TRANSCRIPTOME SEQUENCING OF LEPISANTHES FRUTICOSA TO DISCOVER SSR MARKERSZulkifli Ahmad Seman 1 1 Research Officer, Biotechnology and
Nanotechnology Research Centre, MARDI Headquarters, Persiaran MARDI-UPM 43400
Serdang, Selangor Malaysia.
2 Scientist, Malaysia Genome and Vaccine Institute
(MGVI), National Institute of Biotechnology Malaysia (NIBM), Jalan
Bangi,43000 Kajang, Selangor, Malaysia.
3 Deputy Director, Bio-Agrodiversity and
Environment Research Centre, MARDI Headquarters, Persiaran MARDI-UPM 43400
Serdang, Selangor Malaysia.
4 Director, Strategic Planning and
Innovation Management Centre, MARDI Headquarters, Persiaran MARDI-UPM 43400
Serdang, Selangor Malaysia
5 Director,
Bio-Agrodiversity and Environment Research Centre, MARDI Headquarters,
Persiaran MARDI-UPM 43400 Serdang, Selangor Malaysia
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Received 5 December 2021 Accepted 15 December 2021 Published 31 January 2022 Corresponding Author Zulkifli
Ahmad Seman, szula@mardi.gov.my DOI 10.29121/granthaalayah.v10.i1.2022.4451 Funding:
This
research received no specific grant from any funding agency in the public, commercial,
or not-for-profit sectors. Copyright:
© 2022
The Author(s). This is an open access article distributed under the terms of
the Creative Commons Attribution License, which permits unrestricted use, distribution,
and reproduction in any medium, provided the original author and source are
credited. |
ABSTRACT |
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Lepisanthes
fruticosa (ceri Terengganu) is one of the important underutilized fruit
plants with high value of bioactive compounds and pharmacological properties.
Current studies have focused mainly on the bioactive compounds which are
essential for functional food and pharmaceutical applications. However,
studies on the diversity and conservation of L. fruticosa are still scarce
since genomic and genetic resources for this plant species are still lacking.
In this study, RNA sequencing of L. fruticosa leaf was carried out using
Illumina HiSeq to identify potential unigenes and simple sequence repeats
(SSRs). A total of 52,657 unigenes were identified from about 91,043,356 million
raw sequence reads. Mining of SSRs from these unigenes have predicted a total
of 23,958 SSRs which was approximately 45.58% of total unigenes obtained.
Dinucleotide repeats motif was the highest (21.48%) and the next were
trinucleotide repeats motif (14.65%). A total of 4,620 SSRs ranging from 12
to 116 bp were selected for experimental validation. Bioinformatic analysis
via GO and KEGG platforms showed that a total of 1,861 (40.28%)
SSR-containing unigenes matched to Gene Ontology (GO) terminology and 48
biochemical pathways. The SSR-containing unigenes of L. fruticosa were
involved in various cell functions and a majority of their functions were
associated with purine and thiamine metabolism. In addition. A majority of
SSR-containing unigenes were involved in organic and heterocylic compounds
bindings, indicating an active event of biosynthesis process of secondary
metabolites in L. fruticosa. SSR markers obtained from this study provides
new genetic information that can be utilized to facilitate future
characterization of L. fruticosa accessions at molecular levels. |
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Keywords: EST-SSRs,
Lepisanthes Fruticosa, Transcriptome Sequencing 1. INTRODUCTION There is multiplicity
of underutilized fruits which are natively grown at the regions of Peninsular
Malaysia, Sabah and Sarawak. The plants bear less attractive fruits compared
to commercial plant species, however many of them |
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have high nutritional value and
medicinal properties Rizvi et al. (2015). Ibrahim et al. (2010) has shown the
important of native fruits to be traditionally used as a medicine to treat
several common diseases. However, narrow down of research has been focused on
different part of plants including fruit to further scientifically studied of
their medicinal benefits. Research has shown that the bioactive compounds, carotenoid, and other terpenoids
are the primary contributors to compounds identified from the plants extracts
which include phenolic their
medicinal properties.
