2019 Volume 69 Issue 1 Pages 1-10
Sago palm (Metroxylon sagu Rottb.) is one of the most productive carbohydrate-producing crops. Unfortunately, only limited information regarding sago palm genetics is available. This study aimed to develop simple sequence repeat (SSR) markers using sago palm NGS genomic data and use these markers to evaluate the genetic diversity of sago palm from Indonesia. De novo assembly of partial sago palm genomic data and subsequent SSR mining identified 29,953 contigs containing 31,659 perfect SSR loci and 31,578 contigs with 33,576 imperfect SSR loci. The perfect SSR loci density was 132.57/Mb, and AG, AAG and AAAT were the most frequent SSR motifs. Five hundred perfect SSR loci were randomly selected and used for designing SSR primers; 93 SSR primer pairs were identified. After synteny analysis using rice genome sequences, 20 primer pairs were validated using 11 sago palm accessions, and seven primers generated polymorphic alleles. Genetic diversity analysis of 41 sago palm accessions from across Indonesia using polymorphic SSR loci indicated the presence of three clusters. These results demonstrated the success of SSR identification and marker development for sago palm based on NGS genome data, which can be further used for assisting sago palm breeding in the future.
Sago palm (Metroxylon sagu Rottb.) is one of the most productive carbohydrate-yielding plants worldwide (Ishizaki 1997). A sago palm tree can approximately accumulate 100–300 kg starch in its trunk (Dewi et al. 2016). Moreover, sago palm yields four times more starch than rice (Karim et al. 2008). Therefore, sago palm is a potential solution for the impending worldwide food crisis (Abbas et al. 2010). This monocot species has a diploid number of 26 chromosomes (2n = 2x = 26). It belongs to family Arecaceae and order Arecales (Flach 1997). Sago palm occurs throughout the Southeast Asian region. However, Beccari (1918) proposed the Maluku Islands in the eastern part of Indonesia as the centre of sago palm genetic diversity. Sago palm is relatively tolerant of abiotic stress environments, especially that of swampy and waterlogged areas (Singhal et al. 2008). Moreover, sago palm can thrive in acidic peat soils with high concentrations of metal compounds (Miyamoto et al. 2009). Most current commercial crops are unlikely to survive under such suboptimum conditions (Tajuddin et al. 2007).
Although sago palm is an essential starch-producing crop and has excellent environmental adaptability (Tajuddin et al. 2007, Uthumporn et al. 2014, Wee and Roslan 2012), until recently, attention to this crop has been insufficient (Karim et al. 2008). Most commercial producers only harvest sago palm from natural sago forests and invest little for sustainable use. Although phenotypic variabilities exist among natural populations of sago palms, breeding for specific phenotypes and traits is still necessary. Unfortunately, understanding of sago palm genetics is also limited (Wee and Roslan 2012). Therefore, modern molecular biology approaches are required to support further elucidation of sago palm biology and genetics in order to support a large-scale cultivatation of sago palm.
Some researchers have reported the use of molecular markers for elucidating genetic information in sago palm. However, most of these reports have described a limited number of marker loci, for either sago provenance or population samples, or both. Abbas et al. (2009) used RAPD markers to study sago palm genetics. In other studies, Kjær et al. (2004) used AFLPs and Abbas et al. (2010) used chloroplast DNA (cpDNA) markers for studying the crop. The availability of more robust molecular markers will assist in improving the understanding of sago palm genetics.
Understanding the genetic variability of natural sago palm populations is necessary to support future sago palm breeding (Abbas et al. 2010) and genetic resource conservation programmes (Kjær et al. 2004); however, robust genetic markers must be developed to support these important endeavours. Unfortunately, markers capable of providing high-resolution information regarding sago palm genome have not been readily available.
