Seven distinct variants of C. ternatea were collected from a wild population in Sleman, Indonesia, each exhibiting different petal architecture and colors differences. Genomic DNA was extracted from 100 mg of frozen leaf samples with the Genomic DNA Mini Kit Plant (Geneaid, Taiwan). The concentration and purity of the DNA were evaluated using a NanoDrop Lite Plus spectrophotometer (Thermo Fisher Scientific, USA), with acceptable purity from 1.8 to 2.0 (Green & Sambrook, 2012).

PCR amplification was performed using the MyTaq HS RedMix (Bioline, US) protocol with several modifications. The total reaction volume was 20 µL, comprising 10 µL of MyTaq HS RedMix, 2 µL of primer, 2 µL of DNA sample at 50 ng/µL, and 6 µL of nuclease-free water. The amplification process was conducted in a T100 Thermal Cycler (Bio-Rad, US) over 40 cycles. The total reaction volume was 20 µL, consisting of 10 µL of MyTaq HS RedMix, 2 µL of primer, 2 µL of DNA sample (50 ng/µL), and 6 µL of nuclease-free water. Amplification was performed using a T100 Thermal Cycler (Bio-Rad, US) with 40 cycles. Each cycle involved a 45-second denaturation step at 95 °C, a 30-second annealing step at 45 - 50 °C, and a 1-minute elongation step at 72 °C. The protocol began with an initial denaturation at 95 °C for 3 minutes and ended with a final extension at 72 °C for 5 minutes. A universal ITS primer pair was employed, comprising ITS-u1 (5’-GGAAGKARAAGTCGTAACAAGG-3’) and ITS-u4 (5’-RGTTTCTTTTCCTCCGCTTA-3’)(Cheng et al., 2016). Details of the eighteen ISSR primers used are provided in Table 1.

The PCR products were separated by horizontal agarose gel electrophoresis on a 2% agarose gel (1st BASE, Singapore) stained with FloroSafe DNA stain (1st BASE, Singapore). Electrophoresis was carried out in 2% TBE buffer at 50 volts for 60 minutes. A 100 bp DNA ladder (Geneaid, Taiwan) was run simultaneously to estimate the molecular weights of the amplified products. The stained gels were subsequently visualized and documented with a GelDoc-UV transilluminator.

Sanger sequencing was analyzed at LPPT UGM, and the contig sequences were edited using GeneStudio v.2.2.0.0 (GeneStudio, Inc.). The cleaned sequences were subsequently analyzed using a BLAST search against the NCBI database. OPAL, integrated with MESQUITE (version 3.61), was used to align the contig sequences and accession sequence data retrieved from GenBank. Genetic distances were determined using MEGA-X (Tripathi et al., 2013), and multiple sequence alignments were visualized with the MultAlin web- based software.

ISSR are dominant markers that provide bi- state (presence-absence) assessments (Shiran et al., 2007); (Bishoyi et al., 2014). The recorded gels were scored for the absence (0) and presence (1) of bands, without considering band intensity, since each band occurrence with a specific molecular weight represents a locus (Ali et al., 2013). Band interpretation was performed using CorelDRAW software to define the operational taxonomical unit (OTU). The formula D = 1 − F, where F is the species similarity matrix value, was used to compute genetic distance (D) (Hillis et al. 1996) . The Multi- Variate Statistical Package (MVSP) program was then used to create phenetic dendrograms based on the similarity matrix.

The flowers of C. ternatea exhibit a range of morphological variations, including petal architecture and colors differences ( (Bishoyi et al., 2014); (Suarna & Wijaya, 2021)). Based on petal arrangement, we identified two different types: single and double petalloid (Figure 1). In terms of color, there were four variations: white, light blue, light purple, and purple (Figure 1). Petal colors have been recorded in several studies (Yeotkar et al., 2011); (Ali et al., 2013); (Bishoyi et al., 2014). Chemically, petal color corresponds to its phytochemical composition, particularly anthocyanins (Jeyaraj et al., 2022), with no correlation to kaempferol content (Ali et al., 2013). Variations in anthocyanin concentration may influence the intensity of purple pigmentation, creating a color gradient ranging from white and blue to deep purple.

