Genetic diversity and population structure of Rhizoctonia solani AG-1 IA, the causal agent of rice sheath blight, in South China

Abstract

The genetic diversity and population structure among 72 rice-infecting isolates of Rhizoctonia solani AG-1 IA, collected from 12 counties (subpopulations) of Guangdong, Guangxi and Hainan provinces (populations) in south China, were investigated using nine inter-simple sequence repeat (ISSR) markers. A total of 116 bands were amplified, with a majority of amplified fragments ranging from 500 bp to 2500 bp in size, of which 110 (94.8%) were polymorphic. Seventy-two isolates were grouped into six major clusters at 73% genetic similarity coefficient by the unweighted pair group method with arithmetic mean (UPGMA) with Dice’s distance matrices. The genetic diversity was high [percentage of polymorphic bands (P %) = 94.83%; Shannon’s diversity index (I) = 0.3175; Nei’s diversity (h) = 0.2034] at the population level, but low within populations [P % = 53.38%; Shannon’s diversity index (I) = 0.2734; Nei’s diversity (h) = 0.1811]. The mean coefficient of gene differentiation (Gst) was 0.165, indicating that 83.5% of the genetic diversity resided within the population. The genetic similarity values among 12 subpopulations ranged from 0.9672 to a minimum of 0.8641, and genetic distance values ranged from 0.1461 to 0.0333. Total gene flow (Nm) of 5.5810 indicated that there was significant gene flow among the sampled populations. Analysis of molecular variance (AMOVA) demonstrated that there was a relatively high level (81.93%) of genetic variation within subpopulations, with the gene differentiation coefficient (Φ_ST) being 0.181. A Mantel test suggested that there was no significant correlation between genetic differentiation and geographical distance.

Résumé

La diversité et la structure génétiques des populations de 72 isolats de Rhizoctonia solani (AG-1 IA) infectant le riz, collectés dans 12 comtés (sous-populations) des provinces du Guandong, du Guangxi et d’Hainan dans le sud de la Chine (populations), ont été étudiées à l’aide de 9 marqueurs ISSR (amplification intermicrosatellite). En tout, 116 bandes ont été amplifiées, dont la majorité des fragments mesurait de 500 bp à 2 500 bp, desquelles 110 (94.8 %) étaient polymorphes. En outre, 72 isolats ont été groupés en 6 principaux clusters dont le coefficient de similarité était de 73 % selon la méthode des moyennes par paire non pondérée, en concordance avec les matrices de distance de Dice. Le taux de diversité génétique était élevé à l’échelle de la population (pourcentage de bandes polymorphes [P %] = 94.83 %; indice de diversité de Shannon [I] = 0.3175; indice de diversité de Nei [h] = 0.2034), mais faible au sein des populations (P % = 53.38 %; indice de diversité de Shannon [I] = 0.2734; indice de diversité de Nei [h] = 0.1811). Le coefficient moyen de différenciation génétique (Gst) était de 0.165, indiquant que 83.5 % de la diversité génétique se trouvait au sein de la population. Les valeurs de similarité génétique chez 12 sous-populations variaient de 0.9672 à 0.8641, et celles de distance génétique, de 0.1461 à 0.0333. Le flux génique total (Nm) de 5.5810 indiquait que le flux génique parmi les populations échantillonnées était significatif. L’analyse de variance moléculaire (AMOVA) a démontré qu’il y avait un taux relativement élevé (81.93 %) de variation génétique au sein des sous-populations, avec un coefficient de différenciation génétique (Φ_ST) de 0.181. Un test de Mantel a suggéré qu’il n’y avait pas de corrélation significative entre la différenciation génétique et la distance géographique.

Keywords:

• gene flow
• genetic diversity
• inter-simple sequence repeat (ISSR)
• phylogenetic analysis
• population genetic structure
• Rhizoctonia solani

Mots clés:

• amplification intermicrosatellite (ISSR)
• analyse phylogénétique
• diversité génétique
• flux génique
• Rhizoctonia solani
• structure génétique de population

Introduction

Rice sheath blight  (RSB), caused by the soilborne basidiomycetous fungus Rhizoctonia solani Kühn [teleomorph Thanatephorus cucumeris (A. B. Frank) Donk], is one of the most devastating fungal diseases of rice worldwide (Stodart et al. 2007; Taheri et al. 2007; Bernardes-de-Assis et al. 2009). In China, RSB affects c. 15–20 million ha of paddy-irrigated rice and causes a yield loss of about 6 million tons of rice grain per year. Furthermore, yield losses per growing season can reach up to 50% (Bernardes-de-Assis et al. 2009; Zheng et al. 2013).