Simple sequence repeats (SSRs), or microsatellites, are repetitive DNA sequences that consist of 1–6 bp motifs widespread across both prokaryotic and eukaryotic genomes (Grover et al. 2012, Guo et al. 2009, Kelkar et al. 2008, Sharma et al. 2007). Such SSRs are useful for molecular marker development because they are abundant, highly polymorphic, multiallelic and inherited codominantly (Singh Kesawat and Das 2009). SSRs have been widely employed as genetic markers for many genetic studies on crops (Ashkani et al. 2012, Geethanjali et al. 2017, Girichev et al. 2017, Kaur et al. 2016, Ott et al. 2011, Rauscher and Simko 2013, Zong et al. 2015). Despite the many advantages of SSR markers, one disadvantage is the high cost of marker development because it requires extensive sequencing of the target plant genome (Zalapa et al. 2012). This constraint hinders the usage of SSR markers for crops with limited genome sequence information, such as sago palm.
Recent developments in DNA sequencing technology, such as next-generation sequencing (NGS), have offered a new avenue to acquire large genome sequences of non-model crops, mine SSR marker sequences and develop the required primers to generate SSR markers (Zalapa et al. 2012). Many examples of the use of such genomic data for SSR marker development in non-model crops have recently become available in the literature (Zalapa et al. 2012). Although some palm species have received great attention using NGS sequencing technology, unfortunately, sago palm has not.
In this study, we aimed to identify and develop new SSR markers from partial genome sequences of sago palm using the Illumina GAIIx platform and paired-end genomic fragment libraries. Following de novo assembly of the raw reads, the partial genomic sequence data were subsequently used to mine SSR sequences, design appropriate primers and develop specific SSR markers for sago palm. After validation of the developed markers, some polymorphic markers were used to evaluate the genetic diversity of 41 Indonesian sago palm accessions. To the best of our knowledge, this paper is the first to report SSR marker mining using genomic sequences of sago palm and use generated markers for evaluating diverse Indonesian sago palm accessions.
For genome sequencing and SSR marker development, we utilised the collection of sago palm accessions of the Indonesian Agency for Assessment and Application of Technology (BPPT). For initial SSR marker validation, we used 11 sago palm accessions. Subsequently, we used a diverse assortment of 41 sago palm accessions originating from different regions in Indonesia for genetic diversity studies (Fig. 1). Fresh leaf samples were collected from BPPT sago palm accessions representing all available provenances and used for DNA isolation. Additionally, fresh leaf samples from field points of origin of sago palm were collected, wrapped in paper and sent by airmail to the Biotechnology Lab, BPPT, for DNA isolation.
Map illustrating the origin of sago palm samples used to analyse genetic diversity using SSR markers. Sago palm accessions originated from (
) Sumatra, (
) Seram, (
) Java, (
) Borneo, (
) Halmahera and (
) Papua. The number in the circles indicates the number of accessions collected from each location.
Total DNA was isolated from leaf samples of sago palm using the standard CTAB method modified for DNA isolation from palm leaves (Maskromo et al. 2016, Novero et al. 2012, Pesik et al. 2015, 2017, Tinche et al. 2014). We performed sago palm genome sequencing of DNA extracted from young leaves using the Illumina GAIIx instrument. Paired-end genomic library construction (2 × 72 bp) was conducted with a commercial Nextera XT Index with TruSeq Dual Index Sequencing Primer Box kit, following the manufacturer’s protocol (https://www.illumina.com/products/by-type/sequencing-kits/cluster-gen-sequencing-reagents/truseq-dual-index-seq-primers.html). After quality trimming of raw reads with Trimmomatic, we used Ray software (https://github.com/sebhtml/ray) for de novo genome assembly. Subsequently, we used the assembled sago palm genome sequences for SSR mining and marker development.
SSR sequence mining from partial sago palm genome sequenceWe used the assembled contig data (at least 200 bp) to search for di-, tri-, tetra- and hexanucleotide repeats of SSR loci of at least 20 bp lengths. We used Phobos software (http://www.ruhr-uni-bochum.de/ecoevo/cm/cm_phobos.htm) to mine SSR motifs from the assembled genome sequence. Identified SSR loci were then grouped into either perfect or imperfect SSRs and designated as either class I or II SSRs. SSR locus density was determined based on the frequency of SSR loci and the total length of contigs containing SSRs. We also evaluated the motif length, loci numbers, mean repeat numbers and densities for the selected repetitive motifs.
