniospores can over-winter and the 3
71
inoculum can be accumulated. Thus, other regions may receive the source of Pst from
72
the northwestern Hubei in the early spring (Li and Zeng, 2002).
73
Eastern spring epidemic regions, including Henan, Shandong, and Hebei
74
Provinces, are the main wheat-producing regions in China covering huge plain areas.
75
Pst could not over-summer in these areas owing to hot summer, and epidemics in
76
spring were occasional. Thus, the epidemics of wheat stripe rust in the central and
77
eastern regions mainly depend on Pst sources from other areas (Li and Zeng, 2002).
78
Drought is another factor affecting occurrence of epidemics in spring, which restricts
79
infection and accumulation of urediniospores. However, the disease’s prevalence rate
80
would be extremely high once favorable environmental conditions such as
81
temperature ranging from 7 to 12°C and rainfall in early spring occur (Chen et al.,
82
2014; Li and Zeng, 2002). Initial inoculum is a prerequisite for disease epidemics in
83
central and eastern China, although it is not known whether urediniospores of Pst
84
could be dispersed directly from other regions towards eastern China. Furthermore,
85
Hubei was hypothesized as an over-wintering region to provide inoculum for Pst for
86
eastern China. Whether the migration of Pst from Hubei to eastern China can be
87
through one-step long-distance dispersal or through stepwise dispersal is yet to be
88
identified. This could contribute to the understanding of the development of disease
89
epidemics at a spatial scale. Furthermore, the population genetic structure of the
90
pathogen in the central and eastern regions was rarely reported which will be useful to
91
understand the interregional gene flow.
92
Studies on migration pathway or direction of pathogen dispersal would
93
contribute to the estimation of disease epidemic risk, formulating comprehensive
94
prevention and control strategy of the rust disease among regions (Chen et al., 2013). 4
95
To achieve this goal, phylogeographical and population structure analyses could
96
greatly improve our ability to infer the dispersal routes of pathogens (Archie et al.,
97
2009; Xhaard et al., 2012). Inferring historical migration by using Bayesian clustering
98
methods, nonparametric clustering methods, and approximate Bayesian computation,
99
indicated that Himalayan and neighboring regions were the putative center of origin
100
of Pst in the world, and Europe was confirmed as the source population contributing
101
migrants to the South American, North American, and Australian populations (Ali et
102
al. 2014a). The Mediterranean-Central Asian populations were the origin of South
103
African populations (Ali et al., 2014a). Phylogeographical and population structure
104
analyses provide accurate information for inferring migration at a small (provincial)
105
scale during limited time. Asymmetric migration was confirmed from Gansu Province
106
to Sichuan Basin in China in the 2009 and 2010 fall seasons (Liang et al., 2015), and
107
an inverse direction of migration existed in fall and spring between these two regions
108
(Liang et al., 2013).
109
Analyzing disease epidemic patterns will help us understand the geographic
110
movement of pathogen populations. The objectives of this study were to i) understand
111
the pathogen population in genetic structure, ii) determine the migration direction, and
112
iii) infer the major migration pathways of Pst from central to eastern China.
113 114
2. Materials and Methods
115
2.1 Sampling, isolation and reproduction of Pst isolates
116 117
In the present study, populations of Pst were sampled from different geographic locations in Hubei, Henan, Shandong, and Hebei Provinces during spring 2017, a 5
118
severe epidemic year (Fig. 1B). From April and June of 2017, diseased leaves in the
119
fields showing sporulation of Pst were collected in 3 counties of Hubei Province, 2
120
counties in Henan Province, 7 counties in Shandong Province, and 2 counties in Hebei
121
Province (Fig. 1, Table 1). Five to ten sampling sites with distances of at least 50 m
122
apart from each other were identified, and approximately 10-20 diseased leaves were
123
collected in each field. Leaf samples were packed using dried absorbent paper and
124
stored at 4 . Isolates collected from the same county were considered as one
125
subpopulation, and fourteen subpopulations containing a total of 264 isolates were
126
collected. These subpopulations were assigned as follows: FC (Fancheng),
127
YY(Yunyang), YX (Yunxi), XP (Xiping), LY (Linying), DY (Daiyue), ZC (Zhoucun),
128
AQ (Anqiu), XJ (Xiajin), DOM (Dongming), CW (Chengwu), LC (Laicheng), BY
129
(Boye), and DAM (Daming) (Fig. 1, Table 1).