Lepisanthes fruticosa or locally known as ceri Terengganu
is one of the valuable underutilized fruits in Malaysia with the potential to
be exploited for commercial production. L.
fruticosa is a non-seasonal woody plant of which the fruits are available
throughout the year. Typically, the fruits are arranged closely and
attractively in a big bunch or cluster (20 fruits/bunch). The flesh is soft and
fairly sweet taste with 1-3 seeds/fruit. The tree is small but can reach medium
height with spreading out canopy. The purplish colour of the young leaves adds
to the attractiveness of the tree Mirfat
et al. (2017). Studies have shown that L. fruticosa
ripe fruits contain the highest free radical scavenging and total phenolic
contents compared to numbers of underutilized and commercial fruits Mirfat
and Salma (2015).
This suggests the fruit of L.
fruticose is a good candidate for alternative medicine and health
benefiting food supplement. Umikalsum
and Mirfat (2014), Dayang
et al. (2012), Ibrahim
et al. (2010), Ikram et
al. (2009). Nevertheless, notwithstanding the
rich genetic diversity of L. fruticosa,
there are limited reports on germplasm diversity and molecular markers data.
For instance, the nucleotide sequences of L.
fruticosa deposited in NCBI
database (https://www.ncbi.nlm.nih.gov/gquery/?term=Lepisanthes+alata+) were found to be scarce (as little as
nucleotide sequences as of March 2018).
Large-scale
sequencing data possibly be generated from both genome and transcriptome via
recent advance in sequencing technology. Likewise, this next-generation
sequencing technologies and bioinformatics analysis has led to large-scale
identification of EST-SSRs from various crops Wang et al. (2010), Garg et al. (2011), Zeng et al. (2010), Zhou et al. (2016)). Simple sequence repeats (SSRs)
represent arrays of short motifs which are characterized based on their
hypervariability, abundance, reproducibility, Mendelian inheritance, codominant
nature Scott et
al. (2000), Gupta et
al. (1996) and convenient to be applied as
compared to the molecular markers Zhou et al. (2016). SSRs can be either predicted from
genomic or transcriptome which are known as genomics SSRs and EST-SSRs,
respectively Song et al. (2012). While EST-SSRs are derived from
expressed sequence tags and these types of SSRs are more evolutionary conserved
compared to genomic SSRs derived from noncoding sequences with relatively high
transferability Wei et al. (2011b). SSRs is powerful tool that have
been extensively used in population study to determine genetic diversity and
also to analyze genetic structure Yoichi
et al. (2017). The present study aimed to
generate and identify EST-SSRs from leaf of L.
fruticosa using Illumina paired end sequencing technology. Genic SSRs markers (in genic sequences) will
then be characterized based on their frequency and distribution followed by
analysis the functional properties of those SSRs. This data and results
obtained from the present study will be a valuable genomic and genetic
resources for future studies of L.
fruticosa.
2. MATERIALS AND METHODS
2.1. PLANT MATERIAL
The plants of L. fruticosa were grown at
MARDI’s germplasm located at MARDI headquarter, Serdang, Malaysia. Harvested young
leaves of L. fruticosa were snap-frozen in liquid nitrogen and kept in
-80°C freezer for further use.
2.2. RNA EXTRACTION FOR ILLUMINA SEQUENCING
A
total of 100 mg of young leaves tissue were powdered in liquid nitrogen prior
to RNA extraction using TRI Reagent (SIGMA-Aldrich, St. Louis, USA) according
to provide instruction. Extracted RNA was treated with RNase-free DNase I
Recombinant (QIAGEN, USA) to prevent sample from genomic DNA contamination. The
quantification of extracted total RNA was performed using Nanodrop ND-100
spectrophotometer (Thermo Scientific, Wilmington, USA). The integrity of RNA
bands was checked with 1% TAE agarose gel electrophoresis. The quality and RNA
integrity number (RIN) of total RNA was assessed using a 2100 Bioanalyzer
(Agilent Technology, Santa Clara, CA, USA). Illumina sequencing was performed
at Novogen Co., Ltd. Beijing, China, the cDNA libraries were sequenced using
Illumina HiSeqTM 2500 system under effective concentration.
2.3. SEQUENCE PREPROCESSING AND DE
NOVO ASSEMBLY
The
raw reads of sequencing data were filtered to generate high quality data. This
includes removing adaptor contaminants, removing reads with more than 10% of
uncertainty nucleotides and low-quality base reads with a cut-off value of
Phred score, q <= 20. The cleaned reads were then assembled using Trinity
version r20140413p1, with parameter minimum kmer coverage =2 and other
parameters were by default. De novo
transcripts assembly Trinity was then clustered using Corset version 1.05 to
obtain unigene sequences.