SSR primer design and primer validationTo design SSR primers, we selected 500 of the total class I SSR loci with a minimum of 10-fold coverage from the outputs of Phobos software. The contigs containing selected SSRs were used for SSR primer design using Primer3-Plus software (http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi). The parameters for SSR primer design included 200–600 bp amplicon size, 18 bp optimum primer size, 50°C–60°C primer melting temperature (Tm) and 40%–60% primer GC content.
Once the SSR primers were identified, we performed synteny analysis on selected contigs containing SSR loci. Synteny analysis was performed on rice (Oryza sativa) chromosome sequences available from the Phytozome website (https://phytozome.jgi.doe.gov/pz/portal.html#!search?show=BLAST) to evaluate their probable position distributions in the rice genome. Subsequently, we selected 20 primer pairs distributed across the 11 rice chromosomes for primer validation. The ability of the selected SSR primer pairs to amplify polymorphic markers was evaluated and validated using 11 sago palm accessions. The SSR primers successfully yielded polymorphic markers across the 11 sago palm accessions during primer validation steps and were subsequently used for genetic diversity analysis.
PCR amplification and allele identificationPCR amplification mixtures consisted of 5 μL of 5× Taq Polymerase buffer, 0.25 μL KAPA Taq HotStart Extra (100 units/μL), 1.5 μL MgCl2 (25 mM), 0.5 μL dNTP (10 mM), 0.5 μL Forward and Reverse primers (100 mM) and made up to 25 μL with sterile ddH2O. The Takara PCR Thermal Cycler Dice® (http://catalog.takara-bio.co.jp/product/basic_info.php?unitid=U100004192) was used for SSR marker amplification. First, DNA extract was subjected to one cycle of denaturation at 95°C for 3 min. This was followed by 35 cycles of denaturation at 95°C for 30 s, primer annealing at the appropriate Tm for each primer pair for 30 s and primer extension at 72°C for 30 s. Finally, there was a final extension step at 72°C for 60 s.
For SSR allele identification, we used denaturing polyacrylamide gel electrophoresis (PAGE) using a vertical slab gel DNA sequencer (34 × 45 cm) in a 6% SB (1×) buffer-polyacrylamide gel (Brody and Kern 2004). Allele visualisation employed the silver staining method, as described by Chevallet et al. (2016). We scored markers manually and selected the polymorphic ones for genetic analysis.
Sago palm genetic diversity and structure assessmentWe calculated a dissimilarity matrix based on allelic data for the diploid using a simple matching dissimilarity index. The calculation of dissimilarity matrices used bootstrap analysis with 10,000 iterations. Principal coordinate analysis (PCoA) based on dissimilarity was set using the option of 41 axes to edit, and the default axis as determined by the PCoA was selected. We performed tree construction using the calculated dissimilarity matrix by the weighted neighbour-joining approach. The dissimilarity matrix, bootstrapping, PCoA and tree construction for the sago palm accessions were conducted using Dissimilarity Analysis and Representation for WINDOWS (DARWin) software version 6.05 (Perrier and Jacquemoud-Collet 2006; http://darwin.cirad.fr/darwin).
We calculated population genetic parameters (allele numbers, He, Ho and PIC) for each of the SSR marker loci using CERVUS software version 3.0 (Kalinowski et al. 2007) and GENALEX software version 6.501 (Peakall and Smouse 2012). STRUCTURE software version 2.3.4 (Pritchard et al. 2000, http://pritch.bsd.uchicago.edu/structure.html) was used to analyse population structures and differentiate allele frequencies. For calculations to estimate an ideal number of populations (K), we ran each of the K estimate in an admixture model, with K = 1–10 and with each K replicated 20 times. We implemented each replication with a burn-in period of 100,000 steps followed by 250,000 replications of Monte Carlo Markov Chain model generation. Ad-hoc statistics were evaluated to estimate changes in the log probability of data according to the K value, as suggested by Evanno et al. (2005). The ideal number of population clusters was determined based on the highest K value as estimated using STRUCTURE HARVESTER (http://taylor0.biology.ucla.edu/struct_harvest/) (Earl and vonHoldt 2012).