130
A single pustule was collected from each leaf sampled from Hubei to obtain pure
131
isolates. Purification was carried out according to Liang et al. (2013). The spores were
132
harvested, transferred into a 0.5 mL Eppendorf tube, dried in desiccators at 4
133
to 4 days, and stored at -20°C for DNA extraction. The infected leaves in the fields in
134
Henan, Shandong and Hebei bore a single lesion, which was considered presumably
135
resulted from a single spore infection (Ali et al., 2011). Therefore, the procedure of
136
propagation was removed and the DNA was exacted using the mixture of leaf and
137
urediniospores (Ali et al. 2011) sampled in Henan, Shandong and Hebei.
6
for 3
138 139
Figure 1. Geographic locations where the 264 isolates of Puccinia striiformis f. sp. tritici were collected from
140
central to eastern China in spring 2017.
141
Footnote: (A) Three main epidemic regions (pathogen over-summering, over-wintering, and spring epidemic
142
regions) of rust disease are divided according to the altitude, climate, and the wheat-producing features etc. The
143
provincial (or city’s) names are shown in abbreviated form: QH = Qinghai, GS = Gansu, NX = Ningxia, SC =
144
Sichuan, YN = Yunnan, GZ = Guizhou, SX = Shaanxi, HB = Hubei, ShX = Shanxi, HN = Henan, AH = Anhui, JS
145
= Jiangsu, HeB = Hebei, SD = Shandong, BJ = Beijing, TJ = Tianjin. (B) The distribution of sampling sites in the
146
present study.
147
Subpopulations named with abbreviation of counties’ names. FC = Fancheng, YY = Yunyang, YX = Yunxi, XP =
148
Xiping, LY = Linying, DAM = Daming, DOM = Dongming, CW = Chengwu, XJ = Xiajin, DY = Daiyue, LC =
149
Laicheng, ZC = Zhoucun, AQ = Anqiu, BY = Boye.
150 151 152
Table 1
153
Information on Puccinia striiformis f. sp. tritici isolate collections from Hubei, Henan, Shandong, and Hebei
154
provinces in spring 2017.
Province
County
Code
No. of isolates
Cultivars
Elevation (m)
Sampling time in 2017
Hubei
Fancheng
FC
24
Unknown
Unknown
April, 20
Yunyang
YY
44
Echun series
205 - 680
April, 29
7
Henan
Shandong
Hebei
Total
Yunxi
YX
28
Mianyang 31
Unknown
May, 1
Xiping
XP
15
Huaichuan916, 9817, Aikang58, Xinong207, Xinong979
Unknown
May, 15
Linying
LY
20
Unknown
Unknown
May, 14
Daiyue
DY
14
Unknown
Unknown
June, 1
Zhoucun
ZC
16
Unknown
Unknown
May, 15
Anqiu
AQ
11
Jimai22
Unknown
May, 11
Xiajin
XJ
10
Shannong21
Unknown
May, 17
Dongming
DOM
27
Unknown
Unknown
May, 16
Chengwu
CW
13
Monong99, Jimai22
Unknown
May, 16
Laicheng
LC
11
Jimai22
Unknown
May, 22
Boye
BY
11
Unknown
Unknown
May, 23
Daming
DAM
20
Nongda399, Shimai18
Unknown
May, 18
264
155 156 157
2.2 DNA extraction and SSR amplification CTAB method was used to extract the whole genomic DNA of Pst (Wan et al.,
158
2015) for all 264 samples. Among them, 96 DNA samples in Hubei were extracted
159
from urediniospores, and the left 168 DNA samples in Henan, Shandong and Hebei
160
were extracted from the infected leaf tissues (approximately 1-2 cm) (Ali et al. 2011;
161
Wan et al., 2015). A NanoDrop 2000 spectrophotometer (Gene Company Limited,
162
China) was used to determine the DNA concentrations and qualities with UV
163
absorption at wavelengths 260 and 280 nm. The DNA samples were then diluted to
164
the same concentration of 20-80 ng/µL for SSR amplification.
165
Twelve published SSR primer pairs (Bahri et al., 2009; Chen et al., 2009;
166
Enjalbert et al., 2002; Zhan et al., 2015) were used to genotype each isolate (Table 2),
167
and these SSR primers were labeled with FAM, ROX, TAMRA, or HEX fluorescence 8
168
at the 5’ end (Table 2). All PCRs were carried out according to corresponding
169
references (Bahri et al., 2009; Chen et al., 2009; Enjalbert et al., 2002; Zhan et al.,
170
2015). All SSR amplifications were performed in a thermal cycler (Eppendorf AG).
171
Subsequent separation of the PCR products was done using an ABI 3730 DNA
172
Analyzer (Applied Biosystems, Carlsbad, CA, USA) by Beijing Tsingke Biotech Co.,
173
Ltd.). A DNA maker GS500 (35-500 bp) was used as the internal standard.
