Acta Botanica 1-2017 - za web.indd ACTA BOT. CROAT. 76 (1), 2017 55 Acta Bot. Croat. 76 (1), 55–63, 2017 CODEN: ABCRA 25 DOI: 10.1515/botcro-2016-0042 ISSN 0365-0588 eISSN 1847-8476 Genetic analysis of microsatellite markers for salt stress in two contrasting maize parental lines and their RIL population Ayşen Yumurtaci1*, Hülya Sipahi2, Li Zhao3 1 Marmara University, Faculty of Science and Letters, Department of Biology, 34722, Istanbul, Turkey 2 Sinop University, Faculty of the Arts and Sciences, Department of Biology, 57000, Sinop, Turkey 3 The State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Abstract – Salt stress considerably hinders the growth and productivity of maize (Zea mays L.). Identifi ca- tion of salt tolerant genotypes and integration of alternative molecular markers have important roles in en- hancing breeding processes. In this study, 3308 maize expressed sequence tags (ESTs) from salt stress-related libraries were assembled to mine repetitive sequences for development of applicable markers. In this core EST data, 208 simple and 18 non-simple repetitive regions were detected in 312 contigs and 1121 singletons. The di-nucleotide repeats were the most abundant type and accounted for 79.3%, followed by tri (19.7%), and tetra-nucleotide (1%). Among 59 EST-simple sequence repeats (SSRs), a total of 55 were screened for polymorphism between F35 (salt sensitive) and F63 (salt tolerant) parents and 48 out of 55 were detected as monomorphic. Signifi cantly, seven of them (12.7%) were found to be polymorphic and were used for geno- typing of 158 F5 derived recombinant inbred maize lines, and four of them were located on chromosome 1 and 3. Using in silico mapping, 44 out of 59 EST-SSR markers were mapped on 10 maize chromosomes. Analysis of sequence homology revealed different functional groups such as: membrane transport, cell de- fense, cell division, signaling components, photosynthesis and cell metabolism. These EST-SSRs might be used as new functional molecular markers in the diversity analysis, identifi cation of quantitative trait loci (QTLs) and comparative genomic studies in maize in the future. Keywords: EST, maize molecular markers, salinity, SSR * Corresponding author, e-mail: yumurtaciay@yahoo.com Introduction Soil salinization is an important external stress factor for plants except halophytes, and has proved to be a serious drawback for plant growth from seed germination to adult plant stage (Singh 2015). Nearly 50% of the world’s territo- rial areas may be under pressure from salinity in the next forty years, due to the rapid shifts that are occurring in global weather conditions (Bray et al. 2000). Therefore, im- provement of salt tolerance in plants has gained importance because of the need to provide suffi cient food for the world’s increasing population (Bita and Gerats 2013, Yumurtaci 2015). Maize (Zea mays L.) is the third most cultivated crop after wheat and provides many commercial benefi ts for clean energy production, food and livestock feed (Klopfen- stein et al. 2013). Since detrimental declines in yield and agro-morphological traits have seen under saline condi- tions, maize is accepted as a salt sensitive crop (Schubert et al. 2001). In this perspective, investigation of the genetic control and identifi cation of genome regions associated with salt tolerance are of great signifi cance. Implementation of various DNA markers and quantita- tive trait loci (QTL) mapping techniques have contributed to an improved knowledge of the genetic bases of agricul- turally significant traits and assisted the progress of plant breeding (Lee 2007, Xue et al. 2010). The development and mapping of DNA markers are essential for using QTL map- ping of salt tolerance and marker-assisted selection of this trait in maize genotypes. Maize is one of the fi rst major crop species to have had maps of different molecular mark- ers (Davis et al. 1999, Sharopova et al. 2002, Sibov et al. 2003, Lima et al. 2009, Xu et al. 2013, Zhou et al. 2011). Recently, research has focused on genetic dissection in maize using QTL mapping of recombinant inbred lines YUMURTACI A., SİPAHİ H., ZHAO L. 56 ACTA BOT. CROAT. 