In this study, a total of 315.56 MB of raw reads of partial sago genome sequence data was generated using the Illumina GAIIx paired-end NGS system (Table 1). Results of total nucleotide composition analysis indicated that adenine was the most frequent base (A = 31.4%), followed by thymine (T = 30.7%), cytosine (C = 18.4%) and guanine (G = 18.3%). The percentage of GC content in partial genome sequences of sago palm was approximately 37%. Following de novo assembly, we identified a total of 904,670 contigs (Table 1). The minimum length of the assembled contigs was 100 bp and the maximum was 355,487 bp. The average length of the assembled contig was 263 bp, whereas that of N50 was 291 bp (Table 1).
| NGS and de novo assembly summary | Number |
|---|---|
| Reads sequences (Mb) | 315,563,284 |
| Contig sequence numbers | 904,670 |
| Contigs length (bp) | 238,803,664 |
| Shortest contig (bp) | 100 |
| Longest contig (bp) | 355,487 |
| Mean length of contigs (bp) | 263 |
| N50 | 291 |
We identified 29,953 contigs containing 31,659 loci of perfect SSRs and 31,578 contigs with 33,576 loci of imperfect SSRs (Table 2). Further analysis also indicated that 12,673 (40.03%) SSR loci were class I SSRs (repeat length ≥20 bp), with a density of 40.2 SSR/Mb, and 18,986 (59.97%) were class II SSRs (repeat length 12–20 bp), with a density of 60.2 SSR/Mb (Supplemental Fig. 1A).
| SSR sequence mining summary | Number |
|---|---|
| Total SSR number : | |
| Perfect | 31,659 |
| Imperfect | 33,576 |
| Contig containing SSR: | |
| Perfect | 29,953 |
| Imperfect | 31,578 |
Dinucleotides (17,376; 55%) were the most frequently found types among perfect SSR motifs, and hexanucleotides (2,196; 7%) were the least frequently found (Table 3). The cumulative length of the SSR-containing contigs was 335,162 kb for dinucleotide and 45,885 kb for hexanucleotide SSRs, while the SSR loci densities were 72.06 loci/Mb for dinucleotides and 9.19 loci/Mb for hexanucleotides (Table 3). The frequency, SSR cumulative length and density of SSR loci occurrence in the partial sago palm genome decreased with increasing motif length (di- to hexanucleotides, Table 3). The mean repeat number for dinucleotides was 9.6, whereas that for hexanucleotides was 3.5 (Table 3).
| Motifs length | Number of loci identified | Mean of repeat number | Cumulative length (kb) | Density (SSR/Mb)* |
|---|---|---|---|---|
| Di- | 17,376 | 9.6 | 335,162 | 72.76 |
| Tri- | 5,682 | 6.1 | 104,216 | 23.79 |
| Tetra- | 3,816 | 4.8 | 72,671 | 15.98 |
| Penta- | 2,589 | 4.0 | 51,211 | 10.84 |
| Hexa- | 2,196 | 3.5 | 45,885 | 9.19 |
| Total | 31,659 | 27.9 | 609,145 | 132.57 |
The most frequently found sequence motifs for the class I and class II SSR loci were AG, AAG or AAAT. Among the four dinucleotide repeat motifs found in the sago palm genome (AC, AG, AT or CG), the AG repeat was most common (38.7%), whereas the CG repeat was least common (0.22% of the total number of SSRs). For trinucleotides, the AAG repeat (5.2%) was most common, whereas the ACG repeat was least common (0.12% of the total SSR). We did not identify any CCG trinucleotide repeats in the results of the NGS SSR sequence mining of the sago palm genome (Supplemental Fig. 1B, 1C).
SSR primer design and primer validationTo design PCR primers, we randomly selected 500 of 31,659 identified class I SSR loci. However, for 407 (81.4%) of these randomly selected loci, we could not design the flanking primers because of either unsuitable flanking sequences or Tm constraints. We designed flanking primers for 93 (18.6%) selected loci that consisted of 37 dinucleotide, 24 trinucleotide and 32 tetranucleotide repeats (Supplemental Table 1). The contig sequences used to design primers were deposited in the NCBI GenBank DNA Database under the accession no. MG904300-MG904384.
