Introduction
Turcicum leaf blight (TLB), a significant foliar disease caused by Exserohilum turcicum (Pass.), can significantly reduce the yield of maize (Zea mays L.), the third-most important grain crop in the world (Leonard and Suggs, Reference Leonard and Suggs1974; Jakhar et al., Reference Jakhar, Singh, Devesh, Kumar, Singh and Srivastava2022). Grain yield can be reduced up to 80% when TLB becomes severe (Tefferi et al., Reference Tefferi, Hulluka and Welz1996). Long, elliptical, brown or greyish green leaf lesions that begin on the lower leaves and eventually spread throughout the foliage are the characteristic features of the disease. Blighted leaves die too soon if the disease is not treated in early stages (Jakhar et al., Reference Jakhar, Singh, Kumar, Singh and Ojha2017; Keerthana et al., Reference Keerthana, Haritha, Sudhir Kumar and Ramesh2023). Therefore, planting resistant cultivars is a widely accepted strategy that can reduce the rate at which diseases emerge (Keerthana et al., Reference Keerthana, Haritha, Sudhir Kumar and Ramesh2023). Disease resistance is classified as either qualitative or quantitative. Qualitative disease resistance is exclusive to a certain race, but quantitative or polygenic disease resistance is typically non-specific to a particular race and is more common in the Indian subcontinent (St. Clair, Reference St Clair2010). The initial study on E. turcicum in several Indian races was carried out by Payak and Sharma (Reference Payak and Sharma1985). Gowda et al. (Reference Gowda, Shetty, Gowda, Prakash and Sangam1993) found the four maize differentials, A-503HtN, H-4460Ht, H-4460Ht2 and H-4460Ht3, that include the numerous Ht resistance genes (Ht for Helminthosporium turcicum). A longer incubation period and fewer, usually smaller disease lesions are characteristics of quantitative TLB resistance, which is durable (Brewster et al., Reference Brewster, Carson and Wicks1992; Smith and Kinsey, Reference Smith and Kinsey1993; Abebe et al., Reference Abebe, Singburaudom, Sangchote and Sarobol2008; Gulzar et al., Reference Gulzar, Dar, Ahangar, Lone, Bhat, Kamal-ud-din, Khan, Sofi, Yousuf, Yousuf and Majid2018). Jakhar et al. (Reference Jakhar, Singh, Singh and Srivastava2021) screened 40 maize inbreds for the TLB resistance in Varanasi and Nagenahalli under artificially inoculated field conditions and observed that 10 inbreds viz., HKI-586, HUZM-53, CM-145, V-336, V-338, HKI-PC-8, HUZM-47, CM-104, CM-105 and CML-192 were resistant for the TLB disease. Similarly, Alemayehu et al. (Reference Alemayehu, Tajudin and Bayoush2018) also evaluated the disease reaction of maize lines against common leaf rust and TLB under field conditions with artificial inoculation. The development of resistance against TLB disease will have a significant effect on the maize breeding programmes.