76 (1), 2017 (RILs) derived from a cross between two maize inbred lines with contrasting salinity tolerance. A high density genetic map based on single nucleotide polymorphism (SNP) mark- ers identifi ed 20 QTLs on seven maize chromosomes (Cui et al. 2015). Signifi cantly, three QTLs had only additive ef- fects, while 12 had both additive and additive x treatment interaction effects. These QTLs were mainly clustered on maize chromosomes 1, 3 and 5. The fi ve unconditional and three conditional QTLs could individually explain more than 20% of the phenotypic variation. Xiang et al. (2015) identifi ed six sequence-related amplified polymorphism (SRAP) markers linked to salt tolerance using bulk segre- gant analysis of DNA pools from two salt-tolerant and salt- sensitive maize genotypes. Compared with other types of molecular markers, sim- ple sequence repeats (SSRs) have many advantages be- cause of their multi-allelic nature and co-dominant inheri- tance, as well as simple and inexpensive developmental methodology. Expressed sequence tag SSRs (EST-SSRs) are derived from expressed genome parts, which are more evolutionarily conserved than non-coding sequences and therefore have transferability. Also, EST-SSRs have close associations with gene/QTL regions that offer a practical strategy for molecular plant breeding. Thus, EST mining can be accepted as a reconstruction of genome-scale analy- ses. Many maize ESTs contained simple sequence repeats and could be readily converted to functional markers (Lee 2007). In recent years, a number of maize EST-SSRs have been developed (Xu et al. 2013) and applied to construct linkage maps (Sharopova et al. 2002, Zhou et al. 2011, Or- sini et al. 2012, Sa et al. 2012), QTL mapping and marker- assisted breeding (Orsini et al. 2012, Cui et al. 2015). In addition, Banisetti et al. (2012) identifi ed candidate gene- based SSR markers for lysine, tryptophan pathway and opaque2 modifi ers. A total of twenty-four SSR loci were developed and found to be useful markers for fi ne mapping and high-density mapping of opaque2 modifi ers. Despite an increasing number of DNA markers, the identifi cation of comprehensive markers for screening of salinity specifi c re- gions in maize is lagging behind and needs to be further developed. In the present study, motif structures and density esti- mations of SSR regions were identifi ed through in silico analysis of publicly available maize EST libraries which were constructed under salt stress at seedling stage. A set of primer pairs fl anking repetitive regions was developed from these analyses and validated using two contrasting parental genotypes and their recombinant inbred lines (F35-salt sen- sitive and F63-salt tolerant) for the genetic basis of salt tol- erance. Also, relative in silico map positions were defi ned and functional annotations of these SSRs were carried out to detect the functional marker source for maize. Materials and methods Plant material Maize parental lines F63 (salt tolerant) and F35 (salt sensitive) and their segregating population of 158 F5 RILs were used in this study. These materials were previously developed by Cui et al. (2015) in the State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China. SSR detection Two publicly available cDNA libraries, constructed by Wang/Bohnert lab under salinity stress in maize B73 line, were used; library ZM14 from root and library ZM13 from shoot tissue. This study was performed at four main compu- tational steps. At fi rst, a total of 3308 EST sequences were downloaded in fasta format from NCBI GenBank (http:// www.ncbı.nlm.nıh.gov). For scanning vector contaminated sites in ESTs, NCBI VecScreen module and DNADragon (http://www.sequentix.de/download/dnadragon.zip) were