Development of Genomic SSR Markers and Analysis of Genetic Diversity of 40 Haploid Isolates of Ustilago maydis in China.
Byline: Meijing Zhang, Yanping Chen, Jianhua Yuan and Qingchang MengAbstract
Ustilago maydis (DC) Corda is an important pathogen of maize (Zea mays L.) in China. The poorly defined genetic diversity of this pathogen hinders efforts to better predict and complement resistance of maize in the breeding programs. In this study, we developed 2196 SSR markers in U. maydis, of which 122 were successfully experimentally validated and used to analyze genetic diversity and population structure of the 40 U. maydis isolates. In total, these markers generated 488 alleles with an average of 4.0 alleles per marker. The PIC values ranged from 0.094 to 0.811 with an average of 0.479. The averaged genetic similarity was 0.462 among all isolates. Cluster analysis and population structure analysis both classified the 40 isolates into six different groups. Group classification showed no clear relationship with mating type locus a' or the geographical origin of the studied isolates.
This study demonstrates a unique genetic diversity among U. maydis haplotypes in corn production areas in China. 2015 Friends Science Publishers
Keywords: Genetic diversity; Mating type; Simple sequence repeats (SSR); Ustilago maydis
Introduction
The basidiomycete Ustilago maydis (DC) Corda is a ubiquitous causative agent of smut disease on maize (Zea mays), one of the world's most important cereal crops (FAO 2012, http://www.fao.org). This disease is especially important in China where it is estimated to be responsible for 5%~10% yield losses in annual maize production (Ding et al., 2008). In sexually reproducing fungi like U. maydis, sexual compatibility is controlled by mating type genes, which function to prevent self-fertilization and ensure the genetic diversity of the population (Wahl et al., 2010). In U. maydis, mating is accompanied with a dramatic change of biology and is controlled by two independent mating type loci, termed a' and b'. The recognition reaction of different products of these loci further leads to the formation of a dikaryon that requires a maize host for further propagation (Kronstad and Staben, 1997).
Hence, infection by this smut fungus triggers gall formation in maize vegetation and reproductive organs, which can result in stunted plant growth and reduced yield, leading to economic losses (LA1/4bberstedt et al., 1998; Baumgarten et al., 2007; Ding et al., 2008). However, in some places, such as Mexico, the resulting gall's of smut on the maize ear are sold as an expensive delicacy (Valverde et al., 1995; Juarez- Montiel et al., 2011).
U. maydis has recently been used as model system for investigating the molecular interactions between biotrophic fungal pathogens and plant hosts (Martinez-Espinoza et al., 2002; Kamper et al., 2006). While recent research of U. maydis has explored sex determination and morphogenesis, molecular biology, and genomics (Brefort et al., 2009) little has been done to assess genetic diversity of U. maydis strains, especially in China. Although mating type loci are believed to be very important for genetic diversity, Zambino et al. (1997) surprisingly found very low levels of differentiation in U. maydis populations with respect to mating type locus b variation. However, mating type locus a, and genome-wide markers have not been investigated for the ability to differentiate genetic diversity in U. maydis.
Microsatellites or simple sequence repeat (SSR) genetic markers are polymerase chain reaction (PCR)-based, frequently co-dominant, hyper-variable, multi-allelic, abundant and well distributed throughout all prokaryotic and eukaryotic genomes and would likely be useful to differentiate diversity in U. maydis (Powell et al., 1996; Zane et al., 2002; Kalia et al., 2010). SSRs have been successfully used for genetic diversity studies in many fungi (Scott and Chakraborty, 2008; Vogelgsang et al., 2009; Ren et al., 2012). To assess SSR flanking regions, sequences have to first be known for primer design. Recently developed genomics resources including the genomic sequence of U. maydis (Kamper et al., 2006) allow thousands of genome wide SSR markers to be developed now.
We hypothesized that SSR markers will be useful to better characterize the diversity in U. maydis and will correspond to mating type and geographic origins of isolates. Here, we developed a set of SSR markers in U. maydis: (1) to analyze genotype and genetic diversity, (2) to assess genetic relationship among isolates collected from the main production region for corn in China, and (3) evaluate the correspondence to mating type and geographic origin of isolates.