9
174 175
Table 2
176
Information of the SSR primers for Puccinia striiformis f. sp. tritici used in this study. Locus
Repeat motif
CPS8
(CAG)14
CPS13
(GAC)6
CPS27
(TTC)4
CPS34
(TC)9
RJO3
(TGG)8
RJO20
(CAG)4
RJ3N
(CT)9
RJ5N
(CT)8
RJ6N
(AAC)9
RJ8N
(GAT)8
RJ13N
(ACG)6
WSR44
(GT)6
Primer sequence (5’-3’) F: FAM-GATAAGAAACAAGGGACAGC R: CAGTGAACCCAATTACTCAG F: FAM-TCCAGGCAGTAAATCAGACGC R: ATCAGCAGGTGTAGCCCCATC F: TAMRA-GATGGGGAAAAGTAAGAAGT R: GGTGGGGGATGTAAGTATGTA F: TAMRA-GTTGGCTACGAGTGGTCATC R: TAACACTACAAAAGGGGTC F: FAM- GCAGCACTGGCAGGTGG R: GATGAATCAGGATGGCTCC F: HEX-AGAAGATCGACGCACCCG R: CCTCCGATTGGCTTAGGC F: ROX-TGGTGGTGCTCCTCTAGTC R: AGGGGTCTTGTAAGATGCTC F: ROX-AACGGTCAACAGCACTCAC R: AGTTGGTCGCGTTTTGCTC F: TAMRA-CAATCTGGCGGACAGCAAC R: CACCTAGGATACCACCGCC F: FAM-ACTGGGCAGACTGGTCAAC R: TCGTTTCCCTCCAGATGGC F: HEX-TTAGCTCAGCCGGTTCCTC R: CAGGTGTAGCCCCATCTCC F: HEX-AGGCCCCAGGAACACAAAAA R: TCACACACGCTCCACAGTAC
10
Ta (℃ ℃)
No. of allele (size)
55
5 (200–212)
58
2 (125–128)
57
2 (225–228)
55
5 (104-114)
52
4 (201-212)
52
3 (283-289)
52
4 (335-343)
52
3 (223-229)
52
4 (309-318)
52
6 (301-330)
52
2 (149-152)
56
2 (188-190)
Reference Chen et al. 2009 Chen et al. 2009 Chen et al. 2009 Chen et al. 2009 Enjalbert et al. 2002 Enjalbert et al. 2002 Bahri et al. 2009 Bahri et al. 2009 Bahri et al. 2009 Bahri et al. 2009 Bahri et al. 2009 Zhan et al. 2015
177 178
2.3 Data analysis
179
Genetic diversity
180
In the process of analyzing shared genotypes among subpopulations of different
181
locations, the following 5 sub-regions (P1-P5, Fig. 2A) were defined according to
182
geographic distribution from the southwest to northeast in the current study: P1
183
contained YY, YX, and FC in Hubei Province; P2 contained LY and XY in Henan
184
Province; P3 contained DOM and CW in Shandong Province and DAM in Hebei
185
Province; P4 contained XJ, DY, and LC in Shandong Province; and P5 contained BY
186
in Hebei Province and ZC and AQ in Shandong Province. The numbers of SSR
187
genotypes in overall population and in each subpopulation were calculated using the
188
GenClone 2.0 program (Arnaud-Haond and Belkhir, 2007). The number of common
189
genotypes was calculated according to the result of overall genotype data using the
190
GenClone 2.0 program. Subsequent calculations were performed with the
191
clone-corrected data to minimize the effects of clonal reproduction especially in the
192
spring epidemic season, which might bias the population genetic analyses. The
193
subsequent analysis was carried out for 14 single subpopulations, 5 sub-regional
194
subpopulations and the overall population, respectively. Gene diversity was calculated
195
with POPGENE version 1.3.1 (Yeh et al., 1999). Mean value of effective number of
196
alleles (Ne) and Shannon’s information index (I) of each locus were calculated (Nei,
197
1972), and the standard error among 12 loci divided by the square root of 12 was
198
determined. Gene flow (Nm) between any two studied populations was calculated
199
using POPGENE version 1.3.1 estimated as Nm = 0.25(1 - Fst)/Fst (Nei, 1972). When
200
1 < Nm < 4, it implied a considerable amount of gene flow between populations, and 11
201
when Nm > 4, it indicated a high level of gene flow (Slatkin and Barton, 1989).
202
Population genetic structure
203
Pair-wise genetic differentiation (Fst) among the 14 subpopulations was
204
calculated using the AMOVA program in GENALEX version 6.5 (Peakall and
205
Smouse, 2006; 2012). A principal coordinate plot based on Nei’s distances between all
206
pairs of SSR genotypes was generated with GENALEX 6.5 and was used to generate
207
a two-dimensional PCoA plot showing the distribution of genotypes among the
208
studied populations.