used. After trimming of contaminated sites, the DNADrag- on software “sequence assemble” module was used to clus- ter all ESTs into condensed non-redundant groups. Non- clustered (singletons) and clustered ESTs (contigs) were analyzed on eTRA 1.0 software, developed by Bilgen et al. (2004) and Karaca et al. (2005), under the following param- eters: up and down fi ltering was selected as 5%, minimum and maximum motif length was 2 and 10, respectively. Re- peat index and repeat percentages were automatically cal- culated on eTRA 1.0 software. PCR conditions Genomic DNA was extracted from young leaves of each RIL using cetyltrimethyl ammonium bromide (CTAB) method (Doyle and Doyle 1987). Polymerase chain reac- tion (PCR) was conducted in 15 μl volume containing 5 ng of genomic DNA template, fi nal concentrations of 0.25 pM of both forward and reverse primers, 1.5 mM of MgCl2, 0.2 mM of dNTPs, 1× PCR buffer, and 0.1 U of Taq polymerase enzymes. The cycling conditions for PCR were used to an initial denaturation of 2 min at 95 °C, followed by 33 cycles of 94 °C for 30 sec, annealing temperatures at 59 °C (for ZM13-AI977889, ZM14-Contig119), 56 °C (for the re- maining primers) for 30 sec, and extension at 72 °C for 30 sec. This was followed by fi nal extension of 7 min at 72 °C. PCR products were separated electrophoretically on poly- acrylamide gels (12%) for detailed fragment separation and results were scored manually. A total of 55 primers were generated and used in the validation studies. Data analysis and linkage map construction Salt tolerant (F63) and sensitive (F35) parents were screened with newly developed markers. Subsequently, polymorphic ones were also tested in a segregating popula- tion of 158 F5 RILs derived from a cross between F63 and F35. Chi-square analysis was performed at a signifi cance threshold of 5% in order to detect deviations from the ex- pected Mendelian segregation ratio of 1:1. Linkage analysis was performed using the program QTL IciMapping 3.3 (Li et al. 2007, 2008). The linkage groups were created with LOD threshold of 3.0. Map distances (centimorgan) were calculated according to the Kosambi mapping function (Kosambi 1943). GENETIC ANALYSIS OF MICROSATELLITE MARKERS IN MAIZE ACTA BOT. CROAT. 76 (1), 2017 57 Chromosomal localization and annotation studies of SSRs For nucleotide-protein similarities in contig and single- ton groups, Blastx (http://www.ncbi.nlm.nih.gov) search was retained for the best hit match score with the following criteria; E-value<10–5 and 90% accepted as for minimum identity. EST-based three hundred and twenty-six SSR con- tained sequences were aligned with several linkage maps of maize genomes, using e-PCR module (Rotmistrovsky et al. 2004) under default stringency parameters (http://www. ncbi.nlm.nih.gov/projects/e-pcr/). An in silico consensus map was drawn on MapChart 2.0 software according to Voorrips (2002). Results Based on the in silico analysis, 3308 ESTs consisting of 477119 base pairs were analyzed to fi nd SSR loci. Redun- dancy profi les revealed 312 contigs and 1121 singletons. Average lengths of contig sequences were 714 bp and 632 bp for root and shoot tissues, respectively, but these values decreased to 483bp and 373bp for singleton sequences in root and shoot tissue. Among contig sequences, repeat in- dex was 0.314 and 0.266 for shoot and root tissue, respec- tively. Additionally, singleton sequences had lower repeat indices for shoot (0.158) and root (0.156) tissues. Overall statistics for all clustered sequences indicated a repeat in- dex as low as 0.186. Here, 226 repeats, 208 simple and 18 non-simple, were detected in a total of 1433 contigs and singletons. Di-nucle- otide repeats were the most abundant type and accounted for 79.3%, followed by tri- (19.7%), and tetra-nucleotide (1%). The vast majority of Class I type dinucleotide repeats were found in shoot tissue, in the percentage of 84.9% and root tissue specifi c Class I type trinucleotide