Materials and Methods
SSR Mining and Primer Design
Genomic sequences of U. maydis were downloaded from the Broad Institute (USA, http://www.broadinstitute.org/). SSR loci were screened using the MISA program (http://pgrc.ipk- gatersleben.de/misa/) with default parameters. Compound SSRs were defined as = 2 SSRs interrupted by =100 bases (Sonah et al., 2011). Then 126 randomly selected primer pairs were then synthesized (Invitrogen, Shanghai, China).
U. maydis Haploid Isolates
U. maydis-affected galls in maize were collected in the summers of 2011 and 2012 mainly from East China, located between longitude 87.58o- 126.65o W to E, and latitudes 32.05o- 45.75o S to N representing the major region of corn production in China. At least eight haploid isolates were isolated from each gall using a monospore separation method. In our previous study (Zhang et al., 2013) two pairs of sequence-specific primers were used to detect two alleles (mfa1 and mfa2) at locus a, a mating type locus that controls haploid cell fusion of two U. maydis strains. Only two strains with either mfa1 or mfa2 were kept for further DNA isolation and SSR analysis. SG200, a modified solopathogenic fungal strain derived from the U. maydis strain FB1 with mfa1 locus (Doehlemann et al., 2008), was used as reference. Table 1 lists the isolates used in this study.
PCR Amplification and Product Detection
Genomic DNA was extracted from strains of U. maydis using the CTAB protocol as described by Weising et al. (1995). For PCR amplification, a ten L reaction mixture was made containing 1 L 1A-PCR Buffer, 0.5 U Taq DNA polymerase, 4 nmol/L dNTPs, 10 pmol/L primer pairs and 20 ng gDNA. PCR amplification conditions were as follows: pre-denaturation at 94oC for 5 min, followed by 35 cycles of 94oC for 30 s, 55oC for 30 s, and 72oC for 1 min, with a final extension step of 72oC for 10 min. PCR products were resolved on a 8.0% (w/v) non-denatured polyacrylamide gel at a constant current 70 mA for 1h. Detection of SSR bands was visualized by the silver staining method.
Data Analysis
Allele size was estimated using a 50-bp ladder (Promega, Beijing). The number of alleles, major allele frequency, and polymorphisminformationcontent(PIC)valueswere calculated using Power Marker version 3.25 (Liu and Muse, 2005).
The genetic diversity assuming four geographic origin populations (Table 1) was analyzed using POPGENE (V1.32; Yeh et al., 1997). The polymorphic bands of each SSR marker were treated as binary characters for their presence (1) or absence (0) in the 40 haploid isolates and was analyzed using the NTSYS-pc program (V2.1, Rohlf, 2002). Genetic similarity between isolates was calculated by the Dice similarity coefficient based on the proportion of shared alleles (Dice, 1945; Nei and Li, 1979) with the SIMQUAL (similarity for qualitative data) subprogram. Cluster analysis was performed using the Un-weighted Pair Group Method with Arithmetic average (UPGMA, Yu et al., 2006) in the SAHN subprogram.
Population structure analysis was performed with STRUCTURE version 2.3.4 (Pritchard et al., 2000). The optimum number of populations (K) was selected by testing K=1 to K=12 using five independent runs of 10,000 burn-in period length at fixed iterations of 100,000 with a model allowing for admixture and correlated allele frequencies. After the optimal K (K=6 in this study) was determined according to Murray et al. (2009), an additional run of 5,000,000 burn-in and sampling iterations were used to infer population structure.
Results
Characterization of SSR Primers in U. maydis Genome
U. maydis genomic DNA totaling 19.68 Mbp belonging to 274 contigs (average of 71.8 Kb per contig) were investigated for SSRs. The contigs were assembled into 47 supercontigs, among which 35 contigs could be placed into the 23 linkage groups of U. maydis. We identified 2462 SSR loci on 240 contigs with the average of 9 SSRs per contig and 125 SSRs per million base pairs. Because of the existence of 249 compound formation SSRs, 2213 candidate SSR loci were used for further primer design.