RSB is difficult to control due to the low level of inherent resistance of rice against this disease (Anderson et al. 1972; Khodayari et al. 2009). Although partial genetic resistance to RSB has been reported and major genes responsible for resistance have been found (Pan et al. 1999), RSB is still not efficiently controlled by resistance breeding (Cubeta & Vilgalys 1997; Wu et al. 2003). The major management strategies include agricultural preventive treatments and the use of the antifungal antibiotics jinggangmycin or validamycin A, neither of which is highly effective (Jiang et al. 2012). Moreover, the application of fungicides can result in environmental concerns (Willocquet et al. 2000; Ciampi et al. 2008; Taheri & Tarighi 2011; González et al. 2012). Hence, development of resistant varieties as a means to control RSB is desirable, but greatly dependent on an understanding of the genetic diversity and population structure of the pathogen.

A method based on classification of isolates into anastomosis groups (AG) has been used for the identification and classification of isolates of R. solani (Liu & Sinclair 1992; Cubeta & Vilgalys 1997). Thus far, 14 AGs have been identified within the R. solani complex. Isolates of AG-1 have been further divided into three subgroups, i.e. IA, IB and IC (Ogoshi 1987). Among them, isolates of R. solani of AG-1 IA have been associated with sheath blight in rice (Wang et al. 2013). However, anastomosis groupings do not reflect genetic variation or taxonomic relationships within and between AGs. Sometimes, isolates within an AG are more similar than between different AGs, and genetically distinct subgroups exist within AGs (Wang et al. 2013). Therefore, problems associated with studying genetic diversity in R. solani can be best addressed by the use of DNA-based molecular techniques (Cubeta & Vilgalys 1997; Toda et al. 1999; Sharma et al. 2005).

Numerous genetic markers were developed to evaluate genetic variability and characterization of R. solani. These genetic markers included isoenzymes (Liu & Nickrent 1990; Neeraja et al. 2003), DNA base sequence complementary analysis (Cubeta & Vilgalys 1997; Ceresini et al. 2002), restriction fragment length polymorphisms (RFLP) (O’Brien 1994; Rosewich et al. 1999; Ceresini et al. 2002), random amplified polymorphic DNA (RAPD) (Duncan et al. 1993; Zhou et al. 2002; Dubey et al. 2012; Mirmajlessi et al. 2012; Wang et al. 2013), amplified fragment length polymorphism (AFLP) (Taheri et al. 2007), simple sequence repeats (SSR) (Bernardes-de-Assis et al. 2009) and inter-simple sequence repeats (ISSR) (Sharma et al. 2005; Guleria et al. 2007; Stodart et al. 2007; Khodayari et al. 2009; Dubey et al. 2012; Mirmajlessi et al. 2012).

ISSR markers were first developed in 1994, and can be used to rapidly differentiate closely related individuals. ISSR analysis employs a dominant inheritance marker and can generate a great number of highly informative and reproducible alleles. This technique involves amplification of genomic DNA segments between two identical SSR regions with a single primer 16–18 bp long composed of a repeated sequence anchored at the 3′ or 5′ end by 2–4 arbitrary nucleotides (Zietkiewicz et al. 1994; Wang et al. 2005). ISSR analysis could be highly variable within a species and has higher repeatability and stability than RAPD analysis in utilizing longer primers that allow more stringent annealing temperatures (Nan et al. 2003; Stodart et al. 2007). Moreover, ISSR is less expensive to conduct than AFLP analysis, and this technique has been widely applied to studies of genetic diversity, phylogeny and in plant breeding (Pradeep et al. 2002; Stodart et al. 2007; Dubey et al. 2012). Li et al. (2010) used ISSR analysis to evaluate 100 isolates of R. solani from 10 counties in Fujian Province, China. The tested isolates could be classified into five groups and high genetic diversity and significant genetic variance were found among populations. Hu et al. (2010) isolated 186 Rhizoctonia-like fungal strains in 28 counties of Guangxi Province and found that R. solani was the most virulent and common species. Recently, Wang et al. (2013) reported considerable genetic variation among 180 isolates of R. solani AG-1 IA from six provinces in southern China, and that most genetic diversity was distributed within populations. The reasons for this distribution might include a high gene flow and low genetic differentiation among populations. Three virulence groups were identified, with moderately virulent isolates predominant in the R. solani population. Virulence has been a very useful feature for the characterization of diversity among strains of R. solani AG-1 IA. At least four different methods have been developed to evaluate the virulence of R. solani isolates on rice under glasshouse conditions (Wang et al. 2013).