repeats (30.1%) marked as a higher level than shoot (13.5%) (Tab. 1). Among di-nucleotide motifs, AA (18.75%) and TT (13%) had the highest frequency, while GG had the lowest frequency, 7.2%. Thirty different tri-nucleotide motif re- peats were identifi ed. The most frequent tri-nucleotide mo- tif was CCG (7.3%). Some tri-nucleotide repeats (CTC, CTG, CTT, TTA) that are responsible for leucine synthesis were found only in root tissue-specifi c sequences. Only two tetra-nucleotide motifs (ATAA, ACCA) specifi c to shoot tissue were identifi ed. A total of 55 primer pairs were de- signed from 59 EST-SSR containing regions (Tabs. 2, 3). Of the 55 tested maize EST-SSRs, 48 generated amplifi ca- tion of monomorphic products and 7 others (12.7%) pro- duced clear and consistent polymorphic banding patterns between the parental lines F35 and F63. These seven poly- morphic markers (ZM14-Contig119, ZM13-AI964577, ZM13 -AI977889, ZM13-Contig37, ZM13-Contig83, ZM14-AI 855336, ZM13-AI966933) were also genotyped across the 158 individuals of the RIL population; four of them were assigned to two maize chromosomes. The loci ZM14Con- tig119 and ZM13AI964577 were located on the same Tab. 1. Distribution of root and shoot tissue specifi c expressed sequence tag (EST) based simple sequence repeats (SSR) and sequence tagged site (STS) marker densities and number of ClassI and ClassII type repeats. EST Shoot tissue Root tissue Number of contigs Number of singletons Number of contigs Number of singletons Total 188 647 124 474 Only SSR included 46 90 25 65 Only STS included 40 66 32 69 Both (SSR + STS) 14 18 9 18 ClassI (di/tri/tetra repeats) 11/2/2 4/2/– ClassII (di/tri/tetra repeats) 95/15/– 54/23/– Tab. 2. Detailed blastx similarity matches and motifs with repeat types of both simple sequence repeats (SSR) and sequence tagged site STS contained sequences in maize shoot tissues. “P” indicates perfect repeats, “IP” imperfect repeats and “*” shows unmapped markers. ZM13 is the library name. Sequence ID Motifs/Repeat Type Organism Protein E-value Primer pairs (5’-3’) Product size (bp) ZM13-Contig13 (TA)6/ P Z. mays Glyceraldehyde-3- dehydrogenase 2e-91 CAGATTATTCGACGAAAGAGA GCATGGTTGATGAAACAAATA 148 ZM13-Contig19 (GAT)6/P Z. mays Hypothetical protein 8e-36 CAACCAAGTCCTAAATTGTCA TTAAGCAGAGCTCAAAAACTG 153 ZM13-Contig33* (CA)5,(GT)5/P- (GT)3,(TA)5IP Z. mays Jacalin like lectin protein 3e-100 AGGTTCGACGAGAGCTTGAC CGTTTTGTTCGTGCTGAAGA 800 ZM13-Contig37 (TC) 10/P Z. mays Phospholipid transfer protein precursor 1e-38 AATAAACCAAACAGCCAAAA AATAAAATCTCTCCGTGTGTG 163 ZM13-Contig 60* (TA)6/P Z. mays Unknown protein 1e-11 GGAGGAACAGAGGTTTGTTAT ACTTTCAAGGTGGTGGAAG 335 YUMURTACI A., SİPAHİ H., ZHAO L. 58 ACTA BOT. CROAT. 76 (1), 2017 Tab. 2. – continued Sequence ID Motifs/Repeat Type Organism Protein E-value Primer pairs (5’-3’) Product size (bp) ZM13-Contig63 (GCG)6/P Z. mays Unknown protein 3e-43 TTTGATACACGGAGGAAGATA AACCAGAGATTCATCAGAGTG 427 ZM13-Contig65 (GA)6,(GCAG)4/IP Z. mays Hypothetical protein 5e-96 GTTCCTTCACACAGACACAGT CTTTTACATGGTGGACGAC 508 ZM13-Contig72 (GC)6/P Z. mays Unknown protein 3e-19 AACAACTGGCTGCATCTATAA ACAGAAGCAGAACCAGCA 504 ZM13-Contig83 (TA)5/ P Z. mays Nonspecifi c lipid transfer protein 8e-04 GCAGGCCACACATACATAATA GACCAAGCTAATAAGCCTACC 169 ZM13-Contig99* (TA)6/ P Z. mays Unknown protein 2e-29 GCCATAGCAAAAGCTCAAATCC CTCCCGATCAGCCGACTCTAC 482 ZM13-Contig111* (GAG)5,(ACG)4/IP Z. mays Chlorophyll ab binding protein 1e-107 AGTATCCCCTTTTTACACTGG CGTCGTCGTCGTTGTT 941 ZM13-Contig136 (AA)6/ P Z. mays Putative Glutation peroxidase 2e-97 CATACAGAAAGGGCGAAACA ACACGCTTCAAGGCTGAGTA 501 ZM13-Contig148 (ACCA)5/ P Z. mays Putative Protein kinase 1e-18 AAATATACGGCCCCAAGAAAA CAAGCAAGATCGGTGGAAAC 327 ZM13-Contig175 (CCA)7/ P-(TA)5/ P Z. mays Unknown protein 5e-46 ATGAGTACAGCGTCGGAGT ATGGATGGGATACAAATACAA 900 ZM13-AI649785* (CA)5/ P Z. mays Unknown protein 9e-33 GAAACAAGAAAGCAGGAACTC GCGCTACAACTCCAACAC 552 ZM13-AI649789 (TT)10/ P Z. mays Penta