The presence of distinct genetic groups among maize inbreds is characterized to increase gene diversity that is helpful in optimizing hybrid vigour. Using molecular markers to create diversity among maize inbred lines is a valuable tool (Singh and Srivastava, Reference Singh and Srivastava2017). Because it is highly polymorphic, repeatable and co-dominant in nature, the microsatellite marker is determined to be the most appropriate molecular marker among the many classes for screening TLB resistance (De Loose and Gheysen, Reference De Loose and Gheysen1995; Singh and Srivastava, Reference Singh and Srivastava2017; Keerthana et al., Reference Keerthana, Haritha, Sudhir Kumar and Ramesh2023). The microsatellite markers are useful in characterizing maize inbreds and in establishing distinct clusters based on genetic diversity and also useful in maize breeding programmes (Nikolic et al., Reference Nikolic, Ignjatovic-Micic, Kovacevic, Camdzija, Filipovic and Mladenovic-Drinic2015). Likewise, based on UPGMA clustering method, the inbred lines were categorized into various clusters and cluster A was solitary, and the inbred line VL171488-2 exhibited a resistant reaction against leaf blight (Keerthana et al., Reference Keerthana, Haritha, Sudhir Kumar and Ramesh2023). The simple sequence repeat (SSR) markers will allow detection of polymorphisms at the DNA level which will make it easier to distinguish among inbreds. Keerthana et al. (Reference Keerthana, Haritha, Sudhir Kumar and Ramesh2023) reported 26 SSR markers for the assessment of molecular diversity in maize inbred lines for resistance to TLB. Additionally, microsatellite markers have been used by many earlier researchers to analyse the quantitative traits, i.e. TLB resistance and to identify the quantitative trait loci (QTLs) for TLB resistance in maize (Jakhar et al., Reference Jakhar, Singh, Devesh, Kumar, Singh and Srivastava2022, Reference Jakhar, Singh and Singh2024). QTLs identification helps in marker-assisted selection process for improving genetic traits in crop plants (Singh and Srivastava, Reference Singh and Srivastava2017). SSR marker system could be used to evaluate TLB-resistant lines because they have a distinct molecular fingerprint compared to susceptible lines. Therefore, the present study was planned to study molecular diversity to identify polymorphic SSR markers for TLB resistance.
Materials and methods
Plant materials and layout
A set of 40 inbred lines including checks (V-336 and CM-145 as resistant check, and CM-212 as susceptible check) were obtained from DMR, New Delhi; VPKAS, Almora and Maize programme, BHU, Varanasi subjected to molecular divergence in maize (Table 1). A study on molecular characterization was carried out in Kharif 2015 at the Agricultural Research Farm, Institute of Agricultural Sciences, BHU, Varanasi (located at 25.18°N latitude, 83.03°E longitude and 123.23 m above sea level) using randomized block design with two replications. Each genotype consists of two rows spaced 70 cm apart and 25 cm between plants. Adopting recommended agronomic practices helped to produce a good crop (Mallikarjuna et al., Reference Mallikarjuna, Puttaramanaik, Kumar, Raveendra HR and Kumar2018). Additionally, at the same time, an investigation was carried out in 2015 to examine TLB reactions with a pure culture of E. turcicum in maize under artificial inoculation conditions at two different locations: Nagenahalli, Karnataka, and Varanasi, Uttar Pradesh (Jakhar et al., Reference Jakhar, Singh, Singh and Srivastava2021).
List of 40 maize inbreds used in this study and their pedigree/source germplasm details, major characteristics along with TLB disease reaction
HR, highly resistant; R, resistant; PR, partial resistant; PS, partial susceptible; S, susceptible; HS, highly susceptible.
DNA extraction and quantification
Total genomic DNA was isolated from 100 mg (21–24 days old) healthy leaf samples collected from individual plants of each inbred lines and stored in a deep freezer at −80°C. The DNA extraction was carried out by using the CTAB procedure described by Saghai-Maroof et al. (Reference Saghai-Maroof, Soliman, Jorgensen and Allard1984).
SSR marker assay
In the present study, a set of 75 SSR markers (Table 2) were evaluated for 40 maize inbred lines. These SSR markers were received from Applied Biotechnology Centre, CIMMYT, Mexico and the Asian Maize Biotechnology Network (AMBIONET). The primer details for the selected microsatellite markers were retrieved from the maizeGDB (Maize Genetics and Genomics Database) and are publicly accessible (https://www.maizegdb.org/).
List of microsatellite markers used for molecular divergence among TLB-resistant and -susceptible lines of maize
SSR fragment analysis
The polymerase chain reaction (PCR) amplicons of SSR markers were electrophoretically fractionated on a 2.5% agarose gel that was stained with ethidium bromide in 1x TAE buffer. The PCR amplicons were visualized under UV light and photographed using Alpha imager gel documentation system. The 100 bp DNA ladder was used as a molecular weight marker for the analysis of SSR. The distinct PCR amplified products were scored as ‘1’ for the presence and ‘0’ for the absence of DNA bands for each SSR marker genotype combination (Anderson et al., Reference Anderson, Churchill, Sutrique, Tanksley and Sorrells1993).