2.4. SSR MARKERS DISCOVERY
SSR
markers were discovered using MISA (MIcroSAtellite)
(http://pgrc.ipk-gatersleben.de/misa/). Parameters chosen for detection of a
SSR motif with minimum length of 12 base pairs (bp) and repeat length of
mono-10, di-6, tri-5, tetra-5, penta-5 and hexa-5. The maximum size of
interruption allowed between two different SSRs in a compound SSR was 100 bp.
The SSRs filtering was performed using a custom Perl script and R programming.
Criteria used in the SSRs filtering were choosing the markers which represent
more than two alleles and in a single contig. Approximately 200 bp of each side
of repeat motif region were extracted using bedtools version 2.21.0 (Quinlan
& Hall 2010). SSR markers were annotated using BLAST program (blastx)
against non-redundant (nr) and SwissProt protein databases with E-value of 1x10‑5.
Gene ontology (GO) enrichment and KEGG pathway analyses were conducted using
BLAST2GO software.
3. RESULTS
3.1. De novo assembly of
transcriptomic sequencing data
A total of 91,043,356 paired end raw reads were generated from
transcriptome sequencing of L. fruticosa leaf.
Approximately, the total reads comprise of approximately 13.66 Gigabase (Gb)
with an average read length of 150 bp were generated using Illumina HiSeqTM
2500 sequencer. After reads quality assessment, approximately 89,441,736 (98.24%) of high-quality
data were recovered (Table 1). This cleaned pair-end reads were
then assembled into 52,657 transcripts. Clustering of transcripts resulted in
52,569 unigenes (Table 2).
Table 1 Statistic of data quality control |
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Type of Data |
Total Reads |
Total Bases (Base Pair) |
Average Read Length (Base Pair) |
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Raw dataa |
91,043,356 |
13,656,503400 |
150 |
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Clean datab |
89,441,736 |
13,416,260,400 |
150 |
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Percentage of data quality (%) c |
98.24 |
98.24 |
100 |
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a original raw reads from sequencer Illumina HiSeqTM 2500 b Reads after quality filtering. c Figure obtained based on, |
Table 2 Statistic of de novo L. fructicosa transcriptome assembly |
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Transcriptsa |
Unigenesb |
|
Number of transcripts/unigenes |
52,657 |
52,569 |
Total bases transcripts/unigenes (base
pair) |
77,451,424 |
77,428,927 |
Range of length transcripts/unigenes
(base pair) |
201-17,572 |
201-17,572 |
Average of length transcripts/unigenes
(base pair) |
1,471 |
1,473 |
Length of N50 transcripts/unigenes (base
pair) |
2,136 |
2,136 |
3.2. Simple sequence repeats (SSRs) marker
discovery
A total of 23,958 of SSRs were identified which was accounted for 45.58%
of the total unigenes. All the markers were classified into seven types,
consisting of type c, type p1 (mono), p2 (di-), p3 (tri-), p4 (tetra-), p5
(penta-) and p6 (hexa-) nucleotide repeats (Table 3).
Table 3 Occurrence of SSRs in L. fructicosa transcriptome |
|
SSRs repeat type |
Total number |
SR markers type c |
1,941 |
SSR markers type p1 |
13,179 |
SSR markers type p2 |
5,146 |
SSR markers type p3 |
3,510 |
SSR markers type p4 |
113 |
SSR markers type p5 |
11 |
SSR markers type p6 |
12 |
Total |
23,958 |
Mononucleotide
repeats were the most abundant (55.01%) in the L. fruticosa transcriptome, followed by dinucleotide repeats
(21.48%) and trinucleotide repeats (14.65%). Among them, the most frequent
repeat motifs were AT/AG (9%), followed by TC/GA/CT (7%), TA (5%), TG/AC (2%),
CA and GAAAA (1%) (Table 4). A total of 4,620 SSRs with the
motif size ranging from 12 to 116 bp were selected as potential SSRs for genotyping
validation. The analysis was not included mononucleotide repeats and compound
formation.