Results of synteny analysis using rice genome data indicated that 55 of the 93 selected SSR loci were represented on the 11 rice chromosomes; therefore, 38 sago palm loci were not found within any rice chromosome. For primer validation, we selected 16 SSR primer pairs distributed across the 11 rice chromosomes and four pairs of unknown location (Table 4). Selected primer pairs amplified nine loci of dinucleotide, six of trinucleotide and five of tetranucleotide repeats. The results of primer validation (Supplemental Fig. 2A) indicated that only seven primer pairs produced polymorphic SSR markers, 12 produced monomorphic markers and one primer pair failed to generate any amplicon across the 11 sago palm accessions investigated.
| No. | Primer | Primer sequences (5′-3′) | Motifs | Product size (bp) | Results of synteni analysis | |
|---|---|---|---|---|---|---|
| Contig location in ricechromosome | E-value | |||||
| 1 | sV16071 | F: TGCCACTGGTGAAGAGCA | AAGG | 497 | Chr 3 | 7.00E-75 |
| R: TTCTCGAGGCCGTTCTTG | ||||||
| 2 | sV4223 | F: TCATCAGCCCCTTCAGATG | ATC | 444 | Not found | Not found |
| R: CACGCTGAGGCAGAGAAA | ||||||
| 3 | sV523089 | F: TCCCAAAAGGGCAAACAA | AAG | 381 | Not found | Not found |
| R: AGAAAAGTCTGGGCAGATCG | ||||||
| 4 | sV7173 | F: TGCTGGTTCTCTTGTCGTGT | AG | 419 | Chr 11 | 0.002 |
| R: TCTCCCCTCCGGACATTT | ||||||
| 5 | sV4442 | F: CATGCATGCACTGTTTGCT | AAT | 433 | Chr 1 | 1.00E-15 |
| R: GAGCGTTGGTTGCTCGAT | ||||||
| 6 | sV196074 | F: TGACCGAGGCAAGCTAGTG | AAAG | 405 | Not found | Not found |
| R: AGCTTGCGTGTTGCATTG | ||||||
| 7 | sV646 | F: GTAGCTGATTGCCCACTTAC | AGC | 229 | Chr 1 | 7.00E-19 |
| R: ATGGCACCACATCTTCTAAC | ||||||
| 8 | sV95916 | F: GGCATGCCCTATACAATTAC | ATCC | 233 | Not found | Not found |
| R: TGTGCCTTGCATGTATAAAG | ||||||
| 9 | sV7446 | F: CCTTCAGATAAACTGGTGGA | AG | 230 | Chr 12 | 5.00E-09 |
| R: CTCCTCGTAACAGAGAGGTG | ||||||
| 10 | sV2006 | F: GTATAGATGGAAAGCGTTGG | AT | 247 | Chr 2 | 3.00E-28 |
| R: CCGCTCCTTATCCTAGTCTT | ||||||
| 11 | sV400785 | F: ACTCCGCTCACTTGCACA | AG | 300 | Chr 5 | 8.00E-08 |
| R: GCACGCCTAAGGATGGAA | ||||||
| 12 | sV513907 | F: GGCGGAGCTTCAAGAACA | AG | 312 | Chr 6 | 0.016 |
| R: TCAATGCCAGACAAAGATGC | ||||||
| 13 | sV67385 | F: AGCACCGAAGGAAACAACC | AG | 310 | Chr 7 | 1.00E-05 |
| R: AGCCGAAAAGCCGAGTCT | ||||||
| 14 | sV6886 | F: GACATGCTTGGCCTTGGT | AT | 448 | Chr 8 | 9.00E-07 |
| R: CCTTGGTTGGAACCCTCA | ||||||
| 15 | sV109470 | F: CCCATGCCTTATGCTGGA | AAG | 360 | Chr 9 | 4.00E-24 |
| R: CTTGCTGGCTAGTGCCAAT | ||||||
| 16 | sV100242 | F: TTGAGCCAGGTATCATCCAA | AAAC | 308 | Chr 10 | 0.028 |
| R: ATCGTGGCAGAAGGTGGT | ||||||
| 17 | sV2283 | F: ACGGACCAGTCGGCATTA | AG | 596 | Chr 2 | 2.00E-63 |
| R: TCGGGGAGAGAGCGATTA | ||||||
| 18 | sV328094 | F: AACTGATGGGTGGGCAAA | AG | 471 | Chr 3 | 3.00E-47 |
| R: GCATGCACATGGGAGACA | ||||||
| 19 | sV72_1 | F: TCAGCCTTCCCTTCCTCA | AAG | 564 | Chr 5 | 9.00E-05 |
| R: ACAGCACATCGCAAGCAC | ||||||
| 20 | sV897681 | F: AGCACCGCGTGGAAAGTT | AAAT | 453 | Chr 6 | 0.011 |
| R: GCAACACATCTCCCACCA | ||||||
Results of the analysis using seven polymorphic SSR marker loci across 41 sago palm accessions indicated that there were 2–5 alleles per locus, with an average of 3.4 alleles (Table 5; see Supplemental Fig. 2B for a representative sample of silver stained acrylamide gel showing polymorphic SSR alleles). The estimated polymorphic information content (PIC) ranged from 0.356 to 0.704, with an average of 0.475 (Table 5). The sV2006 SSR locus (see Table 5) yielded the highest number of alleles (5) and highest PIC (0.704; Table 5).