Data analysis
The informativeness of SSR markers in terms of polymorphism was determined by calculating polymorphic information content (PIC) for each SSR marker loci according to the formula (Anderson et al., Reference Anderson, Churchill, Sutrique, Tanksley and Sorrells1993):
where pij is the frequency of the jth microsatellite allele for locus i.
The DNA data of microsatellite primers for maize inbred lines were clustered by using the UPGMA with the module of DARwin 6.0 software. The principal coordinate analysis (PCoA) was estimated to depict the diverse origin of the genotypes by using DARwin 6.0 software (Perrier et al., Reference Perrier, Flori, Bonnot, Hamon, Seguin, Perrier and Glaszmann2003).
Results
Allelic diversity and PIC value
A survey of the molecular profiles generated by the evaluation of amplified products clearly indicated that altogether 212 alleles were detected with an average of 2.83 alleles per markers. Allelic diversity was observed with the number of alleles ranged from two to five. In this study, a direct correlation for the number of alleles and polymorphism information content (PIC) values was observed. For instance, marker phi123 produced high number of alleles (5) with PIC values of 0.77. Similarly other markers such as bnlg1812, umc1097, umc1027, dupssr1, umc1042, bnlg1136, bnlg1043, umc1051, bnlg1885, nc005, phi112, bnlg1138, bnlg1331 and bnlg1396 which also produced higher number of alleles (4) also exhibited high PIC values viz., 0.78, 0.80, 0.72, 0.73, 0.73, 0.74, 0.74, 0.73, 0.74, 0.75, 0.74, 0.75, 0.75 and 0.75, respectively (Table 3 and Fig. 1). Furthermore, SSR loci with tetra-nucleotide sequence motifs detected a greater number of alleles than the repeated loci with di-nucleotide, tri-nucleotide and complex sequence motifs.
Allelic distributions and PIC values of 75 microsatellite markers among 40 TLB-resistant and -susceptible lines
Radar chart for allelic distributions and PIC values of 75 microsatellite markers.
Genetic relationship among diverse genotypes
The DARwin 6.0 software was used to prepare an UPGMA dendrogram. The genetic distance was used to prepare the UPGMA dendrogram to group 40 maize inbred lines into two major clusters (cluster-I and cluster-II). Cluster-I contained two subclusters, the first subcluster contained 28 genotypes and the second contained eight genotypes whereas cluster-II contained four inbreds. Most of the inbreds except V-25 from cluster-II were highly resistant to TLB disease. This major cluster-II was further classified into two subclusters which contained two genotypes each (Fig. 2).
Dendrogram indicating genetic relationship among 40 maize inbred lines generated by using UPGMA method.
The PCoA revealed that the 40 maize inbred lines were dispersed into four quadrangles and signifying complex genetic relationship among themselves (Fig. 3). The results of the PCoA supported the UPGMA dendrogram's clustering pattern and nearly coincided with the findings of the cluster analysis.
Principal coordinate analysis (PCoA) among 40 maize inbreds using 75 SSR markers.
Discussion
The UPGMA dendrogram grouped 40 maize inbreds into two major clusters (cluster-I and cluster-II). Cluster-I contained two subclusters, the first subcluster contained 28 genotypes and the second contained eight genotypes whereas cluster II contained 4 inbreds. This major cluster II was further classified into two subclusters which contained two genotypes each. Therefore, TLB-resistant maize inbreds were unambiguously differentiated from susceptible and partially resistant maize inbred lines.
The 75 microsatellite marker loci differed in their ability to determine the variability among 40 TLB-resistant and -susceptible lines of maize based on their genetic polymorphism. The value of PIC of a marker reflects primer specific gene diversity, and frequency among the inbreds. The higher value of PIC of a SSR marker indicated a higher number of alleles among inbreds (Kumari et al., Reference Kumari, Kumar, Sharma and Kumar2018). A sum total of 212 alleles were found at 75 SSR loci with a mean of 2.83 alleles per SSR locus. The number of alleles ranged from two to five with an average of 2.83 alleles per markers. The average number of alleles in the present study is comparable with earlier genetic diversity analysis (Jaya Kumar, Reference Jaya Kumar2010), who reported 2–8 alleles per marker. Similar studies have also been conducted in maize (Yuan et al., Reference Yuan, Fu, Warburton, Li, Zhang, Khairallah, Liu, Peng and Li2000; Prasanna and Hoisington, Reference Prasanna and Hoisington2003; Choukan et al., Reference Choukan, Hossainzadeh, Ghannadha, Warburton, Talei and Mohammadi2006).