2196 pairs of SSR primer were successfully designed with the average product size of 221 bp. There were 653 SSRs with mono-nucleotide repeat motifs, 600 with di-nucleotide, and 861 with tri-nucleotide. Comparably, there were 348 SSRs with tetra, penta- and hexa- repeat motifs, suggesting SSRs with 1~3 repeat motif(s) dominate in the genome of U. maydis (Fig. 1). Linkage groups one to five contained the greatest number of SSRs, which corresponded to greater sequence lengths (r2=0.89). An uneven distribution for different motif type was observed among different linkage groups with the exception of di- and tetra-nucleotide (data not shown). SSR density (number of SSRs per Mb) was lowest on linkage groups 1, 3, 6, 7 and 9.
Validation of SSR Markers
To validate the SSR primers we randomly selected 126 SSR markers for further analysis. Of 126 SSR markers only 4 JAAS0014, JAAS0017, JAAS0055 and JAAS0094 did not
Table 1: Description and geographic origins of isolates used in this study
Name###Collection date###Allelea Province###City###Geographical groupb###Longitude###Latitude###Cluster groupc
UM01_JS###6/2011###mfa2###Jiangsu (JS)###Nanjing###S. China###118 46'E###32 03'N###I
UM02_JS###6/2011###mfa1###Jiangsu (JS)###Nanjing###S. China###118 46'E###32 03'N###I
UM03_JS###6/2011###mfa2###Jiangsu (JS)###Nanjing###S. China###118 46'E###32 03'N###I
UM04_HB 10/2011###mfa1###Hebei (HB)###Shijiazhuang N.E. China###114 30'E###38 02'N###I
UM05_HB 10/2011###mfa2###Hebei (HB)###Shijiazhuang N.E. China###114 30'E###38 02'N###I
UM06_BJ###10/2011###mfa2###Beijing (BJ)###Beijing###N.E. China###116 23'E###39 55'N###I
UM07_HLJ 8/2011###mfa1###Heilongjiang (HLJ)###Harbin###N.E. China###126 39'E###45 45'N###I
UM08_HLJ 8/2011###mfa2###Heilongjiang (HLJ)###Harbin###N.E. China###126 39'E###45 45'N###I
UM09_JS###7/2011###mfa1###Jiangsu (JS)###Yancheng###S. China###120 07'E###33 23'N###III
UM10_JS###7/2011###mfa2###Jiangsu (JS)###Yancheng###S. China###120 07'E###33 23'N###III
UM11_SD 9/2011###mfa2###Shandong (SD)###Jinan###Huang-Huai###116 59'E###36 40'N###III
UM12_SD 9/2011###mfa1###Shandong (SD)###Jinan###Huang-Huai###116 59'E###36 40'N###III
UM13_LN 10/2011###mfa2###Liaoning (LN)###Dandong###N.E. China###124 23'E###40 07'N###III
UM14_LN 10/2011###mfa1###Liaoning (LN)###Dandong###N.E. China###124 23'E###40 07'N###III
UM15_JS###9/2011###mfa2###Jiangsu (JS)###Xuzhou###Huang-Huai###117 11'E###34 16'N###II
UM16_JS###9/2011###mfa1###Jiangsu (JS)###Xuzhou###Huang-Huai###117 11'E###34 16'N###II
UM17_JS###10/2011###mfa2###Jiangsu (JS)###Lianyungang Huang-Huai###119 10'E###34 36'N###II
UM18_JS###10/2011###mfa1###Jiangsu (JS)###Lianyungang Huang-Huai###119 10'E###34 36'N###II
UM19_JS###10/2011###mfa1###Jiangsu (JS)###Huaian###Huang-Huai###119 01'E###33 35'N###II
UM20_JS###10/2011###mfa2###Jiangsu (JS)###Huaian###Huang-Huai###119 01'E###33 35'N###II
UM21_JS###6/2012###mfa2###Jiangsu (JS)###Nanjing###S. China###118 46'E###32 03'N###II
UM22_JS###6/2012###mfa1###Jiangsu (JS)###Nanjing###S. China###118 46'E###32
03'N###II
UM23_JS###7/2011###mfa2###Jiangsu (JS)###Taizhou###S. China###119 54'E###32 29'N###II
UM24_JS###7/2011###mfa1###Jiangsu (JS)###Taizhou###S. China###119 54'E###32 29'N###II
UM25_JS###10/2011###mfa1###Jiangsu (JS)###Suqian###Huang-Huai###118 15'E###33 56'N###V
UM26_JS###10/2011###mfa2###Jiangsu (JS)###Suqian###Huang-Huai###118 15'E###33 56'N###V
UM27_JS###10/2012###mfa1###Jiangsu (JS)###Suqian###Huang-Huai###118 15'E###33 56'N###V