tricopeptid repeat 1e-89 GCCACTCTGTTTGGTGGATAG CGGCCTTGTTAATTACCTTGA 537 ZM13-AI649800* (AC)7,(CA)3/ P Z. mays Transcription factor 5e-32 CTAAGCACAAATTCAAGAAC TAGATAGCCTCGATCACTC 406 ZM13-AI964452 (CT)8,(AA)5/ P Z. mays Hypothetical protein 2e-37 GAAGTTGCAGTTGTTGTTTTT TAGGCAAGGTGTTATTACTGG 207 ZM13-AI964488 (AT)5,(TA)4/IP Z. mays Carbonic anhydrase 4e-38 CTCAAGACCTACCCCTTCGTC ACTCCTCGCATTCACATATCG 215 ZM13-AI964426 (CT)5,(CA)5/ P Z. mays Phospholipide transfer protein 1e-13 TAAATAAAGACCGCATGAACT AAGCACTTGCCATCTACC 399 ZM13-AI964559* (AC)3,(AA)5/IP Z. mays NADH ubiquinon oxidoreductase 6e-59 GAGAGGGGTATTTGCTGTATT CCTACTCTGTTGTACGTGGAT 397 ZM13-AI964577 (CT)13/ P G. max Anaphase entrance complex 0.44 CCACAAAGGCATCAAAAGAAT ATTGTCCGCCTTAACCAGAAG 356 ZM13-AI977807* (CG)5/ P Z. mays Hypothetical protein 7e-108 CTCATCATCTCGTTGTCCTC GACGCACCTGTACCTGAA 453 ZM13-AI977889 (GAGAAGG)4, (GG)3,(GA)5/IP T. versicolor Unknown protein 8.2 CGCCAATGGTGAGTGAAAGAA GCAAACGACACCTCCAACCAC 281 ZM13-AI977895 (TA)5/ P Z. mays Nucleotide sugar transporter 3e-32 GCTGCACATGGAAACACTTCA ACGCAGGTGGCGTTAGAAAGG 463 ZM13-AI977907 (CGT)5/ P Z. mays Hypothetical protein 3e-09 ATCTCGATCTGCTTCATCAT GTAACCTCAACGTCAGCTTG 253 ZM13-AI977935 (GCT)5/ P No similarity – – CAAACAAAAGAGACATCAGGA CCCAAGCATTCAAAACTACTA 303 ZM13-AI966834 (AA)5/ P Z. mays 40S ribosomal protein 2e-48 TCCATTTTGCTCTGTTTCTT AGCCTTCTTCTGCCACAG 285 ZM13-AI966924* (TT)8/ P Z. mays Phosphoglycomutase 1e-35 ATCCGATACCTCTTCGGAAAT CAAAGGCTCAAACAAAACCAG 505 ZM13-AI966933 (AATA)5/P-(GGT)5/ IP,(CGG)3/IP Z. mays Unknown protein 2e-21 ATTTGAGAACGGAAGCAAGTA ACCACCACCACCGACAAG 268 ZM13-AI966957* (AA)5,(AG)5/ P Z. mays Unknown protein 5e-30 CATCCACCGTGTTGTGTGGAA GTGGTTATGGCAGTCGGCAAC 281 ZM13-AI967009* (GC)5/ P Z. mays Alcohol dehydroge- nase 1e-56 TCAACTGCGTGTCCCCCTATG TGGAATGTTCAATCATCTGTTGG 400 GENETIC ANALYSIS OF MICROSATELLITE MARKERS IN MAIZE ACTA BOT. CROAT. 76 (1), 2017 59 Tab. 3. Detailed blastx similarity matches and motifs with repeat types of both simple sequence repeats (SSR) and sequence tagged site STS contained sequences in maize root tissues. “P” indicates perfect repeats, “IP” imperfect repeats and “*” shows unmapped markers. ZM14 is the library name. Sequence ID Motifs/Repeat Type Organism Protein E-value Primer pairs (5’-3’) Product size (bp) ZM14-Contig13 (GT)6/ P-(GCG)6, (CG)3/ IP Z. mays Putative ring zinc fi nger protein 7e-38 ATACATTTTTACGTCCAC GTTTCGTTTGGAGGGTGTCTT 214 ZM14-Contig24* (CA)6/ P Z. mays Unknown protein 3e-26 TTTGTCCAATCAAGCGAGATA TTGTGTCCGTGCAAATTGGTG 488 ZM14-Contig27* (TA)9,(AC)3/ IP Z. mays Jacalin like lectin protein 3e-93 CGGTAGCGAAAATACAGT TCCATCTCCTTCATCTACATC 504 ZM14-Contig53 (CCG)6/ P Z. mays Unknown protein 3e-140 Not suitable for primer design – ZM14-Contig68* (AT)5/ P Z. mays Aquaporin plasma membran protein 1e-32 AAACAGGGAGTCTTCTTCTTAAC AAACAGGGAGTCTTCTTCTTAAC 382 ZM14-Contig79 (CG)5/ P Z. mays Unknown protein 3e-166 Not suitable for primer design – ZM14-Contig106* (TT)6/ P Z. mays Hypothetical protein 1e-48 GCCCATATTACATACAAAAG CAACAAGAAGGTTTATTCTG 708 ZM14-Contig114 (GG)5/ P Z. mays Unknown protein 5e-48 TGGACCAAACATCGGTTGAGC GAATGGAAGGAAGAGGGGTGGT 530 ZM14-Contig119 (ACA)5,(CCA)6/ P(CCG)5,(CCA)3/IP Z. mays Hypothetical protein 6e-39 TGGCCCCACCTGATGAAATAA GGAGTGGAGGCGGAGGATCT 615 ZM14-AI649544 (TA)3,(TGGA)5/ IP Z. mays Oxin transporter protein 1e-81 CGATGCCGAAAACCCATTCTT GCATCTGCTCGTGGAGGAAAA 303 ZM14-AI649556 (CTG)5/ P Z. mays Lysin decarboxylase 3e-07 Not suitable for primer design – ZM14-AI855154 (GAG)3,(CTG)5/ IP Z. mays Hypothetical protein 0.002 TGGTCCTGCTGCTTCTCTTGC CAAACGGTCCACCTCCACCTC 316 ZM14-AI855158 (AT)5/ P Z. mays Ribosome biogenesis protein 1e-94 Not suitable for primer design – ZM14-AI855164 (AT)5/ P Z. mays Unknown protein 4e-08 GTGCAGAGACGGACAGCGAGT TTTTGAGTTTTGCCGGAGTGG 346 ZM14-AI855182 (AT)6/ P Z. mays Glyceraldehyde-3- dehydrogenase 1e-47 CGACTCCCAACAGGAAATGGA GTCGCCTGGTACGACAACGAG 349 ZM14-AI855214 (TA)5/ P Z. mays Unknown