In this study, a positive correlation for the number of alleles and PIC values was found. For instance, marker phi123 produced high number of alleles (5) with PIC values of 0.77. Furthermore, SSR loci with tetra-nucleotide sequence motifs found a maximum number of alleles than the repeated loci with di-nucleotide, tri-nucleotide and complex sequence motifs in the present investigation. Generally, the di-nucleotide repeat motifs were found to be more polymorphic than those with tri-nucleotide, tetra-nucleotide and complex repeat motifs (Lapitan et al., Reference Lapitan, Brar, Abe and Redona2007; Kumar et al., Reference Kumar, Kumari and Sharma2018; Kumari et al., Reference Kumari, Kumar, Sharma and Kumar2018). The importance of SSR markers for the assessment of genetic diversity has been reported by earlier workers (Li et al., Reference Li, Du, Wang, Shi, Song and Jia2002; Xia et al., Reference Xia, Reif, Hoisington, Melchinger, Frish and Warburton2004; Yu et al., Reference Yu, Wang, Shi, Song, Wang and Li2007; Reid et al., Reference Reid, Xiang, Zhu, Baum and Molnar2011; Haasbroek et al., Reference Haasbroek, Craven, Barnes and Crampton2014) by using maize genotypes.
In the present investigation, 75 SSR markers revealed sufficiently high sensitivity for detecting DNA polymorphism among the 40 maize inbreds. The dendrogram revealed the genotypic relationship among each other to explore their utility for further studies.
The PCoA showed that the distributions of 40 maize inbreds in the four quadrangles were highly dispersed, signifying complex genetic relationship among themselves. In the present study, the results of PCoA also illustrated the diverse nature of the maize inbreds and agreed well with their cluster analysis pattern. The diverse nature of the maize inbreds has already been proven to be a good source for the development of superior heterotic hybrids by Solomon et al. (Reference Solomon, Martin and Zeppa2012), Babic et al. (Reference Babic, Srdic, Pajic, Grcic and Filipovic2014) and Mehta et al. (Reference Mehta, Hossain, Muthusamy, Baveja, Zunjare, Jha and Gupta2017). Further investigations are required to evaluate whether the discrimination between resistant and susceptible genotypes is consistent for other isolates of E. turcicum and during natural infection in different environmental conditions in multiple years.
Conclusions
In the present investigation, 75 microsatellite markers were used for the assessment of molecular divergence among 40 TLB-resistant and -susceptible lines of maize. Among these SSR markers, Phi123 produced a large number of alleles (5) with PIC values of 0.77. Similarly, other markers that produced a greater number of alleles, such as bnlg1812, umc1097, umc1027, dupssr1, umc1042, bnlg1136, bnlg1043, umc1051, bnlg1885, nc005, phi112, bnlg1138, bnlg1331 and bnlg1396, also showed a high PIC value. Furthermore, these markers observed a significant amount of variation in the resistant genotypes indicating their usefulness. In this study, most of the inbred lines from cluster-II were highly resistant to TLB disease. These TLB-resistant lines can be used as the divergent parents to develop TLB-resistant hybrids. Allelic diversity and PIC values in this investigation showed that the markers used were effective in determining the polymorphism level found in the studied inbred lines. Furthermore, the notable level of divergence detected by SSR markers can be used in crossing programmes to develop TLB-resistant hybrids. The findings of this study can also be used to design efficient breeding programmes for resistance to TLB.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S1479262124000315
Acknowledgements
The Institute of Agricultural Sciences at Banaras Hindu University, Varanasi is acknowledged by the authors for making this research possible. We would also like to thank the University Grants Commission (UGC), New Delhi for providing a research fellowship that allowed us to complete this study.