UM28_JS###10/2012###mfa2###Jiangsu (JS)###Suqian###Huang-Huai###118 15'E###33 56'N###V
UM29_HN 10/2012###mfa2###Henan (HN)###Xunxian###Huang-Huai###114 23'E###35 35'N###V
UM30_HN 10/2012###mfa1###Henan (HN)###Xunxian###Huang-Huai###114 23'E###35 35'N###V
UM31_XJ###8/2011###mfa1###Xinjiang (XJ)###Urumqi###N.W. China###87 35'E###43 48'N###IV
UM32_XJ###8/2011###mfa2###Xinjiang (XJ)###Urumqi###N.W. China###87 35'E###43 48'N###IV
UM33_SX 10/2012###mfa2###Shanxi (SX)###Taiyuan###Huang-Huai###112 28'E###37 43'N###V
UM34_SX 10/2012###mfa1###Shanxi (SX)###Taiyuan###Huang-Huai###112 28'E###37 43'N###V
UM35_JS###10/2012###mfa1###Jiangsu (JS)###Nanjing###S. China###118 46'E###32 03'N###V
UM36_HN 10/2012###mfa1###Henan (HN)###Zhengzhou###Huang-Huai###113 38'E###34 45'N###IV
UM37_XJ###8/2012###mfa1###Xinjiang (XJ)###Urumqi###N.W. China###87 35'E###43 48'N###IV
UM38_XJ###8/2012###mfa1###Xinjiang (XJ)###Urumqi###N.W. China###87 35'E###43 48'N###IV
UM39_SD 7/2012###mfa2###Shandong (SD)###Jinan###Huang-Huai###116 59'E###36 40'N###IV
UM40_USA 9/2011###mfa1###USA###VI
Amplify any bands in the 40 isolates. 72.9% (89 of 122) SSR primers were polymorphic and amplified unique bands in the 40 isolates. Calculated product sizes were well matched with that as predicted. Examples of typical amplification pattern were shown in Fig. 2. Most markers generated reproducible, clear, distinct and polymorphic amplifications products, suggesting these SSRs were suitable for genetic diversity analysis.
PIC of SSR Markers
The polymorphic loci resulted in a total of 488 alleles for the 40 isolates. The number of alleles per locus ranged from two to 10, with an average of 4.0 alleles across the 122 loci. The marker JAAS0064, flanking the CAG repeated motif, produced 10 different loci in 40 isolates, the highest among the 122 SSR markers. The following top two were JAAS0023 and JAAS0025, which produced eight different alleles each.
The PIC values ranged from 0.094 to 0.811 with an average of 0.479 for all SSR markers. Of these markers, JAAS0025 with the repeated motif of TGCAAG showed the highest PIC value (0.811); JAAS0023 ranked second (PIC=0.807). Despite having the most alleles JAAS0064 had a lower PIC value (0.787). Among polymorphic markers JAAS0037 showed the highest value for major allele frequency (0.950) and the lowest value for PIC (0.094). Statistical analysis showed no significant difference between the different SSR repeat type and PIC value (F(6, 121)=1.88, p=0.089).
Analysis of Genetic Diversity Revealed by SSR Markers
Estimated parameters of genetic diversity for four
Table 2: Genetic diversity within 4 geographic groups of U. maydis
###Geographic group###NA###NE###I###PPB(%)
###N.E. China###2.3091.001a###1.9270.804###0.6360.439###76.4
###Huang-Huai###3.261.085###2.2730.945###0.8680.380###98.4
###S. China###2.7890.977###2.1320.793###0.7930.366###93.5
###N.W. China###1.7560.657###1.5780.552###0.4370.363###63.4
###average###2.528###1.978###0.683###82.9
###All###3.9841.431###2.4741.038###0.9830.391
Geographic populations in China are presented in Table 2. There was abundant genetic variation of U. maydis at both the population level and the isolate level. Shannon's information index (I) varied between 0.437 (N.W. China) and 0.868 (Huang-Huai) with an average of 0.683 at the population level. Population Huang-Huai had 98.4% of polymorphic bands (PPB). At the isolate level Shannon's information index (I) was 0.983 with standard error of 0.391.