In the present study, the ISSR technique was used to investigate genetic variation of R. solani AG-1 IA, the causal agent of rice sheath blight, collected from three provinces (populations) in south China. The objective of this study was to assess the population genetic structure and genetic differentiation level of R. solani AG-1 IA, and understand the genetic relationship and genetic variation patterns in different regions.

Materials and methods

Field sampling, fungal isolates and DNA extraction from R. solani AG-1 IA

A total of 72 isolates of R. solani AG-1 IA were collected from 12 counties (subpopulations) in three provinces (populations) [Guangdong (GD), Guangxi (GX) and Hainan (HN)]. These counties and provinces represent the important rice producing areas in south China and the details of the 72 isolates are given in Table 1 and Fig. 1. One  isolate of Colletotrichum gloeosporioides was used as an outgroup control.

Fig. 1 The distribution of the 72 isolates of Rhizoctonia solani AG-1 IA collected from Guangdong, Guangxi and Hainan provinces.

Table 1. Details of the 72 isolates of Rhizoctonia solani AG-1 IA from Guangdong (GD), Guangxi (GX) and Hainan (HN).

Isolate code	Population & size	Subpopulation & size	Sampling date
GD01–GD06	GD, 24	Maoming, GD (MMGD), 6	July 9, 2007
GD07–GD12		Conghua, GD (CHGD), 6	July 13, 2007
GD13–GD18		Wuhua, GD (WHGD), 6	July 17, 2007
GD19–GD24		Lianzhou, GD (LZGD), 6	July 18, 2007
GX01–GX06	GX, 24	Bobai, GX (BBGX), 6	July 10, 2007
GX07–GX12		Zhaoping, GX (ZPGX), 6	July 11, 2007
GX13–GX18		Liuzhou, GX (LZGX), 6	July 21, 2007
GX19–GX24		Baise, GX (BSGX), 6	July 19, 2007
HN01–HN06	HN, 24	Ledong, HN (LDHN), 6	July 19, 2007
HN07–HN12		Dongfang, HN (DFHN), 6	July 19, 2007
HN13–HN18		Qiongzhong, HN (QZHN), 6	July 18, 2007
HN19–HN24		Wenchang, HN (WCHN), 6	July 21, 2007

ISSR analysis

Nine ISSR markers were identified with 69 primers, synthesized by Beijing AuGCT DNA-SYN Biotechnology Co., Ltd, and used for PCR amplification (Table 2). Amplification reactions were performed in a 25 μL volume containing 2 μL of 10 × PCR buffer, 2 μL of 2.5 mm dNTP, 1 μL 0.5 μm of each primer, 0.2 μL Taq DNA polymerase (5 U μL⁻¹) (TAKARA, Kyoto, Japan), 1 μL (8 ng) template DNA and 17.8 μL ddH₂O.

Table 2. Sequences of arbitrary primers, annealing temperature, numbers of amplified and polymorphic bands resulting from ISSR analysis.