12
209 210
Figure 2. Shared genotypes among subpopulations. (A) Five subregions (P1-P5) were classified according to
211
geographic features. (B) Shared genotypes and frequencies among subregion populations were calculated using
212
GenClone software.
213
A model-based Bayesian method implemented in STRUCTURE 2.3 (Pritchard et
214
al., 2000) was used to identify genetic clusters and to evaluate the extent of admixture
215
among them. A model allowing admixture and independent allele frequencies among
216
populations was used. All the Pst isolates were assigned into K clusters ranging from
217
1 to 10 based on their SSR multi-locus genotypes with clone-corrected data. For each 13
218
simulated cluster K, 15 independent runs were performed with 40,000 iterations in
219
Monte Carlo Markov Chain replications and a burn-in period of 10,000. The best K
220
value was estimated based on ∆K (Evanno et al., 2005) through uploading the results
221
of STRUCTURE to STRUCTURE HARVESTER
222
(http://taylor0.biology.ucla.edu/structureHarvester). Indi files and pop files of 15 runs
223
corresponding to the best K were processed with CLUMPP 1.1.2 (Jakobsson et al.,
224
2007), and then, a bar plot was generated in Distruct 1.1 (Rosenberg, 2004) using the
225
output of CLUMPP 1.1.2 (Jakobsson et al., 2007). Results of the bar plot as pie charts
226
were generated for each location based on the membership fraction (Q) Q > 0.8
227
(Pritchard et al., 2000).
228
Another Bayesian clustering method implemented in R package GENELAND
229
version 4.0.4 (Guillot et al., 2005) was used to detect the geographic discontinuities
230
for microsatellite loci. Ten independent runs were performed with 1,000,000 MCMC
231
iterations, and it saved once for every 1000 interactions in GENELAND. The tested
232
number of genetic clusters (K) was set from 1 to 10. The largest posterior probability
233
of population membership among 10 runs was computed using a burn-in period length
234
of 200 iterations.
235 236
3. Results
237
3.1 Genetic diversity
238
Totally, 89 genotypes were obtained from 264 isolates using the 12 primer pairs,
239
with the overall genotypic diversity as 0.3371 (Table 3). The genotypic diversities
240
among 14 subpopulations ranged from 0.1818 to 0.7857, and the highest and the 14
241
lowest level of genotypic diversities were found in Hubei and Hebei, respectively. The
242
result implied that the direction of pathogen population movement could be from west
243
(Hubei) to east (Hebei) owing to the dilution of the genotypic diversity.
244
Subpopulations of FC, YY, and YX in Hubei all had a high level of genotypic
245
diversity, revealing that Hubei might be an origin of migration for Pst in 2017. In
246
general, subpopulations in southwest had high genotypic diversity than those in
247
northeast, although a few regions had some exception (e.g., genotypic diversity in
248
DAM was higher than that in DOM and LY, which were located in the southwest of
249
DAM). Therefore, the results suggested a genotype flow from the southwest to
250
northeast. There were 11 genotypes shared among 5 geographic populations (P1-P5)
251
(Fig. 2), 8 of which were common between P1 and other populations, implying that
252
the Hubei population (P1) had frequent genotypic exchange with other regions.
253
Among the 8 genotypes, 2, 5, 5, and 2 genotypes were shared between P1 and P2, P3,
254
P4, and P5, respectively, which suggested that genotypic flow occurred frequently
255
between P1 and P3 and between P1 and P4. P2 had the lowest number of shared
256
genotypes (G1, G4, and G10) with other populations, implying a weak effect on the
257
migration of Pst. There were 4 and 5 genotypes shared between P3 and P5 and
258
between P4 and P5, respectively (Fig. 2B). However, only 3 and 1 genotypes were
259
common between P1 and P5 and between P2 and P5, respectively. Comparison of
260
common genotypes among P1, P2, and P5 and among P3, P4, and P5 revealed that a
261
higher level of genotypic flow occurred from P3, P4, to P5 than from P1, P2, to P5
262
directly.
263
Effective number of alleles (Ne) and Shannon’s information index (I) showed a
264
similar trend of relationship among subpopulations. The highest and lowest levels of 15
265
Ne and I were all in YY and CW, such as Ne and I were 1.4028 and 0.4238 in YY
266
and1.1654 and 0.1436 in CW respectively (Table 3). Similarly, from the aspect of
267
sub-regional populations, P1 had the largest genotypic diversity and I, however, P5
268
had the lowest values. In addition, genotypic diversities decreased gradually from P1
269
to P5, implying a gene flow direction from the southwest to the northeast. Not
270
surprisingly, DAM had a high value of Ne and I compared to others except for
271
subpopulations in Hubei.
