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Genetic analysis of pigeonpea (Cajanus cajan L. Millsp.) genotypes for Fusarium wilt using microsatellite markers.

Pigeonpea [Cajanus cajan (L.) Millsp.] (2n = 22) is an important food legume crop of the semi-arid tropical regions of Africa and Asia. It belongs to the family Leguminosae and sub family Papilionaceae. The chromosome number of all Cajanus species is n=11, with a genome size of 808 Mbp (Greilhuber and Obermayer, 1998). India is the largest pigeonpea growing country, cultivating it on 36.3 lakh hectares with an annual production of around 27.6 lakh tonnes and productivity of 760.33 kg/ha. The productivity of this crop remained low at around 700-800 kg/ha over the last five decades. This is mainly due to limited exploitation of available natural variability of genus Cajanus in breeding lines. It is drought tolerant but highly susceptible to some abiotic (water-logging) and biotic (Fusarium wilt and sterility mosaic disease, etc.) stresses.

Fusarium wilt of pigeonpea is a soil borne disease caused by fungus Fusarium udum. Therefore, to minimize yield losses due to fusarium wilt, it is necessary to tackle these problems at molecular level by developing cultivars which resist/ tolerate these biotic stresses and have greater recovery from damage. Genomic tools especially molecular markers have facilitated breeding in many cereal crops leading to development of several improved cultivars/ varieties with enhanced resistance / tolerance to biotic or abiotic stresses (Varshney et al., 2006). Molecular markers, such as RAPD, SCAR, SSR, RFLP, AFLP etc., have been used to assess genetic variations at DNA level.

Microsatellites (Tautz and Rentz 1984), also known as Simple Sequence Repeat (SSR) markers, are DNA-based molecular markers that offer several advantages because they are reproducible, polymorphic, polymerase chain reaction (PCR)-based and readily portable within a species (Edwards et al. 1996). In case of pigeonpea, although a few SSR markers have been reported recently (Burns et al. 2001, Odeny et al. 2007), not a single genetic map is available so far. This can be attributed to mainly two factors: (i) availability of meager number of molecular markers and (ii) a very low level of polymorphism in cultivated pigeopea germplasm (Odeny et al. 2007). In present investigation studied twenty eight microsatellite markers for genetic analysis of genetic analysis of seventeen pigeonpea genotypes for fusarium wilt.

MATERIALS AND METHODS

Plant material

Total seventeen pigeonpea genotypes namely, GT-1, GT-100, GT-101, GT-102, BANAS, AVPP-1, AGT-2, T-15-15, LRG-41, C-11, BDN-2, ICPL-87, ICPL-87119, ICPL- 84060, ICP-8863, BSMR-853 and WRGE-119 were collected from Agricultural Research Station, Dahod and Pulse Research station, Vadodara, Anand Agricultural University, Anand and used in present study. Plants were grown in pots and young leaf samples of all plants of each cultivar were collected for genomic DNA isolation.

Genomic DNA isolation

Extraction of DNA from young leaves was done using modified Cetyl-Trimethyl Ammonium Bromide (CTAB) method (Murray and Thompson, 1980) with some minor modifications.

PCR amplification using microsatellite markers

Twenty eight microsatellite markers were selected from available literatures (Table 1) which were synthesized from MWG biotech, Germany. PCR was carried out in 25il reaction volumes containing 2.5 il of 10 x Taqassay buffer (Tris with15mM MgCl2), 10 mM of each dATP, dCTP, dGTP and dTTP, 5U Taq polymerase (BioLabs, UK), 10 picomole of forward - reverse primer and 50 ng of template DNA. Amplification were carried out in a thermo-cycler (Applied Biosystem Veriti, CA, USA) programmed for 35 cycles with an initial denaturation at 94[degrees]C for 5 min followed by cycling conditions of denaturation at 94[degrees]C for 45 second, annealing at 56[degrees]C for 45 second and extension at 72[degrees]C for 1 min. After 35 cycles, there was a final extension step of 7 min at 72[degrees]C. The amplicons were analyzed on 2.5 % agarose gels and detected by staining with ethidium bromide. UV transilluminated gels were photographed with gel documentation system (SYNGENE, USA).