Primer	Sequence (5′ → 3′)	Nucleotide count	Annealing tem. (˚C)	Amplified band	Polymorphic band	Polymorphism (%)
EZ1	(AC)8 AG	18	55	10	10	100.00
EZ2	(AC)8 TG	18	55	12	12	100.00
EZ7	(AG)8 T	17	52	14	12	85.71
EZ9	(AG)8 G	17	52	11	9	81.81
EZ11	(AG)8 GG	18	54	13	11	84.62
EZ13	(AC)8 C	17	52	16	16	100.00
EZ18	(CA)8 G	17	54	16	16	100.00
EZ25	(AC)8 T	17	52	7	7	100.00
EZ27	(AC)8 G	17	54	17	17	100.00
Average				12.89	12.22	94.83
Total				116	110	94.83

Table 3. Genotype  diversity of Rhizoctonia solani AG-1 IA from the 12 subpopulations.

Subpopulation	h	I	NP	P (%)
MMGD	0.1838	0.2749	61	52.59
CHGD	0.1846	0.2766	61	52.59
WHGD	0.1879	0.2795	60	51.72
LZGD	0.1291	0.1985	48	41.38
BBGX	0.1892	0.2836	63	54.31
ZPGX	0.1836	0.2785	64	55.17
LZGX	0.2248	0.3405	77	66.38
BSGX	0.2237	0.3349	74	63.79
LDHN	0.1484	0.2269	54	46.55
DFHN	0.1759	0.2634	58	50.00
QZHN	0.1593	0.2445	58	50.00
WCHN	0.1826	0.2787	65	56.03
Average	0.1811	0.2734	61.92	53.38
Total	0.2034	0.3175	110	94.83

Notes: h: Nei’s gene diversity; I: Shannon’s diversity index; N_P, number of polymorphic loci; P, percentage of polymorphic loci.

The optimized PCR analysis was performed using a Veriti^TM 96 gradient thermal cycler (Applied Biosystems, CA, USA) with the following amplification conditions: initial denaturation at 94 °C for 4 min, followed by 38 cycles of denaturation at 94 °C for 60 s, appropriate annealing temperature for each primer (Table 2) for 1 min and extension at 72 °C for 1 min. A final extension step at 72 °C for 4 min was included as a final step. PCR products were resolved with 1% (w/v) agarose gel electrophoresis at 120 V for 30 min in 1 × TAE (Tris-acetate-EDTA) buffer. The gels were stained with Goldview^TM (Dingguo, Shanghai, China) (10 mg mL⁻¹), and photographed under UV light with Alpha Imager HP system (ProteinSimple, CA, USA). A DL 5000 bp DNA ladder was used as a molecular weight marker to determine ISSR profiles.

Data analysis

Amplified products, which were reproducible and consistent in performance, were scored for band presence (1) or absence (0) and a binary qualitative data matrix was constructed with UVP Lab Works. A cluster analysis of all isolates was carried out using the unweighted pair group method with arithmetic mean (UPGMA) and a dendrogram was generated by using Ntsys-PC 1.8 (Rohlf 1993). The percentage of polymorphic loci (P), Shannon’s diversity index (I) and Nei’s gene diversity (h) were used to estimate genetic variation using POPGENE ver. 1.32 (Yeh et al. 1997). Average genetic diversity within population (H_S) and total genetic diversity among populations (H_T) were also calculated. Population differentiation was analysed for polymorphism among populations by G_ST. Gene flow (Nm) was estimated from Nm = 0.5 (1–G_ST)/G_ST (Nei 1978).

POPGENE ver. 1.32 software (Yeh et al. 1997) was used to investigate the distribution of genetic differentiation in R. solani AG-1 IA. Genetic differentiation among populations was estimated by pairwise values of ф_ST. Analysis of molecular variance was used to compare the partition of the genetic variation differing in geographical origin. AMOVA calculations were performed in WINAMOVA ver. 1.55. Mantel’s test was used for assessing the relationships between pairwise values of ф_ST and the natural geographical distances among 12 subpopulations. The significance of the relationship was assessed with 9999 permutations.

Results

Genetic diversity

Sixty-nine primers were tested for their ability to produce polymorphic bands ranging from 500 bp to 2500 bp with a subset of the R. solani AG-1 IA isolates (Fig. 2). Nine of the primers were determined to produce bright, polymorphic and reproducible amplicons. For the 72 isolates, 116 scored bands were produced with an average of 12.22 markers per primer, of which 110 bands (94.83%) were polymorphic (Table 2).