272
All pairwise Nm values were > 1 (data not shown), implying a certain degree of
273
gene flow existed in the current area. Gene flow mainly occurred between P1 (FC, YY,
274
and YX) and P3 (DOM, DAM, and CW) groups, as 4 of 9 pairwise Nm values were >
275
20 (Fig. 3A). Most of the values of Nm between P1 (FC, YY, and YX) and P5
276
subpopulations (ZC, AQ, and BY) were < 4 (Fig. 3A), except for between FC and AQ
277
and between FC and BY. Furthermore, most subpopulations in P3 (DOM and CW)
278
and P4 (XJ, LC, and DY) had high values of Nm compared with the subpopulations in
279
P5 (ZC, AQ, and BY) (Nm > 4). From the aspect of the overall sub-regional
280
populations, similar results were obtained. These results implied a possible migration
281
of Pst from the central-eastern (P3 and P4) to the eastern instead of direct migration
282
from Hubei (P1).
16
283
Table 3 Genotypic and genetic diversity of Puccinia striiformis f. sp. tritici collected from central and eastern China in spring 2017. Ne and I represent effective number of alleles and Shannon’s
284
information index.
285
No. of genotypes 13
Genotypic diversity 0.5417
No. of clone-corrected isolates
Ne
I
FC
No. of isolates 24
13
1.2824 ± 0.0845
0.3199 ± 0.0597
YY
44
27
0.6136
27
1.4028 ± 0.0977
0.4238 ± 0.0775
Province
County
Code
Hubei
Fancheng Yunyang
286
Yunxi
YX
28
22
0.7857
22
1.3573 ± 0.0990
0.3650 ± 0.0717
Henan
Xiping
XP
15
9
0.6000
9
1.1897 ± 0.0686
0.2338 ± 0.0723
Linying
LY
20
7
0.3500
7
1.1945 ± 0.0647
0.2459 ± 0.0652
Shandong
Daiyue
DY
14
5
0.3571
5
1.3075 ± 0.1272
0.2421 ± 0.0910
Zhoucun
ZC
16
6
0.3750
6
1.3152 ± 0.1394
0.2364 ± 0.1028
290
Anqiu
AQ
11
5
0.4545
5
1.2756 ± 0.1490
0.1934 ± 0.1032
Xiajin
XJ
10
3
0.3000
3
1.2782 ± 0.0994
0.2610 ± 0.0817
291
Dongming
DOM
27
6
0.2222
6
1.2887 ± 0.0937
0.2837 ± 0.0776
Chengwu
CW
13
3
0.2308
3
1.1654 ± 0.0913
0.1436 ± 0.0762
287 288 289
292 293
Hebei
Laicheng
LC
11
4
0.3636
4
1.2633 ± 0.1130
0.2252 ± 0.0850
Boye
BY
11
2
0.1818
2
1.2167 ± 0.1167
0.1624 ± 0.0853
Daming
DAM
20
15
0.5000
15
1.3072 ± 0.0960
0.3440 ± 0.0671
P1
96
53
0.5521
62
1.3623 ± 0.0902
0.3997 ± 0.0694
295
P2
35
13
0.3714
16
1.1867 ± 0.0515
0.2664 ± 0.0609
P3
60
21
0.3500
24
1.2952 ± 0.0763
0.3439 ± 0.0611
296
P4
35
11
0.3143
12
1.2974 ± 0.1122
0.2723 ± 0.0795
P5
38
11
0.2895
13
1. 3099± 0.1458
0.2405 ± 0.1058
264
89
0.3371
127
1.3258 ± 0.0858
0.3836 ± 0.0622
294
Total
17
297
3.2 Population genetic structure
298
Population differentiation among different subpopulations was estimated by Fst.
299
AMOVA showed that 57 of 91 of pairwise Fst values were not significant at P = 0.05
300
(Fig. 3B). Although significant genetic differentiation existed between several
301
population pairs, the value of Fst was very low, which indicated a high genetic
302
identity among these spatial subpopulations. Large values of pairwise Fst mainly
303
existed between the northeast subpopulations in Shandong (DOM, CW, DY, LW, ZC,
304
XJ, and AQ) and other subpopulations in Hubei (YX, YY, and FC), Henan (LY and
305
DOM), and the south of Hebei (DAM) (Fig. 3B). Compared to the genetic
306
differentiation between 14 subpopulations, similar results were obtained between 5
307
sub-regional populations, that values of Fst of P1 vs P2, and P1 vs P5 were much
308
higher than those of P3 vs P5, and P4 vs P5. P2 had a certain degree of genetic
309
differentiation with other sub-regional populations except for that with P3 (Fig. 3B).