Data collection and analysis

The clear and distinct bands amplified by microsatellite markers were scored for the presence and absence of the corresponding band among the genotypes. The scores 1 and 0 indicates the presence or absence of bands respectively. The software used for the analysis of the scored data was NTSYSpc version 2.02 (Rholf 1994). The molecular weight of the PCR products was estimated by Alpha EaseFC4.0.0 software (Alpha Innotech Corporation, USA) for each primer to analyze allele range and cluster analysis was performed by agglomerative technique using the UPGMA (Un-weighted Pair Group Method with Arithmetic Mean) method by SAHN clustering function of NTSYSpc.

RESULTS AND DISCUSSION

The microsatellite markers which are more effective comparable to RAPD markers due to their co-dominant nature, also as it could distinguish the resistant and susceptible genotypes. The data collected from 28 microsatellite markers produced 88 alleles. The average number of alleles per locus was found to be 3.14. The effective number of alleles was 2.39. The maximum number of alleles was six which were recorded for markers CCttc005 and CCttc008 followed by Ccat006, CCB2, CCB4, CCB6, CCB9, CCtta006, and CCttc007 which produced four alleles. Ccac010, CCac026, CCB7, CCB10, CCttc002, CCttc006, CCttc012, CCttc033, ICPM1A08, ICPM1G01, ICPM1G04 and ICPMTC20 produced three alleles. Whereas CCB1, CCB8, CCtc013 and ICPM2BM08 produced two alleles each and CCgtt002, ICPM1E10 produced single allele which was the lowest in the present investigation. The highest allele frequency found by CCB1 marker was 0.813 which was similar to Songok et al., (2010). The highest PIC value was recorded for CCB10 (0.68), CCttc005 (0.68) which was similar to Saxena et al., (2010) and the lowest for CCtc013 (0.11). The average PIC value and number of alleles were 0.60 and 4.8 for the markers developed by Odeny et al., (2007) while 0.49 and 3.14 for markers used in present study. The molecular weight of the amplified PCR products ranged from 110bp (ICPM1G04) to 295bp (CCtc013) (Table 1).

Based on the microsatellite markers data, cluster analysis was performed using genetic similarity values and a dendrogram was generated showing the grouping of genotypes according to their resistance and susceptibility to fusarium wilt. The highest similarity index value of 0.78 was found between GT-1 and GT-100 in susceptible genotypes, whereas in resistant genotypes the highest similarity index value, 0.77 was found between ICPL-87 and ICPl-87119 while the least similarity index value of 0.05 was found between GT-100 and ICPL-87119. The average similarity coefficient among genotypes was 0.49 (Table 2).

The microsatellite markers cluster pattern is presented Figure 1. It showed two major clusters namely A and B formed at a similarity coefficient of 0.13 (Fig. 1). Cluster A was divided into two sub clusters A1 and A2. Grouping of nine genotypes that are susceptible to fusarium wilt were in one major cluster 'A'. Sub-cluster A1 included pigeonpea genotypes viz., GT-1, GT-100 and GT-101. Sub-cluster A2 consists of genotypes GT-102, BANAS, AVPP-1, AGT-2, T-15-15 and LRG-41. Cluster B was divided into two sub-clusters B1 and B2. Grouping of eight genotypes that are resistant to fusarium wilt were in one major cluster 'B'. Sub-cluster B1 consists of genotypes viz., C-11, BDN-2, ICPL-87 and ICPL-87119; whereas subcluster B2 consists of genotypes viz., ICPL-84060, ICP-8863, BSMR-853 and WRGE-119.

The dendrogram constructed using UPGMA the pooled SSR loci data shows that fusarium wilt susceptible pigeonpea genotypes viz., GT-1, GT-100, GT-101, GT-102, BANAS, AVPP1, AGT-2, T-15-15 and LRG-41 are closely related. It also revealed thatfusarium wilt resistant genotypes of pigeonpea viz., C-11, BDN-2, ICPL-87, ICPL-87119, ICPL-84060, ICP-8863, BSMR-853 and WRGE-119 are closely related.

CONCLUSION

Microsatellite markers attained great significance in characterization of plant germplasm resources. They were used to distinguish the 17 pigeonpea genotypes according to their resistance and susceptibility to fusarium wilt. Dendogram based on microsatellite markers data showed the clustering of genotypes according to their reaction to fusarium wilt. The genetic similarity among the susceptible and the resistant genotypes was expressed more clearly using these markers. The study also revealed that from the tendency of resistant and susceptible genotypes to cluster together, it can be inferred that these genotypes share a common phylogenetic pathway and the resistancy and susceptibility to fusarium wilt may be due to mono or oligogenes which can in turn to be targeted using more efficient marker (molecular) systems.