Table 4 Summary of putative SSRs in L. fruticose transcriptome |
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SSRs repeat type |
Total number |
Total length (base pair) |
Most frequent repeat motif |
SSR markers type p2 |
2,305 |
33,746 |
AT/AG |
SSR markers type p3 |
2,251 |
36,768 |
GAA/TTC |
SSR markers type p4 |
51 |
1,108 |
CTCA |
SSR markers type p5 |
10 |
250 |
GAAAA |
SSR markers type p6 |
3 |
90 |
AAAAGA/GCTGGT/GGGCAA |
Total |
4,620 |
71,962 |
3.3. Functional annotation of SSRs unigene
Following
BlastX homology search analysis, only 954 (20.65%) of 4,620 unigenes of L.
fruticosa returned significant hits to the NCBI non-redundant (nr) database
with 10-5 E-value (Table 5). Based on the similarity search
analysis, sequences were found to be homologous to Citrus sinensis (197,
34%), Citrus clementina (113, 20%), Theobroma cacao (63, 11%), Hevea
brasiliensis (49, 9%), Cephalotus follicularis (32, 6%) and Corchorus
capsularis (32, 6%) (Figure 1). These results indicated that L.
fruticosa has high similarity with other perennial woody tree plants.
Table 5 Homology search of L. fruticosa transcriptome against NCBI nr database |
|
Number of SSRs |
|
With BLAST hitsa |
954 |
Without BLAST hits |
3,666 |
Total of sequences |
4,620 |
a based on an E-value cutoff of 1x10-5 |
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Figure 1 Top hit
species classification upon homology searches of SSR against nr database |
Gene
ontology analysis on SSRs associated unigenes was performed in order to gain an
understanding on the biological mechanisms involved in leaf of L. fruticosa. Results showed that a
total of 52,569 unigenes were annotated, of
which 1,861 (40.28%) unigenes represent identified
SSRs. GO annotation of SSR- containing unigenes were classified into three
categories: biological process (613), cellular component (747) and molecular
function (501), respectively. For biological process, the SSR-containing
unigenes were assigned into top 5 categories were organic substance metabolic
process (18%), primary metabolic process (18%), cellular metabolic process
(17%), nitrogen compound metabolic process (11%) and biosynthetic process (9%).
For cellular component, the intracellular (23%), intracellular part (20%),
intracellular organelle (16%), intrinsic component of membrane (16%) and
membrane-bounded organelle (14%) were those with higher abundancy. While most
of the SSR-containing unigenes were involved in the heterocyclic compound
binding (21%), organic cyclic compound binding (21%), ion binding (17%), small
molecule binding (11%) and carbohydrate derivative binding (9%) (Figure 2).
|
Figure 2 Gene Ontology analyses of 1,861 SSR-containing unigenes
of L. fructicosa transcriptome undergO classifications of biological process,
cellular component and molecular function |
Molecular function Cellular component Biological process
Pathway
prediction for SSR-containing unigenes was performed to gain an insight into
the biosynthesis of bioactive compounds in leaf of L. fruticosa. SSR-containing unigenes were assigned into 48 KEGG
pathways. The most represented pathways were metabolism, secondary metabolite
and signaling pathway (Table 6). Purine and thiamine metabolism
were the two most highly represented metabolisms among all, with 23 and 21
predicted SSRs respectively. These was followed by 13 predicted SSRs that
involved in aminoacyl-tRNA biosynthesis. There was a total of 12 SSR-containing
unigenes were assigned to pathways associated with secondary metabolite
biosynthesis which include isoflavonoid biosynthesis (3), zeatin biosynthesis
(3), diterpenoid biosynthesis (3), flavonoid biosynthesis (2), phenylpropanoid
biosynthesis (1) and ubiquinone and other terpenoid-quinone biosynthesis (1).
In addition, there were SSR-containing unigenes with functions related to cell
signalings such as T cell receptor signaling pathway (4), mTOR signaling
pathway (2) and phosphatidylinositol signaling system (2), respectively.