Cluster Analysis of 40 U. maydis Strains
A similarity coefficient matrix was calculated and then used for cluster analysis. The similarity coefficient of overall strains averaged 0.462. Strains UM01 and UM03 both from Jiangsu Province showed the greatest similarity with each other (similarity coefficient =0.845). Isolate UM19 from Jiangsu province and the USA strain UM40 were the most distant (similarity coefficient of 0.193).
UPGMA clustering result showed the 40 isolates could be classified into six major groups (Fig. 3). Isolates from UM01 to UM08 formed group I. Isolates from UM15 to UM24 (all from Jiangsu) together formed group II. Group III included isolates from UM09 to UM14. Group IV contained six members from UM31 to UM39 excluding UM33 to UM35. Group V consisted of nine members (isolates from UM27 to UM30, UM33, UM34 and UM35). Strain UM40 (Group VI) from the USA did not cluster with any Chinese isolates and is an outgroup for this study. Most clusters were further supported by STRUCTURE analysis (Fig. 4) when the number of populations K=6. Clustering and STRUCTURE results both suggest that mating type locus a does not correlate to SSR genetic diversity (p=0.845 by 2 test) and we did not observe isolates with mfa1 or mfa2 clustered in the same group.
Discussion
In our study 2,462 SSRs were identified in U. maydis, which was less than the 6,854 SSRs suggested by a previous study across seven filamentous fungi that included U. maydis (Li et al. 2009). This was likely a result of different calculation methods and did not reduce the usefulness of this study because more than could be experimentally validated were still detected. Among those tested 96.7% of designed SSR markers this study developed were successful and 72.9% of SSR markers generated unique and useful bands. Both Li et al. (2009) and this study found that SSRs were unevenly distributed among chromosomes and linkage groups.
Genetic diversity high at the population and isolate level indicates a relatively high level of genetic exchange within and among the different sample populations. However, isolates collected from the same city were genetically similar to each other and often clustered together. Members of Group I all came from geographic group S. China and N.E. China, while Group II contained most of isolates from Jiangsu (Huang-Huai and S. China). Members of Group V came from Huang-Huai with only one exception (isolate UM35). However, overall cluster groups were not statistically correlated with geographic origin. For example, isolates from Jiangsu were scatted into most groups (excluding group IV), while Group II contained isolates all from Jiangsu.
The importance in recognition of different alleles at mating type loci a was expected to differentiate isolates. However, from the cluster analysis (Fig. 4), the isolates with the same allele at locus a did not group together, meaning that mating type loci a is not directly correlated with genetic diversity in these isolates. There are a number of potential causes for this unexpected result. First, it might be partly due to high recombination rate in the U. maydis genome (Kamper et al., 2006). This would lead to low linkage disequilibrium between locus a and other loci and low genetic correlations. Second, it could be due to the high mutation rates of SSRs in this species. High mutation rates are useful for providing separation between genotypes but could provide too much divergence. Similarly, if the mutation rates are high enough and SSRs do not follow a stepwise mutation model, these loci can back mutate resulting in homeoplasy in allele lengths.
It would therefore be interesting to use SNP markers, which have a much lower mutation rate to see if the same patterns were observed.
Different combinations of two strains of U. maydis might have different virulence to the host (J Shi, personal communication). But until now, little has been known about the relationship between virulence and genetic diversity. Though recently both genetic and molecular tools have facilitated to identify virulence-related genes more information about the genetics and evolution of virulence at the population level needs to be further elucidated in U. maydis (Chacko and Gold, 2012; Rodriguez-Kessler et al., 2012; Heimel et al., 2013; Karakkat et al., 2013; Mueller et al., 2013).
Conclusion
In conclusion, this paper reports the development of 122 highly polymorphic SSR markers useful for characterizing U. maydis. The applications of these markers encompass strain identification, analysis of genetic diversity, and population structure studies. Genetic relationship between Chinese strains is not correlated with geography.