protein 5e-61 TGGATCGAAGCAACTCGCACT CAACAGCGTCAAGAGCGTCAA 310 ZM14-AI855258 (TC)13/ P- (TA)3, (CACAG)6/ IP Z. mays Unknown protein 3e-19 GAGATAGGCGAGGAAGGTGAG ATCGTCATCATTCGAGCAGAG 180 ZM14-AI855272 (AGG)6/ P Z. mays Putative MYB binding protein 2e-43 GACCTCAACCTCGACCTCTG GGCTTCGTTCACTTCATCTTG 275 ZM14-AI855286 (CTG)9/ P Z. mays Unknown protein 3e-34 CCCTCTCTTTTCTCAGCCCTA ATCCAGAGGACGAGGGTTTT 288 ZM14-AI855336 (CGC)5/ P Z. mays Hypothetical protein 2e-28 GAGAAGCCGAGAACAGTAGCA CACGAGGCAGAGTCGTAGTTT 365 ZM14-AI855352 (GC)5/ P- (CGC)6, (CGA)3/ IP Z. mays Hypothetical protein 8e-11 CGAGAGCGCCATTAGAAGTCG TTTTGTGGAACGAAGCGATGG 401 ZM14-AI855361 (ACC)9,(TA)5/ P Z. mays Unknown protein 2e-47 GACCTGGAGTGGTGGTTC GATGGGATACAAATACAATAC 481 ZM14-AI649556 (CTG)5/ P Z. mays Lysin decarboxylase 3e-07 CCGGGTTTTGGACTTTGGAGA TGTTGTGGCTCTTTGCCTGTG 301 ZM14-AI861145 (TT)5,(GTC)5/ P Z. mays Unknown protein 4e-56 TTGTTTTTGCCTTTCCTTGAA TGCTGTACCCAAATCCTTCTG 447 ZM14-AI857227 (GCC)6/ P Z. mays putative cytochrome P450 superfamily protein 5e-43 GCCGTGCCAACTTTTAATTTC CTGGAGGTGGAAGGAGAGG 367 ZM14-AI855418 (AG)5/ P Z. mays Putative phototroph- ic-responsive NPH3 family 0.082 GCCTGCCTGTCCATCAATCAA CATCCACCTCCCACCCAGAAC 242 ZM14- AI857233 (ATG)6/ P Z. mays Protein transport protein sec31-like ATCTGCACCTCAACCTGAATG ATTGGATGGTTCTTGTGTTGG 236 YUMURTACI A., SİPAHİ H., ZHAO L. 60 ACTA BOT. CROAT. 76 (1), 2017 chromo some 1, but they were located far from each other (50.58 cM). On the other hand, the loci ZM13-Contig37 and ZM13-Contig83 were located together on the other chromosome 2, with 154.58 cM. The in silico mapping of 44 EST-SSR markers in comparison to maize genome as- sembly is given in Fig. 1. All primer pairs were assigned to ten maize chromosomes by in silico mapping while only 4 polymorphic primer pairs were assigned to two chromo- somes by classical genetic mapping. The putative functions of SSR contained sequences were assigned in the GenBank database using similarity search of BLASTX (Tabs. 2, 3). The data indicated that 26 (44.06%) of 59 loci have shown similarity to known genes and have a range of functions, such as metabolic enzymes, structural proteins, disease signaling and transcription fac- tors. More than half (52.7%) of the 55 primer pairs did not yield any signifi cant annotation. Discussion DNA sequences retrieved from maize cDNA libraries in NCBI data-base were searched for repetitive regions. The repeat percentage was found to be 1.46% under salt stress in maize cDNA libraries from NCBI GenBank. This fre- quency is similar to that previously reported by Kantety et al. (2002) (1.5% percentage of maize EST-SSRs). The most abundant repeats were di-nucleotide (78.8%). This result is consistent with those obtained by Wang et al. (1994) in temperate maize and Sibov et al. (2003) in tropical maize. The most repeated di-nucleotide motifs were (TT)31, (CT)27, (CT)32 and (GA)26, (GA)34. Jayashree et al. (2006) observed (CT) and (GA) repeats to be the most common di-nucleo- tide motifs in cereals and legume plants. Four tri-nucleotide repeats (CTC, CTG, CTT, TTA) responsible for leucine synthesis were found only in root tissue specifi c sequences. The triplet codons for proline and arginine were mostly detected in contigs and singletons from root and shoot tis- sue. Proline containing repeats were detected in ZM14- Contig119 and ZM13-Contig175 sequences for root and shoot tissue respectively, while ZM14-AI855272, ZM14- AI855352 and ZM14-AI855336 sequences contained trip- lets for arginine synthesis. Proline is a well-known osmo- protectant molecule and responsible for balancing the cell water potential in plants (Hu et al. 1992). In case of de- creased photosynthesis rate, proline has also functions in protein deamination to provide extra energy from proteins (Mattioli et al. 2009, Hayat et al. 2012). Thus, it has been pointed that these ESTs may especially have the potential to develop markers for salt stress. Although many DNA