310
The result of nonparametric PCoA showed that 14 spatial subpopulations were
311
not clearly separated in two-dimensional coordinates (Fig. 4A, B), with variance of
312
22.65% and 13.63% in horizontal and vertical coordinates, respectively. Among them,
313
genotypes in FC, YY, and YX of Hubei were distributed in four quartiles; several
314
genotypes in DAM, which overlapped with those in Hubei and other regions, were
315
densely distributed in the third and fourth quartiles (Fig. 4A). Similar with the result
316
of genetic differentiation, a certain percentage of genotypes of P3 and P4 were
317
covered with P1 and P5, however, there are few overlaps between P1 and P5.
18
318 319 320
Figure 3. Pairwise gene flow (Nm) and genetic differentiation (Fst) between 14 subpopulations and 5 sub-regional populations. (A) pairwise gene flow. (B) pairwise genetic differentiation. “X” indicated significant at P = 0.05. 19
321 322
Figure 4. Results of the principal coordinate analysis (PCoA) on 14 subpopulations (A) and 5 regional
323
subpopulations (B) of Puccinia striiformis f. sp. tritici sampled from central and eastern China in spring 2017.
324 325 326 327 328 20
329
The model-based clustering method implemented in STRUCTURE identified an
330
optimal number of clusters (K) as 2 (Fig. S1). Isolates assigned into group 2 (white
331
genetic group, G2 in Fig. 5B), with proportion of 31.5% of all individuals (data not
332
shown), were mainly present in FC, YY, and YX in Hubei; LY in Henan; DOM in
333
Shandong; and DAM in Hebei at ratios of 0.23, 0.59, and 0.59, 0.14, 0.33 and 0.33
334
(Fig. 5, Table 4), respectively. This indicated a high level of genetic identity among
335
them. The number of isolates belonging to group 2 decreased from the southwest to
336
the northeast, although several subpopulations interrupted this rule (XP and LY)
337
(Table 4). In contrast, the proportion of individuals belonging to group 1 (gray group,
338
G1 in Fig. 5B), that is 44.88% of all individuals (data not shown), increased among
339
subpopulations from the southwest to the northeast. The Bayesian assignment in
340
sub-regional populations showed a similar trend with 14 subpopulations from the
341
southwest to northeast in geographic distribution. Therefore, the migration direction
342
was inferred from the southwest to the northeast, and the migration events occurred
343
mainly through stepwise movement instead of direct one-step dispersal.
344 345 346 347 348 349 350 21
351
Table 4
352
Bayesian assignments of Puccinia striiformis f. sp. tritici for 14 subpopulations and 5 sub-regional populations
Province Hubei
353
Code No. of isolates FC 13 YY 27 YX 22 Henan XP 9 LY 7 Shandong DY 5 ZC 6 AQ 5 XJ 3 DOM 6 CW 3 LC 4 Hebei BY 2 DAM 15 P1 62 P2 16 P3 24 P4 12 P5 13 collected from central and eastern China in spring 2017.
Group 1 0.46 0.15 0.18 0.56 0.71 1 1 1 0.67 0.67 1 0.5 1 0.27 0.23 0.63 0.50 0.75 1
354
Footnote: The Bayesian assignment was calculated according to the result of STRUCTURE with Q > 0.8. Group 1
355
represented the percentage of individuals divided into the gray group with Q > 0.8, Group 2 represented the
356
percentage of isolates assigned into the white group with Q > 0.8, and the Admixture represented isolates assigned
357
into admixture of Group 1 and Group 2 with 0.2 < Q < 0.8 as shown in Fig. 5.
358 359 360
22
Group 2 0.23 0.59 0.59 0 0.14 0 0 0 0 0.33 0 0 0 0.33 0.24 0.31 0.25 0.25 0
Admixture 0.31 0.26 0.23 0.44 0.14 0 0 0 0.33 0 0 0.5 0 0.4 0.53 0.06 0.25 0 0
361 362
Figure 5. Assignment of isolates and geographical distribution from microsatellite genotypes using STRUCTURE
363
software. (A) Each vertical line represents an individual whose genome is partitioned into K segments (here K = 2
364
genetic groups, in gray and white, respectively). (B) Each pie indicates the proportion of individuals belonging to
365
group 1 (gray), group 2 (white), and admixture (black) at each sampling location with Q > 0.8.
366
Assuming uncorrelated allelic frequencies between sites, GENELAND inferred 2
367
distinct genetic groups (Fig. S2). The first group contained isolates in all locations of
368
Hubei and 1 site of DAM (Fig. 6A), and the second group contained the rest sites of
369
DAM, and all other 10 subpopulations (Fig. 6B).