| No. | SSR Loci ID | Estimated allele size (bp) | N | PIC | Ho | He |
|---|---|---|---|---|---|---|
| 1 | sV2006 | 247–350 | 5 | 0.704 | 0.780 | 0.758 |
| 2 | sV400785 | 300–620 | 3 | 0.530 | 0.854 | 0.604 |
| 3 | sV513907 | 312 | 3 | 0.406 | 0.561 | 0.522 |
| 4 | sV67385 | 310–410 | 4 | 0.533 | 0.878 | 0.620 |
| 5 | sV109470 | 360–400 | 3 | 0.390 | 0.366 | 0.494 |
| 6 | sV100242 | 308 | 2 | 0.356 | 0.683 | 0.470 |
| 7 | sV2283 | 596 | 4 | 0.406 | 0.293 | 0.459 |
| Average | 3.429 | 0.475 | 0.631 | 0.579 |
Expected heterozygosity (He), estimated using each of the evaluated SSR markers among 41 sago palm accessions, ranged from 0.459 to 0.758, with an average of 0.579. However, the observed homozygosity (Ho), estimated using each of the SSR markers, ranged from 0.293 to 0.878, with an average of 0.631. The sV2006 SSR locus exhibited the highest He, whereas sV2283 exhibited the lowest. However, the sV67385 SSR locus exhibited the highest Ho, whereas sV2283 exhibited the lowest.
Unrooted weighted neighbour-joining cluster analysis for the 41 sago palm accessions using DARWin software grouped the accessions into three clusters (Fig. 2). The first cluster (Cluster I) consisted of nine sago accessions from Sumatra and seven from Borneo; the second cluster (Cluster II) consisted of 10 sago palms from Java, four from Halmahera, five from Papua and one from Sumatra (L) and the third cluster (Cluster III) consisted of four sago palms from Seram and one from Sumatra (A2) (Fig. 2). Based on their phenotypes, BT1, BT2, BT3 and BT4 accessions (from Seram), A2 (from Sumatra), C4 (from Halmahera) and W (from Papua) were all of the spiny type, whereas the remaining accessions were spineless.
Unrooted weighted neighbour-joining cluster analysis of genetic dissimilarity as measured using amplified simple sequence repeat (SSR) markers. Accessions and collection localities are indicated in colour labels.
The results of PCoA (Fig. 3) presented a two-dimensional graphical view of the genetic diversity of 41 sago palm accessions originating from various regions in Indonesia. The clustering of sago accessions from PCoA supported dendrogram cluster analysis but not genetic structural analysis. The dendrogram cluster analysis grouped the sago accessions into three major groups (Fig. 2), and the genetic structural analysis grouped them into two major groups (Fig. 4).
Factorial analysis based on Eigen values calculated from seven SSR markers. The 41 sago palm accessions were clustered into three populations represented here by different colours.
Population structure of K = 2, K = 4, K = 6 and K = 8 inferred by Bayesian clustering approaches based on seven SSR markers. Samples of sago palm accessions from 1–10, 41: Sumatra Island; 11–14: Halmahera Island; 15–21, 29–31: Java Island; 22–28: Borneo Island; 32–35, 40: Papua Island and 36–39: Seram Island.