ACKNOWLEDGEMENTS

We thank Retd. Head and Professor Dr. J. C. Jadeja, Department of Genetics and Plant Breeding, B. A. College of Agriculture for providing the research facility as well as encouragement during these study.

REFERENCES

(1.) Burns, M.J., Edward, K.J., Newbury, H.J., FordLloyd, B.V and Baggott, C.D. Development of simple sequence repeat (SSR) marker for assessment of gene flow and genetic diversity in pigeonpea (Cajanus cajan). Mol. Ecol. Notes, 2001; 1: 283-285.

(2.) Edwards, K.J., Barker, J.H.A, Daly, A., Jones, C. and Karp, A. Microsatellite libraries enriched for several microsatellite sequences in plants. Biotechniques, 1996; 20: 758-759.

(3.) Greilhuber, J. and Obermayer, R. Genome size variation in Cajanus cajan (Fabaceae): A reconsideration. Plant Syst. Evol., 1998; 212: 135-141.

(4.) Murray, M.G. and Thompson, W.F. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res., 1980; 8: 4321-4325.

(5.) Odeny, A.D., Jayshree, B., Ferguson, M., Hoisinton, D., Crouch, J., and Gebhardt, C. Development, characterization and utilization of microsatellite markers in pigeonpea. Plant Breeding, 2007; 126: 130-136.

(6.) Rohlf, F.J. NTSYS-PC Numerical Taxonomy and Multivariate Analysis System, ver. 2.02. State University of New York, Stony brook, New York 1994.

(7.) Saxena, R.K., Saxena, K.B., Kumar, R.V, Hoisington, D.A. and Varshney, R.K. Simple sequence repeat-based diversity in pigeonpea genotypes for developing mapping population to map fusarium wilt and sterile mosaic disease. Plant Breeding, 2010; 129: 135-141.

(8.) Songok, S., Ferguson, M., Muigai, A.W. and Silim, S. Genetic diversity in pigeonpea [Cajanus cajan (L) Millsp.] Landraces as revealed by simple sequence repeat markers. African Journal of Biotechnology, 2010; 9(22): 3231-3241.

(9.) Tautz, D. and Renz, M. Simple sequences are ubiquitous repetitive components of eukaryotic genomes. Nucleic Acids Res., 1984; 12, 4127-38.

(10.) Varshney, R.K., Hoisington, D.A. and Tyagi, A.K. Advance in cereal genomics and applications in crop breeding. Trends Biotechnology, 2006; 24: 490-499.

Vijay Prajapati*, N. Sasidharan, Nishit Soni, Ankita Patel and Pravin Prajapat

Department of Genetics and Plant Breeding, B. A. College of Agriculture, Anand Agricultural University, Anand, Gujarat, India.

(Received: 05 February 2016; accepted: 08 March 2016)

* To whom all correspondence should be addressed. E-mail: vijay_microlife105@yahoo.com

Caption: Fig. 1. Dendrogram constructed using UPGMA cluster analysis for microsatellite markers.

Caption: Fig. 2. Amplification of microsatellite markers CCB10

Caption: Fig. 3. Amplification of microsatellite markers CCB9
Table 1. Results of microsatellite markers analysis

S.       Locus name   Repeat Motif                  No. of bands
No                                                  amplified

1        Ccac010      [(CA).sub.7]aca[(TA).sub.3]   17
2        CCac026      (AC)7                         15
3        CCac036      (CATA)3ta(TG)6                11
4        Ccat006      (TA)7(CA)6                    15
5        CCB1         (CA)10                        16
6        CCB2         (CA)21                        12
7        CCB4         (CA)31                        16
8        CCB6         (CA)6                         17
9        CCB7         (CT)16                        16
10       CCB8         (CT)30                        15
11       CCB9         (CT)22                        16
12       CCB10        (CA)15                        17
13       CCgtt002     (TGT)4                        17
14       CCtc013      (TC)6                         17
15       CCtta006     (ATT)21                       16
16       CCttc002     (GAA)5g(GAA)5                 13
17       CCttc005     (CA)8                         17
18       CCttc006     (GAA)11gag(GAA)5              14
                        gaggaagag(GAA) 17
19       CCttc007     (GA)4ca(GA)4                  16
                        cagagt(GA)8
20       CCttc008     (AC)7                         16
21       CCttc012     (TTC)7                        13
22       CCttc033     (CTT)8                        17
23       ICPM1A08     (CA)6                         15
24       ICPM1E10     (CA)7                         17
25       ICPM1G01     (CA)8                         17
26       ICPM1G04     (T)21                         17
27       ICPM2BM08    (TG)5n(TG)5                   15
28       ICPMCT20     (GA)14(AAGA)5                 17
Total                                               437
Avg.                                                15.60