23
370 371
Figure 6. Maps of the posterior probabilities of population membership inferred by GENELAND. (A) Cluster 1
372
and (B) cluster 2 were inferred by using GENNLAND, and contour lines indicate the spatial position of the genetic
373
discontinuities. Lighter shading indicates higher probabilities of population membership.
374 375 376
4. Discussion The ancestral populations usually possess a higher genetic diversity than the
377
derived populations (Ali et al., 2014a; Savolainen et al., 2002). In the process of the
378
pathogen migration, genetic diversity would be reduced within a short period, and it 24
379
would be recovered after continuously recruiting (Bronnenhuber et al., 2011). Liang et
380
al. (2013) inferred the main migration direction of Pst from Ningxia to Gansu in the
381
fall season, as Ningxia had higher genotypic and genetic diversities. The Gansu
382
population had large influence on Qinghai and Xinjiang populations, which was
383
inferred based on the information of genotypic and genetic diversity (Wan et al.,
384
2015). The present study found that Hubei had the highest genotypic and genetic
385
diversities, which reduced toward the northeast. It implied that Hubei could be at least
386
one of sources of Pst in northeastern regions of China. Field investigation also
387
showed that the disease occurrence of wheat stripe rust in Hubei was the earliest
388
among 4 provinces during winter of 2016 to spring of 2017 (Huang et al., 2018). As
389
that Hubei played a vital role in the over-wintering of Pst, a large pathogen population
390
could migrate to eastern and northern wheat-producing areas promoted by southwest
391
winds. The disease developed rapidly owing to the warm spring there (Li and Zeng,
392
2002). Thus, the information of disease occurrence in the spring of Hubei could be
393
used to estimate the possible risk of disease epidemics in eastern and northern areas.
394
Shared genotypes can also be used to determine genotypic exchange among
395
populations (Hovmøller et al., 2008; Lu et al. 2011; Liang et al., 2013; 2015; Wan et
396
al., 2015). In our study, the P1 group (containing subpopulations in Hubei) had the
397
largest number of genotypes shared with other populations, which demonstrated a key
398
role of Hubei on genotype exchange among subpopulations in the present study. Gene
399
flow (Nm) implied the existence of migration of Pst mainly from Hubei to the central
400
area, serving as a “bridge” and then to the northeastern area.
401 402
According to our results, there was population subdivision as well as long-distance migration in investigated area. Several results supported this inference 25
403
based on gene flow, AMOVA, Bayesian and nonparametric clustering analysis. First,
404
values of pairwise Fst between subpopulations in Hubei and the south of Hebei were
405
not significant, and subpopulations above had a relatively high level of genetic
406
differentiation with others. Second, PCoA showed that genotypes in the central and
407
northeast areas gathered to be part of genotypic distribution of Hubei, and the south of
408
Hebei. Third, subpopulations in Hubei and the south of Hebei had similar Bayesian
409
assignment revealed by STRUCTURE. Finally, GENELAND suggested that the
410
populations of Hubei and that of 1 site of Hubei (DAM) belonged to the same group.
411
The northeast subpopulations had closer genetic relationships with those of the central
412
and western Shandong than with others.
413
We have demonstrated that genetic boundaries existed among 14 subpopulations
414
revealed by GENELAND. It was reported that the dispersal of Pst could be limited by
415
mountains < 3,000 m above sea level (a.s.l.) (Xie et al., 1993), and rivers are also
416
reported as dispersal barriers at the process of dispersal for airborne pathogens
417
(Xhaard et al., 2012). However, the genetic boundaries inferred in the present study
418
were not consistent to any geographic barriers of high mountains and rivers. One
419
explanation for spatial genetic differentiation was the founder effects linked to
420
colonization events (Carlier et al., 1996; Halkett et al., 2010). That means, combined
421
with the increasing number of individuals belonging to genetic group 1 and
422
decreasing diversity from Hubei to east regions, the migration of Pst in the central and
423
eastern China seemed to be achieved through a stepwise way from Hubei to Shandong
424
(except for DAM) and Hebei, instead of direct dispersal through long-distance
425
migration. Our inference was generally consistent with the migration routes inferred
426
based on the first occurrence time of the disease in different locations (Huang et al., 26
427 428
2018). The genetics of pathogen population is affected by alternative hosts. Continuous
429
migration and colonization in our study were different from that in Pakistan, as the
430
origin of Pst in the world (Ali et al., 2014b). Although all individuals were collected
431
in spring, large genetic divergence existed between spaced populations corresponding
432
to Berberis and non-Berberis zones, respectively, in Pakistan. This was because that
433
Berberis spp. served as an alternative host of Pst with sexual reproduction. However,
434
the non-Berberis zone received pathogens from the off-season Berberis zone (Ali et
435
al., 2014b; Jin et al., 2010; Zhao et al., 2013). In China, sexual reproduction was
436
found only in Gansu, Shaanxi, and Tibet regions (Wang et al., 2016; Zhao et al., 2013),
437
and no reports showed that Berberis spp. existed in the four provinces in our study.