Table 6 Pathway predicted for SSR-containing unigenes via KEGG pathway analysis |
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Pathway Categories |
Pathway |
Number of SSR |
Metabolism |
Purine metabolism |
23 |
Thiamine metabolism |
21 |
|
Glycerophospholipid metabolism |
5 |
|
Glycerolipid metabolism |
4 |
|
Starch and sucrose metabolism |
4 |
|
Pyrimidine metabolism |
4 |
|
Drug metabolism-other enzymes |
3 |
|
Methane metabolism |
2 |
|
Cysteine and methionine metabolism |
2 |
|
Glycine, serine and threonine metabolism |
2 |
|
Glyoxylate and dicarboxylate metabolism |
1 |
|
Glutathione metabolism |
1 |
|
Cyanoamino acid metabolism |
1 |
|
alpha-Linolenic acid metabolism |
1 |
|
Galactose metabolism |
1 |
|
Drug metabolism-cytochrome P450 |
1 |
|
Retinol metabolism |
1 |
|
Ether lipid metabolism |
1 |
|
Phenylalanine metabolism |
1 |
|
Metabolism of xenobiotics by cytochrome
P450 |
1 |
|
Tyrosine metabolism |
1 |
|
Phosphonate and phosphinate metabolism |
1 |
|
Taurine and hypotaurine metabolism |
1 |
|
Inositol phosphate metabolism |
1 |
|
Ascorbate and aldarate metabolism |
1 |
|
Alanine, aspartate and glutamate
metabolism |
1 |
|
Fructose and mannose metabolism |
1 |
|
Arginine and proline metabolism |
1 |
|
Biosynthesis |
Aminoacyl-tRNA biosynthesis |
13 |
Biosynthesis of antibiotics |
5 |
|
Isoflavonoid biosynthesis |
3 |
|
Zeatin biosynthesis |
3 |
|
Diterpenoid biosynthesis |
2 |
|
Flavonoid biosynthesis |
2 |
|
Phenylpropanoid biosynthesis |
1 |
|
Ubiquinone and other terpenoid-quinone
biosynthesis |
1 |
|
Others |
Aminobenzoate degradation |
4 |
Th1 and Th2 cell differentiation |
4 |
|
T cell receptor signaling pathway |
4 |
|
Glycolysis/Gluconeogenesis |
2 |
|
mTOR signaling pathway |
2 |
|
Naphthalene degradation |
1 |
|
Oxidative phosphorylation |
1 |
|
Chloroalkane and chloroalkene
degradation |
1 |
|
Phosphatidylinositol signaling system |
1 |
|
Fatty acid degradation |
1 |
|
Pentose phosphate pathway |
1 |
|
Other glycan degradation |
1 |
4. DISCUSSION
Understanding of genetic variation in the germplasm, e.g determination of plant genetic
diversity by utilizing DNA molecular markers is the
prerequisite for crop improvement. Previous studies have identified the
application of Random Amplification of Polymorphic DNA (RAPD) markers from Annona species Anuragi
et al. (2016) and loquat Badenes
et al. (2004) of which the markers were utilized
for genetic diversity study. Nevertheless, massive data generated from
transcriptome sequencing has been reported to be comprehensive and useful
genomic and genetic resources that facilitate gene discovery and SSRs
development in future study (Zheng et al. (2013), Huang et
al. (2014), Chen et al. (2015). Compared to coffee (1/2.16 kb) Zheng et
al. (2013), marker density of L. fruticosa is lesser but appeared at a
much higher frequency than Arabidopsis
(1/14 kb), chickpea (1/8.66 kb), Jhanwar
et al. (2012). Cardle
et al. (2000) and cereal plants such as wheat
(1/15.6kb) and barley (1/6.3 kb) Kantety
et al. (2002). Different distribution frequency
of markers among plant species most probably due to composition and size of the
genome and also the criteria chosen to screen the marker.
This
study showed the major common repeat units were mononucleotide and followed by
di-nucleotide repeats which comprised 55.0% and 21.5% of the total SSRs
respectively. This class of SSR repeats (Mononucleotide) were found also abundant
in other plant species Jin et al. (2016) but for certain plant species di-nucleotide
repeat was the most Izzah et
al. (2014), Silva et
al. (2013), Zhang et
al. (2012). Dominant motif of
di-nucleotide repeats detected in this study was (AT/AG) n (48.9%) whereas (GAA/TTC) n (48.7%)
was the dominant motif of tri-nucleotide repeats. However, different dominant
motif detected in Dipteronia Oliver (Aceraceae)
and rubber tree which AG/CT and AAG/CTT as their dominant di-nucleotide trinucleotide
repeat motif respectively (Li et al. 2012; Triwitayakorn et al. (2011), Zhou et al. (2016)Zhou et al. 2016). Interestingly, GGGCAA motif was
considered as a rare motif for L.
fruticosa with a lower frequency of 3 in this present study.