S.       Molecular Size   Total no.   PIC
No       rang (bp)        Alleles

1        175bp-192bp      3           0.62
2        245bp-260bp      3           0.62
3        212bp-221bp      2           0.42
4        220bp-290bp      4           0.64
5        197bp-205bp      2           0.21
6        135bp-168bp      4           0.29
7        235bp-265bp      4           0.61
8        178bp-195bp      4           0.58
9        152bp-170bp      3           0.59
10       128bp-135bp      2           0.29
11       152bp-180bp      4           0.65
12       225bp-240bp      3           0.68
13       212bp            1           0
14       285bp-295bp      2           0.11
15       220bp-246bp      4           0.53
16       165bp-180bp      3           0.50
17       265bp-290bp      6           0.68
18       160bp-172bp      3           0.45
19       235bp-265bp      4           0.57
20       225bp-290bp      6           0.73
21       165bp-175bp      3           0.65
22       210bp-232bp      3           0.58
23       250bp-265bp      3           0.63
24       165bp            1           0
25       270bp-286bp      3           0.49
26       110bp-120bp      3           0.66
27       130bp-140bp      2           0.48
28       120bp-140bp      3           0.65
Total    -                88          13.91
Avg.     197bp-218bp      3.14        0.49

Table 2. Genetic similarity matrix of microsatellite markers data
based on Jacard's similarity coefficient

              GT-1   GT-    GT-    GT-    BANAS   AVPP-
                     100    101    102              1
GT-1          1.00
GT-100        0.78   1.00
GT-101        0.48   0.51   1.00
GT-102        0.40   0.43   0.58   1.00
BANAS         0.45   0.44   0.41   0.75   1.00
AVPP-1        0.34   0.37   0.45   0.63   0.65    1.00
AGT-2         0.35   0.38   0.38   0.47   0.57    0.71
T-15-15       0.28   0.25   0.25   0.39   0.52    0.52
LRG-41        0.21   0.22   0.21   0.32   0.33    0.48
C-11          0.0    0.07   0.06   0.10   0.12    0.16
BDN-2         0.08   0.08   0.11   0.10   0.10    0.12
ICPL-87       0.06   0.05   0.08   0.12   0.13    0.10
ICPL-87119    0.06   0.05   0.08   0.12   0.12    0.09
ICPL-84060    0.09   0.08   0.11   0.13   0.13    0.10
ICP-8863      0.11   0.10   0.09   0.10   0.11    0.10
BSMR-853      0.11   0.10   0.09   0.10   0.11    0.10
WRGE-119      0.11   0.10   0.08   0.06   0.08    0.05

              AGT-   T-15-15   LRG-   C-11   BDN-   ICPL-
               2       15       41            2      87
GT-1
GT-100
GT-101
GT-102
BANAS
AVPP-1
AGT-2         1.00
T-15-15       0.54    1.00
LRG-41        0.38    0.58     1.00
C-11          0.17    0.30     0.34   1.00
BDN-2         0317    0.20     0.25   0.61   1.00
ICPL-87       0.10    0.25     0.28   0.61   0.68   1.00
ICPL-87119    0.12    0.22     0.25   0.55   0.61   0.77
ICPL-84060    0.15    0.21     0.24   0.40   0.54   0.59
ICP-8863      0.13    0.18     0.20   0.47   0.48   0.57
BSMR-853      0.13    0.20     0.23   0.43   0.52   0.52
WRGE-119      0.14    0.17     0.17   0.41   0.50   0.38

              ICPL-   ICPL-   ICP-   BSMR-   WRGE
              87119   84060   8863    853    119
GT-1
GT-100
GT-101
GT-102
BANAS
AVPP-1
AGT-2
T-15-15
LRG-41
C-11
BDN-2
ICPL-87
ICPL-87119    1.00
ICPL-84060    0.62    1.00
ICP-8863      0.60    0.58    1.00
BSMR-853      0.65    0.63    0.66   1.00
WRGE-119      0.44    0.54    0.52   0.62    1.00
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Author:Prajapati, Vijay; Sasidharan, N.; Soni, Nishit; Patel, Ankita; Prajapat, Pravin
Publication:Journal of Pure and Applied Microbiology
Article Type:Report
Date:Jun 1, 2016
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