438
Thus, the population genetic differentiation in space scale was caused by clonal
439
propagation.
440
In the progression of spore dispersal during disease outbreak, multiple disease
441
foci can be formatted, leading to a faster development of disease compared to a single
442
disease center (Li and Zeng, 2002). In view of having the high level of genetic
443
diversity and similar genetic structure with Hubei population in DAM of Hebei, DAM
444
seemed to be an early infection center through occasional migration from Hubei
445
Province directly through long-distance migration according to our results. This
446
process offered urediniospores to other regions, which accelerated the epidemics. On
447
the other hand, spores could blow to DAM from the northwest such as Gansu or
448
Ningxia by wind in the fall (Li and Zeng, 2002). Therefore, future studies should
449
focus on the potential migration from northwest China to DAM and the
450
over-wintering process of Pst in DAM, as the local subpopulation had a high level of 27
451
genotypic and genetic diversity, implying a vital role in the migration process.
452
Undeniably, the east regions might also receive the Pst from the northwest of
453
China in the fall season because of the main northwest winds (Li and Zeng, 2002).
454
Among the previous four epidemic years, namely 1950, 1964, 1990, and 2002, disease
455
incidence in the fall season was high, and the east of China received pathogens from
456
the northwest region (Wan et al., 2004b). The pathogen overwintered locally and
457
promoted disease outbreak in spring. However, the disease incidence in the northwest
458
regions in 2016 fall was very low, and even the disease did not occur on fall seedlings
459
in the east of China (Huang et al., 2018). Thus, it seemed that the epidemics in the
460
northwest regions had weak impact on the spring epidemics in the east regions.
461
However, the potential migration of Pst from the northwest to eastern regions still
462
needed to be monitored, as Pst in most of the east regions could overwinter.
463
It is important to obtain the evidence of pathogen migration through
464
long-distance dispersal when stripe rust occurred severely that covered multiple
465
geographic regions in a season in China. However, this situation rarely occurred,
466
especially in recent decades due to the comprehensive prevention and control of
467
wheat stripe rust (Chen et al. 2013). However, wheat stripe rust occurred severely and
468
expressed as interregional pandemics in 2017 (Huang et al 2018). This provides us
469
with a valuable opportunity to study the possible migration pathway(s) of stripe rust
470
pathogen over multiple geographic regions of China to confirm our hypothesis on
471
mechanisms of interregional disease epidemics.
472 473
Interactions among host, pathogens and environment were the major determinants of incidence and epidemics of a disease (Li and Zeng, 2002). The warm 28
474
winter (Liu and Han, 2017) and rainy spring (Huang et al., 2018) might be factors
475
causing epidemics of wheat stipe rust in spring 2017. The scale of disease epidemics
476
was mainly related to the growing area of susceptible cultivars (Li and Zeng, 2002).
477
In recent years, the race of CYR34 showed a strong virulence to wheat with high
478
frequency (Liu et al., 2017). Thus, monitoring of CYR34 was vital to design disease
479
management strategies at the regional level. Although the occurrence of the disease in
480
northwest (such as Gansu) was less severe in 2017, this area is the primary source for
481
the pathogen’s over-wintering and spring epidemics. These new races were often
482
emerged in the northwestern at the early stage; hence epidemics in the central and
483
eastern China might be more serious in northwest under favorable environmental
484
conditions.
485 486
Acknowledgments:
487
This study was supported by the National Key R&D Program of China
488
(2017YFD0200400 and 2016YFD0300702) and the Key Research and Development
489
Projects of Ningxia Hui Autonomous Region (2016BZ09, East and West Science and
490
Technology Cooperation Project).
491 492
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Acknowledgements: This study was supported by the National Key R&D Program of
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China (2017YFD0200400, 2016YFD0300702) and the Key Research and
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Development Projects of Ningxia Hui Autonomous Region (2016BZ09, East and
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West Science and Technology Cooperation Project).
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Author Contribution Statement: C.W., Y.L., and Z.M. conceived and designed the
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experiments; C.W. performed the experiments, analyzed the data, and wrote the paper;
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L.L., B.J., K.Z., and B.C. helped in collecting and propagating pathogens.
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Competing Interest: The authors declare no competing interests.
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