Acknowledgement
This work was partly supported by Funding Project for Independent Innovation of Agricultural Science and Technology in Jiangsu Province (Grant No. CX(12)5016) and National Natural Science Foundation of China (Grant No. 31301330). The authors acknowledge Dr. Doehlemann, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany, for providing the SG200 strain used in this study. The authors also greatly acknowledge Dr. Seth Murray and Mr. Adam Mahan, Texas AandM University, Texas, USA, for their comments and critical reviews.
References
Baumgarten, A.M., J. Suresh, G. May and R.L. Phillips, 2007. Mapping QTLs contributing to Ustilago maydis resistance in specific plant tissues of maize. Theor. Appl. Genet., 114: 12291238
Brefort, T., G. Doehlemann, A. Mendoza-Mendoza, S. Reissmann, A. Djamei and R. Kahmann, 2009. Ustilago maydis as a Pathogen. Annu. Rev. Phytopathol., 47: 423445
Chacko, N. and S. Gold, 2012. Deletion of the Ustilago maydis ortholog of theAspergillussporulation regulator medA affects mating and virulence through pheromone response. Fungal. Genet. Biol., 49: 426432
Dice, L.R., 1945. Measures of the amount of ecologic association between species. Ecology, 26: 297302
Ding, J.Q., X.M. Wang, S. Chander and J.S. Li, 2008. Identification of QTL for maize resistance to common smut by using recombinant inbred lines developed from the Chinese hybrid Yuyu22. J. Appl. Genet., 49: 147154
Doehlemann, G., R. Wahl, M. Vranes, R.P. de Vries, J. Kamper and R. Kahmann, 2008. Establishment of compatibility in the Ustilago maydis/maize pathosystem. J. Plant. Physiol., 165: 2940
Heimel, K., J. Freitag, M. Hampel, J. Ast, M. Bolker and J. Kamper, 2013. Crosstalk between the unfolded protein response and pathways that regulate pathogenic development in Ustilago maydis. Plant Cell, 25: 42624277
Juarez-Montiel, M., S. Ruiloba de Leon, G. Chavez-Camarillo, C. Hernandez-Rodriguez and L. Villa-Tanaca, 2011. Huitlacoche (corn smut), caused by the phytopathogenic fungus Ustilago maydis, as a functional food. Rev. Iberoam. Micol., 28: 6973
Kalia, R.K., M.K. Rai, S. Kalia, R. Singh and A.K. Dhawan, 2010. Microsatellite markers: an overview of the recent progress in plants. Euphytica, 177: 309334
Kamper, J., R. Kahmann, M. Bolker, L.J. Ma, T. Brefort, B.J. Saville, F. Banuett, J.W. Kronstad, S.E. Gold, O. Muller, 2006. Insights from the genome of the biotrophic fungal plant pathogen Ustilago maydis. Nature, 444: 97101
Karakkat, B.B., S.E. Gold and S.F. Covert, 2013. Two members of the Ustilago maydis velvet family influence teliospore development and virulence on maize seedlings. Fungal. Genet. Biol., 61: 111119
Kronstad, J.W. and C. Staben, 1997. Mating type in filamentous fungi. Annu. Rev. Genet., 31: 245276
Li, C.Y., L. Liu, J. Yang, J.B. Li, Y. Su, Y. Zhang, Y.Y. Wang and Y.Y. Zhu, 2009. Genome-wide analysis of microsatellite sequence in seven filamentous fungi. Interdiscip. Sci., 1: 141150
Liu, K. and S.V. Muse, 2005. PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics, 21: 2128 2129
LA1/4bberstedt, T., D. Klein and A.E. Melchinger, 1998. Comparative QTL mapping of resistance to Ustilago maydis across four populations of European flint-maize. Theor. Appl. Genet., 97: 13211330
Martinez-Espinoza, A.D., M.D. Garcia-Pedrajas and S.E. Gold, 2002. The Ustilaginales as plant pests and model systems. Fungal. Genet. Biol., 35: 120
Mueller, A.N., S. Ziemann, S. Treitschke, D. Assmann and G. Doehlemann, 2013. Compatibility in the Ustilago maydis-maize interaction requires inhibition of host cysteine proteases by the fungal effector Pit2. PLoS Pathog., 9: e1003177