Fig. 2 An example of the ISSR polymorphism of Rhizoctonia solani AG-1 IA isolates revealed by using ISSR primer EZ27. (Left to right: lane M, molecular weight marker DL5000 DNA ladder; lanes 1–16 represent the isolates).

At the subpopulation level, Nei’s diversity (h) ranged from 0.1291 to 0.2248 with an average of 0.1811. Shannon’s diversity index (I) varied from 0.1985 to 0.3405 with an average of 0.2734. The percentage of polymorphic loci (P %) ranged from 41.38% (LZGD) to 66.38% (LZGX) with an average of 53.38%. At the population level, Nei’s diversity (h), Shannon’s diversity index (I) and percentage of polymorphic loci (P %) were 0.2034%, 0.3175% and 94.83%, respectively (Table 3). These findings demonstrated that genetic diversity among populations was higher than within populations.

For the 72 R. solani AG-1 IA isolates, Nei and Li’s similarity coefficient ranged from a high of 0.97 to a minimum of 0.72. Cluster analysis indicated that there was a genetically distinct variation among the 72 isolates. On the dendrogram, six main clusters (groups) were produced at 0.73 similarity coefficient (Fig. 3). These results indicated that high genetic variance occurred among the 72 isolates, and that isolates with different geographical origins assembled to different clusters with diverse similarity coefficients. The isolates from the Guangdong (GD) and Hainan (HN) populations mainly formed a distinct cluster. However, those from Guangxi (GX) population were grouped into five clusters, indicating that the GX population possesses the highest genetic differentiation. The genetic relationship between the GX and GD populations was relatively close, which may have resulted from gene exchange.

Fig. 3 Dendrogram of the 72 isolates of Rhizoctonia solani AG-1 IA based on ISSR data using Nei and Li’s similarity coefficient and UPGMA clustering method. The scale bar at the bottom represents the Li’s genetic similarity coefficient.

Genetic differentiation and gene flow

The total genetic diversity (Ht) was calculated to be 0.2372. Within population genetic diversity (Hs) ranged from 0.1665 (HN) to 0.2053 (GX) with an average of 0.1811 (Table 4). The mean of gene differentiation (Gst = 0.165) indicated that most of the genetic differentiation (83.5%) of R. solani AG-1 IA was found within populations. The total gene flow number (Nm) was 5.581, indicating that higher gene exchange has taken place between populations, and that genetic differentiation was mainly maintained within populations.

Table 4. Comparisons of genetic diversity and gene differentiation in three populations of Rhizoctonia solani AG-1 IA from south China.

Population & size	HT	HS	GST	Nm
GD, 24	0.2086	0.1714	0.1783	2.3039
GX, 24	0.2381	0.2053	0.1377	3.1319
HN, 24	0.2028	0.1665	0.1789	2.2952
Average	0.2165	0.1811	0.1650	2.5770
Total	0.2372	0.2177	0.0822	5.5810

Genetic structure of R. solani AG-1 IA populations

For the purpose of detecting the relationship among populations, among subpopulations and within subpopulations, molecular variance was examined using AMOVA (Table 5). The percentage of variance component found among populations was 9.14% (ф_ST = 0.091, P < 0.002), while it was 8.92% (ф_ST = 0.098, P < 0.002) among subpopulations and 81.93% (ф_ST = 0.181, P < 0.002) within subpopulations. All P values were statistically significant, indicating that the geographical region was a significant factor affecting the genetic structure of these populations.

Table 5. Analysis of molecular variance (AMOVA).

Source of variation	df	SS	MS	Variance component	Percentage of variance component%	фST	P
Among populations	2	122.19	61.09	1.57	9.14	0.091	<0.002
Among subpopulations	9	209.91	23.32	1.53	8.92	0.098	<0.002
Within subpopulation	60	846.33	14.10	14.10	81.93	0.181	<0.002

Note: df, degree of freedom; SS, sum of square; MS, mean square; ф_ST, correlation within populations relative to the total.