STRUCTURE V2.3.4 was used for genetic structural analysis and population distribution. Simulations were run with 100,000 iterations and population number (K) ranging from 1 to 10. Each K value was run over 20 times, and K was determined by the method proposed by Evanno et al. (2005). The results of the STRUCTURE HARVESTER analysis indicated that the highest peak of ΔK was at K = 2, while the second, third and fourth peaks of ΔK were also observed at K = 4, K = 6 and K = 8, respectively (data not shown). Fig. 4 presents sago palm population structures for K = 2, K = 4, K = 6 and K = 8, respectively. These structural analysis results illustrated the possible occurrence of two large sago accession groups in Indonesia, with genetic mixing in each sago palm population from different islands. The accessions of sago palm originating from Sumatra and Kalimantan belonged to a different group than the accessions from Java, Halmahera and Papua. For K = 4, K = 6 or K = 8, it was challenging to determine the number of discrete populations based on Fig. 4.
This research aimed to generate a partial genome sequence for sago palm and develop SSR markers based on these data. Here we successfully generated a partial sago palm draft genome (315.56 Mb) and demonstrated SSR marker development based on the assembled partial genome. The identified sago palm genome was approximately 13%–18% of the size reported for Cocos nucifera (2.42 Gb, Xiao et al. 2017), Elaeis guineensis (1.8 Gb, Singh et al. 2013) and Arenga pinnata (1.75 Gb, Rijzaani et al. 2017). It was also approximately 50% of the reported Phoenix dactylifera genome size (671.2 Mb, Al-Mssallem et al. 2013).
Therefore, more data are probably required to obtain the complete sago palm genome. SSR marker development from generated genomes has been reported for various crops (Kale et al. 2012, Li et al. 2014, Silva et al. 2013, Sonah et al. 2011, Song et al. 2015, Xiao et al. 2016, Yang et al. 2015). The density of SSR loci (132.57 SSR/Mb) in the identified partial sago palm genome was lower than that reported in other monocot species, which ranged from 175.4 to 363.3 SSR/Mb (Sonah et al. 2011). Moreover, the density of SSR loci was also lower than those found in other palm species (662.26–696.50 SSR/Mb, Xiao et al. 2016). One possible reason for this may be because the sago palm genome sequencing was run only once, which resulted in low resolution NGS data, and the assembled sequences only partially covered the sago palm genome. In the partially identified sago palm genome, AG, AAG and AAAT repeat units were the most frequently found SSR motifs for di-, tri- and tetranucleotide repeats, respectively. Similar results were obtained for oil palm (Ting et al. 2010, Zaki et al. 2012), date palm (He et al. 2017) and wheat (Jaiswal et al. 2017).
In this study, we estimated that the densities of penta- and hexanucleotide repeats were at least 10.84 and 9.19 SSR/Mb, respectively. These densities were lower than those found in oil palm, which were estimated as 58.9 SSR/Mb for pentanucleotide and 19.2 SSR/Mb for hexanucleotide repeats (Taeprayoon et al. 2015). In their SSR mining, Taeprayoon et al. (2015) used a total contig size of 499 Mb from the oil palm genome sequences, whereas we used a total contig size of 238.9 Mb in this sago palm study. In their genome-wide SSR investigation, Xiao et al. (2016) suggested that oil palm and date palm have a higher hexanucleotide SSR density than that of some other species.
In Xiao et al. (2016) study, they identified a total of 814,383 and 371,629 mono- and hexanucleotide SSRs in the E. guineensis and P. dactylifera assembled genomic sequences, respectively. They also reported the frequencies of 770.4 and 733.0 SSRs per Mb for mono- and hexanucleotide SSRs (Xiao et al. 2016). Tautz and Schlotterer (1994), Klintschar et al. (2004), and Song et al. (2015) in their studies of two Palmae species found the SSR densities based on the assembled genomic sequences were higher than that in other plant species.