Sequence
annotation showed that about 20.6% of SSR markers had significantly hits to the
NCBI database. However, hits percentage against the existing data in the
database are relatively low (20.65%). This unlikely due to the size of unigenes
as the average size indicated in the analysis was 1473 bp. Low homology percentage
could be probably contributed by certain genes that do not hit against database
upon blast or possibly matched to unknown proteins. Assumption made on the
basis that a scanty genetic information available for L. fruticosa and its close relatives in the current public
database. Similarity search analysis carried out on SSR-containing unigenes
clearly indicated that L. fruticosa has
a closer genetic distance with woody plant species such as Citrus sinensis (34%), Citrus
clementina (20%) and Theobroma cacao
(11%). These data suggested that some of genetic information of L. fruticosa such as unigenes and
SSR-containing unigenes obtained from this current study could be useful and
applicable to other woody plant species such as Citrus and date palm.
The
results of Gene Ontology (GO) analysis suggested that SSR-containing unigenes
of L. fruticosa’s leaf have
diversified biochemical functions. More interestingly, those a number of
detected SSR-containing unigenes have functions related to compounds binding
such as heterocyclic and organic cyclic compound binding. Many of the natural
plant secondary metabolites have cyclic compound structure that carry one or
more atoms that connecting each other to form a ring. Organic cyclic compounds
may contain carbon atom while heterocyclic compounds can be formed by a
combination of carbon and non-carbon atoms Smith
and March (2006). For instance, the compounds of
flavan-3-ols, proanthocyanins and flavanones contain heterocyclic C-ring Crozier
et al. (2007), a highly abundant SSR-containing
unigenes with binding functions to those compounds observed in L. fruticosa suggests that a majority of
detected SSRs are actively involved and being an essential component in
modulating the biosynthesis process of secondary metabolites.
KEGG
pathway analysis revealed the SSR-containing unigenes are highly associated
with purine and thiamine metabolism. This finding is correlated with Study by Suzuki
and Waller (1985) found that purine alkaloids were
abundantly available in the fruits of Camellia
sinensis L. and Coffea
arabica L. during fruit development stages. It is therefore suggested
that high purine content is likely to be observed among fruit bearing tree
plants including L. fruticosa which
is essential for purine alkaloid synthesis during fruit formation. Thiamine
diphosphate (vitamin B (1)) is known to be an enzymatic cofactor for various
metabolic pathways in central metabolism such as glycolysis, pentose phosphate
and the tricarboxylic
acid cycle Goyer
(2010). In addition, previous studies have shown that thiamine was a cofactor
involved to elicit the genes expression in phenylpropanoid pathways of
grapevine and those genes were associated with accumulation of phenolics,
flavonoids, lignin and stilbenes Boubakri
et al. (2013).
The above-mentioned
findings are in agreement with results obtained from KEGG pathway analysis,
where a number of SSR were detected in unigenes that involved in biosynthesis
pathways of secondary metabolites. Likewise, zeatin biosynthesis, diterpenoid
biosynthesis, terpenoid-quinone biosynthesis, flavonoid and isoflavonoid
biosynthesis were among those pathways represented in the secondary metabolite
biosynthesis pathways, well explaining that L.
fruticosa is a rich source of phytochemicals. This is supported by previous
studies which showed that leaf of Lepisanthes species contains various phytochemicals such as
proanthocyanidins Zhang et
al. (2016), alkaloids, terpenoids and flavonoids
Kuspradini
et al. (2012).
5. CONCLUSION
Study carried out was the first transcriptome sequencing of RNA sample extracted from leaf of L. fruticosa using NGS technology. Both EST-SSRs and NGS-based SSRs are functional markers identified from expressed transcripts of an organism, however application of NGS technology is comparatively a more reliable and higher throughput approach for novel SSR markers discovery in rapid, convenient and cost-effective manners than the traditional EST-SSRs identification processes. The present study has detected and characterized 23,958 microsatellite loci from 52,569 non-redundant unigenes. These data have substantially increased the existing genomic information of under explored plant species of L. fruticosa. Also, our data suggested that a high similarity of SSR-containing unigenes between L. fruticosa and date palm and citrus plants. These newly obtained genetic information will be useful for screening and profiling L. fruticosa accessions in particular for their secondary metabolites content. This will support future breeding program for L. fruticosa from under ultilized fruit to local food market as an enhanced nutritional food product.
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