markers have been developed from maize, SSR markers have not been developed specifi - cally from ESTs constructed under salt stress condition. In this research, 55 primer pairs were specifi cally developed by using salt stress-induced maize ESTs. Fragments with expected sizes were clearly amplifi ed in all primer pairs. Molecular markers can be mapped by using classical ge- netic mapping or in silico mapping. In this study, many markers have not been mapped classically due to the lack of polymorphism between the parents of the mapping popula- tion. On the other hand, according to in silico mapping, 44 markers were assigned to ten maize chromosomes (Fig. 1). In silico mapping position of four markers (ZM14-Con- tig119, ZM13-AI964577, ZM13-Contig37, ZM13-Con- tig83) were matched with the conventional linkage map, in terms of both chromosome arm location and order. Fig. 1. In silico relative map locations of 44 expressed sequence tagged-simple sequence repeats (EST-SSRs) on haploid maize chromo- somes constructed on MapChart 2.0 software and mapping unit represented as centimorgan (cM). GENETIC ANALYSIS OF MICROSATELLITE MARKERS IN MAIZE ACTA BOT. CROAT. 76 (1), 2017 61 In classical genetic mapping analysis, segregation devi- ation from expected Mendelian segregation ratios was de- tected for two markers (ZM14-Contig119 and ZM13-AI 964577). These markers mapped to chromosomes 1 were skewed towards the F63 (tolerant) parent. Deviation from the expected Mendelian ratios was previously reported in maize (Pereira and Lee 1995, Sharopova et al. 2002). Segre- gation distortions are often due to differential gametophytic selection (Lu et al. 2002), chromosomal rearrangements (Lu et al. 2002). Sa et al. (2012) indicated that segregation distortions might be infl uenced by the mapping population of F2, a backcross or recombinant inbred line, and by sizes of the mapping population. Expansion of the knowledge of the function of ESTs will increase the likelihood that EST based markers for salt- tolerance in maize molecular breeding will be identifi ed. In this study, there are many ESTs exhibiting sequence homo- logy in sequence databases (Tabs. 2, 3). Two contig se- quences (Contig33, Contig27) relating to maize root and shoot tissues were matched with jacalin-like plant stress proteins. Xiang et al. (2011) have proved that jacalin is a core compound for plant disease resistance mechanism. In addition, two contigs (Zm13-Contig37 and Zm13-Contig83) assigned to chromosome 3 showed homology to nonspecif- ic lipid transfer proteins (nsLTP). LTPs in maize (ZmLTPs) have critical roles in resistance to biotic and abiotic stress. They provide high salinity resistance with decreasing solute permeability of cell membrane (Liu et al. 2015). 63 nsLTP genes identifi ed and differentially expressed under drought, salt and cold stresses were unevenly assigned to ten maize chromosomes by in silico mapping (Wei and Zhong 2014). Six ZmLTPs (ZmLTPd6, ZmLTPd7, ZmLTPd8, ZmLTP1.1, ZmLTP1.2, ZmLTP1.3) were placed on chromosome 3 (Wei and Zhong 2014). Sharapova et al. (2002) used an IBM population B73xMo17 and mapped the microsatellite marker p-umc1010, as an anchor marker for phospholipid transfer protein homolog2 (plt2), on maize chromosome 3. This marker region amplifi ed a (GA) motif with ten repeats. In our study, we have identifi ed two different alternative marker regions associated to phospholipid transfer protein in maize F63xF35 RILs. These marker loci (ZM13-Con- tig37 and ZM13-Contig83) were placed on chromosome 3 and amplifi ed the repeat regions for (TC)10 and (TA)5. In ad- dition, Zm14-Contig119, which covered three different types of triple motifs (ACA, CCG and CCA) and was lo- cated on maize chromosome 1, matched with hypothetical proteins. In Sorghum, Ngara et al. (2012) observed 22 hy- pothetical protein inductions after salt stress application in moderately salt tolerant plants. Similarly, Zahra et al. (2013) observed hypothetical