From 500 randomly sampled loci out of 31,659 total SSR loci, 93 (18.6%) SSR primer pairs were identified and synthesised. The validated primers were successfully used to evaluate the genetic diversity of Indonesian sago palm accessions. Our data indicated that the identified SSR primer pairs were more likely to be polymorphic if they were from loci containing dinucleotide SSR motifs. Similar results have also been reported for other plants, indicating that dinucleotides produce more polymorphic alleles than other motifs (Simbaqueba et al. 2011, Wang et al. 2008).
Using tested and validated SSR primers, we generated 24 different SSR alleles from sago palm, with the number of alleles per locus ranging between two and five across the sago palm samples. In our study, more alleles were detected in dinucleotide SSRs than in other SSR motifs. The average number of alleles and PIC were 3.429 and 0.475, respectively, indicating that the generated SSR markers could be useful tools for genetic analysis of sago palm germplasm. Based on the criteria developed by Mateescu et al. (2005), the average PIC values calculated from seven SSR loci in this study were moderately informative. When tested in sago palm accessions from Indonesia, the SSR markers were more informative than AFLP markers (Kjær et al. 2004). Although the calculated PIC of Indonesian sago palm was lower than that reported in date palm (0.67, Arabnezhad et al. 2012), it was higher than that of oil palm (0.40, Zaki et al. 2012). According to Meszaros et al. (2007), molecular markers with moderate and high PIC values were adequate for assessing relationships among accessions based on geographic origin. Compared with AFLP markers previously used to evaluate sago palm, the developed SSR markers should be more useful because SSRs are codominant.
Eleven sago palm samples from Sumatra consisted of nine accessions (B1 to B9) from Bengkulu and two (L and A2) from Lampung provinces. The nine sago palm samples from Bengkulu were taken from provenances close to each other. In nature, vegetative propagation through tillers or suckers results in the formation of sago palm cluster provenances. Because they showed the same genotypes, nine samples taken from Bengkulu province might have been clonal samples. Alternatively, closely related samples from Bengkulu province would have appeared to be genetically identical if the genotyping was done using limited SSR marker loci. Seven SSR marker loci may have been insufficient to differentiate between nine different sago palm samples. However, two sago palm samples (L and A2) from Lampung province were genotyped using the same set of SSR markers and were found to be genetically different compared with the nine samples from Bengkulu province. Therefore, the evaluated SSR marker loci were informative for detecting distantly related sago palm samples from Sumatra. However, further evaluation should be conducted to clarify the clonal status of the nine sago palm samples from Bengkulu using more comprehensive SSR marker loci.
Genetic diversity analysis of Indonesian sago palm populations was previously performed using RAPD markers (Abbas et al. 2009), cpDNA (Abbas et al. 2010) and waxy gene (Abbas et al. 2012). The SSR markers generated in this study offer an alternative approach for evaluating and understanding genetic diversity and determining the relationships among different accessions of sago palms. In comparison with Abbas et al. (2009) who evaluated sago palm using RAPD markers, here we evaluated a higher number of sago palm accessions and used SSR markers. However, our results were similar to those obtained by Abbas et al. (2009) who also detected at least three different clusters of sago palm. In another study, Abbas et al. (2012) reported the presence of two sago palm clusters based on nucleotide variability of the wx gene. Current and previous studies (Abbas et al. 2009, 2012) investigated different sago palm accessions, although most of the accessions were from Indonesia. Therefore, it may not be valid to compare one set of results to the others. Moreover, similarities or differences in the findings among these studies may require further validation. Meszaros et al. (2007) stated that dissimilarity between groupings obtained using different types of markers might occur because different types of markers access different parts of the genome, even though all selected markers were used to evaluate the same set of organisms.
The results of the current study demonstrated the success of SSR identification and marker development for sago palm based on NGS genome data. The generated SSR markers were used successfully for evaluating the underutilised sago crop genetic diversity. In more comprehensive future studies, additional extensive SSR markers for sago palm based on our current partial genomic resources will be generated. Genotyping more Indonesian sago palm accessions and phenotyping the accessions for various beneficial characters will also be established. By developing additional SSR markers based on partial genome data, genotyping diverse sago palm accessions and phenotyping the studied palm materials, we will provide useful tools for future breeding of this underutilised, carbohydrate-producing crop.
The authors wish to thank BPPT for Master’s degree scholarship funding between 2014 and 2017 at IPB. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.