Murray, S.C., W.L. Rooney, M.T. Hamblin, S.E. Mitchell and S. Kresovich, 2009. Sweet sorghum genetic diversity and association mapping for brix and height. Plant Genome, 2: 4862
Nei, M. and W.H. Li, 1979. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc. Natl. Acad. Sci. USA, 76: 52695273
Powell, W., M. Morgante, C. Andre, M. Hanafey, J. Vogel, S. Tingey and A. Rafalski, 1996. The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol. Breed., 2: 225238
Pritchard, J.K., M. Stephens and P. Donnelly, 2000. Inference of population structure using multilocus genotype data. Genetics, 155: 945959
Ren, X., Z. Zhu, H. Li, C. Duan and X. Wang, 2012. SSR Marker Development and Analysis of genetic diversity of Fusarium verticillioides isolated from maize in China. Sci. Agric. Sin., 45: 52 66
Rodriguez-Kessler, M., L. Baeza-Montanez, M.D. Garcia-Pedrajas, A. Tapia-Moreno, S. Gold, J.F. Jimenez-Bremont and J. Ruiz-Herrera, 2012. Isolation of UmRrm75, a gene involved in dimorphism and virulence of Ustilago maydis. Microbiol. Res., 167: 270282
Rohlf, F., 2002. NTSYS-pc: Numerical Taxonomy and Multivariate Analysis System, 2.1 edition. Department of Ecology and Evolution, State University of NY, Stony Brook, New York, USA
Scott, J.B. and S. Chakraborty, 2008. Identification of 11 polymorphic simple sequence repeat loci in the phytopathogenic fungus Fusarium pseudograminearum as a tool for genetic studies. Mol. Ecol. Resour., 8: 628630
Sonah, H., R.K. Deshmukh, A. Sharma, V.P. Singh, D.K. Gupta, R.N. Gacche, J.C. Rana, N.K. Singh and T.R. Sharma, 2011. Genome- wide distribution and organization of microsatellites in plants: an insight into marker development in Brachypodium. Plos One, 6: e21298
Valverde, M.E., O. Paredes-Lopez, J.K. Pataky and F. Guevara-Lara, 1995. Huitlacoche (Ustilago maydis) as a food source-biology, composition, and production. Crit. Rev. Food Sci. Nutr., 35: 191229
Vogelgsang, S., F. Widmer, E. Jenny and J. Enkerli, 2009. Characterisation of novel Fusarium graminearum microsatellite markers in different Fusarium species from various countries. Eur. J. Plant Pathol., 123: 477482
Wahl, R., A. Zahiri and J. Kamper, 2010. The Ustilago maydis b mating type locus controls hyphal proliferation and expression of secreted virulence factors in planta. Mol. Microbiol., 75: 208220
Weising, K., R.G. Atkinson and R.C. Gardner, 1995. Genomic fingerprinting by microsatellite-primed PCR: a critical evaluation. PCR Methods Appl., 4: 249255
Yeh, F.C., R.C. Yang, T.B.J. Boyle, Z.H. Ye and J.X. Mao, 1997. POPGENE, the User-friendly Shareware for Population Genetic Analysis. Molecular Biology and Biotechnology Centre. University of Alberta, Alberta, USA
Yu, J., G. Pressoir, W.H. Briggs, I. Vroh Bi, M. Yamasaki, J.F. Doebley, M.D. McMullen, B.S. Gaut, D.M. Nielsen, J.B. Holland, S. Kresovich and E.S. Buckler, 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet., 38: 203208
Zambino, P., J.V. Groth, L. Lukens, J.R. Garton and G. May, 1997. Variation at the b mating type locus of Ustilago maydis. Phytopathology, 87: 12331239
Zane, L., L. Bargelloni and T. Patarnello, 2002. Strategies for microsatellite isolation: a review. Mol. Ecol., 11: 116
Zhang, M., S. Yang, Y. Chen, J. Yuan and Q. Meng, 2013. Isolation of haploid strains of Ustilago maydis and identification of mating type loci a. Jiangsu Agric. Sci., 41: 122124
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| Publication: | International Journal of Agriculture and Biology |
|---|---|
| Article Type: | Report |
| Geographic Code: | 9CHIN |
| Date: | Apr 30, 2015 |
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