protein induction after salt stress application. Shinozaki et al. (2005) reported that hy- pothetical proteins with uncharacterized domains were rel- evant to salt tolerance. Another EST-SSR tagged as ZM13- AI964577 showed similarity to Glycine max anaphase entrance complex. However, this sequence showed a poly- morphic fragment pattern, and Blastx E-value resulting was out of the accepted score limits. Parental genotypes and their 158 F5 derived RILs which were used in our study were previously tested with 3072 SNP markers (Cui et al. 2015) and 81 of these SNP markers clustered on chromosomes 1, 3 and 5. Results suggested that some QTLs were related to the traits of shoot sodium and potassium concentration in maize. In our study, we have identifi ed four EST-SSR markers that were annotated to two different maize chromosomes (1 and 3). Signifi cant- ly, annotation analysis of these EST-SSRs showed a close relatedness to some salt tolerance proteins. Another matched EST (AI964488) displayed homology with carbonic anhydrase protein. Yu et al. (2007) demon- strated that carbonic anhydrase gene was expressed in Ory- za under environmental stress such as salt stress. Further, the root specifi c Contig68 showed a similarity with Aqua- porin plasma membrane protein. Plasma membrane pro- teins control osmotic pressure of the cell and they are close- ly correlated to specifi c transport proteins such as SYP121. This protein was identifi ed as vesicle transport protein and controls potassium traffi c in plant cells (Besserer et al. 2012). Potassium is an essential, and the most abundant, cation in plants and it has protective effects for plants under salinity (Cakmak 2005). Lastly, EST (AI855272) derived from root tissue displayed high similarity with the MYB binding protein. Genes coding MYB transcription factors were fi rst discovered from maize (Paz-Ares et al. 1987). These proteins have various regulatory functions in gene expression mechanism under salinity (Hasegawa et al. 2000). For screening of high potential parts of plant genomes, there are different types of molecular marker techniques such as the PCR based; SSR, ISSR (inter simple sequence repeat) and hybridization based; DArT (diversity array te- chnology) and sequencing based markers such as SNPs (Xie et al. 2006, Kalia et al. 2010). ESTs are attractive tools for marker development since they represent coding regi- ons of the genome. There are a number of advantages for markers developed from EST-derived sequences (Davis et al. 1999). When an EST marker is found to be genetically associated with a trait of interest, the corresponding ma- pped gene can directly affect the trait. Also, genetic map- ping with ESTs would provide a more rapid transfer of lin- kage information between species (Cato et al. 2001). SSR markers are abundantly distributed throughout the maize genome and they are cost effective markers for screening QTLs (Xu et al. 2013). In this study, both advantages of ESTs and SSRs were merged to characterize the new mar- ker sources for maize breeding. In terms of overall evaluation of homology analysis, the new primers that were designed from computationally ex- tracted EST-SSRs and tested in tolerant and sensitive maize genotypes might be used for scanning other maize germ- plasm sources. Furthermore, integration of molecular vali- dation of these SSRs may serve cost effective molecular markers for improvement of salt tolerant maize. With the improvement of feasible molecular markers, these candi- date EST-SSRs might have the ability to broaden the ge- netic base of maize. YUMURTACI A., SİPAHİ H., ZHAO L. 62 ACTA BOT. CROAT. 76 (1), 2017 Acknowledgements The authors would like to thank Professor Huabang Chen, State Key Laboratory of Plant Cell and Chromo- some Engineering, Institute of Genetics and Developmen- tal Biology, Chinese Academy of Sciences, for his great support during experiments. 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