Keywords: Portulaca speciesspecies identificationDNA barcodesITStrnL-Frbcla
Morphological features have been the basis of species identification for the past 250 years (Hebert & Gregory, 2005), using features such as stems, fruits, and flowers (Waldchen et al., 2018). However, difficulties in species identification arise when different species share similar morphological characters (Jinbo et al., 2011), or members of the same species look different due to phenotypic plasticity (Sdouga et al., 2018).
Species misidentification has been reported among Portulaca species, for example, the 5th. Edition of the Sunset Western Garden Book (October 1988) mislabelled
There are limited studies on barcoding of this genus. Sdouga et al. (2018) worked mainly on
Misidentification creates problems for conservationists, ecologists, and various types of agencies that deal with food safety, and invasive plants (Hebert & Gregory, 2005). DNA barcoding to supplement morphological identification is suggested (Thompson & Newmaster, 2014). Among the reasons for incorporating DNA based methods in species identification, is that molecular identification can be more efficient, as shown by a taxonomic survey where 202 species were identified using molecular data, but only 142 species were identified using morphology (Thompson & Newmaster, 2014). The molecular identification not only identified more species but was at the same time, 37% less expensive (Thompson & Newmaster, 2014). However, DNA barcoding sometimes fails in species identification (Percy et al., 2014; Stallman et al., 2019). Part of the reason for failure could be the influence of geographical locations of the sample. In
The question then is, will the DNA sequences of ITS1, ITS2,
Purpose of the Study
The study aimed to;
characterise three Portulaca species found in Malaysia using leaf and flower morphology
determine if the nuclear (ITS1, ITS2) and chloroplast (rbcl, trnL-F) loci can be amplified, sequenced, and analysed using the Basic Local Alignment Search Tool (BLAST) and maximum likelihood tree topology, to corroborate the identify or discriminate the morphologically delimited Portulaca species.
Morphological characterisation, followed by molecular characterisation and their comparison was carried out, as explained in the next sections.
Sample selection, morphological characterisation, and identification
Nine samples were collected in Negeri Sembilan, three from each of the morphologically different Portulaca species. The samples were morphological characterised based on observations made by the naked eye and under a light microscope.
DNA extraction, Amplification and Sequencing
DNA was extracted using the modified cetyltrimethylammonium bromide (CTAB) method (Doyle & Doyle, 1987). Polymerase Chain Reactions (PCR) using My Taq™ Mix (Bioline, USA) were performed according to manufacturer’s protocols using primers and thermocycling conditions reported for rbcl (Kress et al., 2009), trnL-F (Taberlet et al., 1991), ITS1(Cheng et al., 2015), and ITS2 (Chen et al., 2010). PCR products confirmed by agarose gel electrophoresis were purified and sequenced at MyTACG Bioscience Enterprise.
The analysis used consensus sequences obtained using DNA Sequence Assembler version 5.15 (2018) or if unavailable, uni-directional reads. Fragment length and GC content obtained using the MEGA X Software (Kumar et al., 2018), if consistent with reported values indicate the authenticity of loci (Buckler & Holtsford, 1996). Authenticity is also indicated by a lack of stop codons in the reading frame of the coding locus rbcl and was determined using the Barcode of Life Database (BOLD) (Ratnasingham & Hebert, 2007). Sequence homogeneity in evolution and substitution saturation were determined using the Disparity Index (ID) in MEGA (Kumar & Gadagkar, 2001) and the Iss statistics, in the DAMBE Software (Xia & Xie, 2001).
To identify and differentiate species we used the BLASTN (Altschul et al., 1990) and created a maximum likelihood tree using MEGA X (Kumar et al., 2018). We assigned identities based on the highest score obtained when the sequences were queried using the BLAST (via (http://blast.ncbi.nlm.nih.gov/Blast.cgi). In the case of ITS2, the ITS2 database was used to assign species name based on a BLAST which uses structure and sequence (Merget et al., 2012).
The maximum likelihood tree was generated using sequences aligned with t-Coffee (Di Tommaso et al., 2011) according to the best-fit nucleotide substitution model selected based on the Akaike information criterion (AIC) calculated in MEGA X (Kumar et al., 2018). Sequences clustered in monophyletic clades in the tree enable species discrimination (Fazekas et al., 2008). Node support evaluated with bootstrapping (BS) (Felsenstein, 1985), was interpreted as giving relatively reliable support of the relationship when BS is between 70 and 85 and strong support when BS is more than 85 (Kress et al., 2002).
Provisional morphological identification
The plant samples were assigned to the Portulaca genus, diagnosed by the contracted head-like inflorescence and the fruit’s dehiscent top portion shed intact as a lid (Nyffeler & Eggli, 2010). Morphology provided temporary species assignments to
The temporary identification to
The second species was first inferred to be in the Pilosa clade, based on conspicuous leaf axillary hairs (Ocampo et al., 2013), and vascular bundles arranged in a peripheral ring with the water storage cells located in the central part (Figure
PCR and sequencing success rate
Barcoding depends on successful amplification and sequencing. This study found a 100% success in amplification, but sequencing was not always successful. Three
Detection possible source of noise by sequence characterisation
The length and percentage GC (Table
BLAST analysis of all loci supported the identification of
The species morphologically identified as
The species morphologically identified as
Inferences from tree topology, in general, coincide with the deductions from BLAST. There is confirmation that the
The ITS1 tree (Figure
The ITS2 based tree (Figure
All loci, using either BLAST or the tree topology, identified the genus correctly. Secondary structure-based BLAST analysis and tree topology of ITS2 confirmed the morphological identification of all Portulaca species. As the standard BLAST of the same sequences could not identify any of the species, the method of sequence analysis had a detectable influence on identification. Identification — Whether it is? Or it isn’t?, currently, cannot depend solely on DNA barcoding, and needs the support of morphology. Morphology based identification, however, can be simplified when DNA barcodes narrow the scope of search from among the estimated 369,000 species of flowering plants currently known, to say within the genus of a few hundred species. In the study, the lack of a complete database impacted the ability to identify species, and the database needs to be further populated to provide coverage of all species, and also reflect the within-species diversity. Also, because taxonomist cannot agree on characters to discriminate species, different names may be given to the same species such as
INTI International University funded this study but had no role in study design, data collection, analysis, or preparation of the manuscript.
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Yee, F. J., Ping, Y. H., Fathimath, S., & Selvarajah, G. (2020). It Is? It Is Not? Identifying Portulaca by Morphology and DNA Barcodes. In N. Samat, J. Sulong, M. Pourya Asl, P. Keikhosrokiani, Y. Azam, & S. T. K. Leng (Eds.), Innovation and Transformation in Humanities for a Sustainable Tomorrow, vol 89. European Proceedings of Social and Behavioural Sciences (pp. 339-352). European Publisher. https://doi.org/10.15405/epsbs.2020.10.02.31