To further investigate the genetic structure of R. solani AG-1 IA, the genetic similarity values and genetic distance of R. solani AG-1 IA isolates from 12 subpopulations were compared. The results showed that the genetic similarity coefficient ranged from 0.9672 (between subpopulations of LZGX and BSGX) to 0.8641 (between subpopulations of LZGD and LZGX), and the counterparts of genetic distance ranged from 0.1461 to 0.0333 (S1). These results demonstrated that there was a high degree of similarity among subpopulations. A UPGMA dendrogram showed a major division between the Guangdong population and all others (Fig. 4) based on Nei’s genetic distance. Within the second group, subpopulations from Hainan and Guangxi provinces clustered together. The Guangxi population showed a closer genetic relationship to the Hainan population than to the Guangdong population. The LZGX and BSGX subpopulations clustered into one sub-group and appeared to be closer to each other.

Fig. 4 UPGMA dendrogram of the genetic relationships among 12 subpopulations of Rhizoctonia solani AG-1 IA inferred from a matrix of Nei’s genetic distances. The scale bar at the bottom represents the genetic distance.

To test the relationships among subpopulations and distance, a matrix was constructed based on the pairwise Φ_ST distance among subpopulations (S2). The genetic differentiation coefficient (Φ_ST) ranged from −0.0070 to 0.3089, and the highest pairwise Φ_ST was between the LZGD and ZPGX subpopulations, indicating that these possessed the greatest genetic differentiation. Mantel tests were used to calculate the relationship between the matrices of geographical distance and pairwise Φ_ST coefficients with the MXCOMP program (Fig. 5). The results showed that the data points were distributed without distinct regularity, suggesting that there was no significant correlation between genetic differentiation and geographical distance in three populations of R. solani AG-1 IA from south China. The phylogenetic relationships of the 12 subpopulations in this study did not meet the model of isolation by distance.

Fig. 5 Correlation between ΦST values and geographical distances among 12 subpopulations of Rhizoctonia solani AG-1 IA.

Discussion

The results from this study clearly demonstrated that high genetic diversity exists among populations of R. solani AG-1 IA, a finding that is consistent with the results of previous reports using the ISSR technique (Guleria et al. 2007; Khodayari et al. 2009; Dubey et al. 2012; González et al. 2012). Cluster analysis showed that most isolates from the Guangdong and Hainan populations belonged to the same group, whereas the isolates from the Guangxi population were distributed into four groups. These findings indicated that clusters were significantly correlated with the geographical origin of the isolates. Some isolates from BSGX and LZGX were clustered in three different clades, and the percentage of polymorphic loci of these subpopulations was very high (66.38 and 63.79, respectively), indicating that the isolates from BSGX and LZGX possessed higher genetic diversity. Similar result was also described in studies of populations of R. solani AG-1 IA from Jiangsu Province (Wu et al. 2013).

The genetic structure of a population can be affected by numerous evolutionary factors including mating system, gene flow, seed dispersal, mode of reproduction and natural selection (Cubeta & Vilgalys 1997; Kar et al. 2010). A gene flow (Nm) value greater than 1.0 is considered necessary to prevent divergence resulting from genetic drift (Wright 1950). The total gene flow value of 5.5810 found in this study suggests that gene flow among the south China R. solani populations is very high. The exchange of genes among populations is known to homogenize allele frequencies among populations and determines the relative effect of selection and genetic drift (Kar et al. 2010). Genetic differentiation becomes non-significant because more gene exchange triggers more genetic similarity. The gene flow value (Nm) was low within populations, however, and the genetic variance became significant within populations, which was confirmed by the rejection of the geographical isolation by distance model in the present study.

The high genetic variability observed in the R. solani AG-1 IA population could be regarded as an adaptive strategy for increasing the population fitness from different geographical environments, climate and co-evolution with hosts (González et al. 2012). Twenty-two isolates from Guangdong population and 22 isolates from Hainan population were clustered into two distinct groups, which may reflect the exchange of genetic information only within the population. The higher variation observed in the Guangxi population might be a result of the frequent change of rice germplasm in breeding programmes and the host selection pressure on the pathogen.

Acknowledgements

This work was supported by the Special Fund for Agro-scientific Research in the Public Interest of the People’s Republic of China [grant number 201403075].