One of the most often utilized genetic markers in plant phylogenetics and DNA barcoding is the ITS region of nuclear ribosomal DNA ((Poczai & Hyvönen, 2010); (Cheng et al., 2016)). Due to its high sequence-level variability, conserved flanking regions, and quick coordinated evolution under comparable functional constraints, this region is helpful for resolving connections within genera and below species level ((J et al., 2002); (Poczai & Hyvönen, 2010); (Zhao et al., 2018)).

The ITS sequences obtained from seven genotypes of C. ternatea were aligned to identify the types of mutations present. The alignment revealed two types of mutations: insertions/ deletions (indels) and substitutions. An indel was observed at 56th nucleotide position (Figure 2), where an additional nucleotide base T was present in the sample from Manipal, Karnataka, India, whereas the other samples exhibited a deletion at this position. Regarding substitution mutations, three specific changes were identified: A/G378, T/C396, and A/T491, which distinguished the sample from Udupi, Karnataka, India from the others (Figure 2 & Figure 3).

These findings suggested a degree of genetic variation among the analyzed genotypes, particularly in samples from Karnataka, India, which exhibited both indels and substitutions that differentiate them from other populations. The presence of an additional T nucleotide in the Manipal sample and the three substitution mutations in the Udupi sample indicated localized genetic divergence, possibly due to geographic isolation, environmental adaptation, or selective pressures.

Conversely, the identical ITS sequences observed in the seven genotypes and the São Paulo, Brazil samples suggested a high level of genetic conservation in this region, implying a shared evolutionary lineage or limited genetic differentiation over time. This could be due to recent common ancestry, low mutation rates in the ITS region, or historical seed exchange and cultivation practices that maintain genetic uniformity.

Moreover, ITS sequence data for C. ternatea remains highly limited in GenBank, restricting comprehensive comparative analyses. Nevertheless, an alignment-based analysis of all available ITS sequences, despite reducing the overall sequence length, revealed no genetic distance among the examined accessions (data not shown). This further reinforces the hypothesis that the ITS region in C. ternatea is highly conserved across different populations. Further phylogenetic studies incorporating additional molecular markers and broader geographic sampling would be necessary to better understand the genetic diversity and evolutionary history of this species. The sequence data from this study has been submitted to GenBank and assigned accession numbers PQ198055–PQ198061 for WS, LBS, LPS, PS, LBD, PD, and WD, respectively.

The universal ITS primer (ITS-u1/ITS-u4) employed in this study amplifies the ITS1-ITS2, and is suitable for over 95% of plants groups (Cheng et al., 2016). Numerous studies have already proven the efficiency of ITS in explaining phylogenetic connections and revealing interspecific genetic diversity within the Fabaceae ((Ainouche & Bayer, 1999); (J et al., 2002); (, 2010); (Wu et al., 2013); (Xu et al., 2017)).

In terms of their application in elucidating intraspecific relationships, ITS regions can reveal sufficient sequence divergence to assess genetic diversity in Korean native Lilium ((Sultana et al., 2011)), while ITS2 has been reported to show significantly higher intraspecific divergence in discriminating Chinese Zanthoxylum species (Zhao et al., 2018).

Nevertheless, some research findings indicated that ITS markers exhibit low intraspecific genetic diversity. This is exemplified by the Thar Desert endemic population of Commiphora wightii (Haque et al., 2009), as well as populations of Aquilaria malaccensis and Aquilaria hirta in Peninsular Malaysia, where no intraspecific variation was detected (Lee et al., 2018). Therefore, ITS markers are not linked to genes encoding morphological traits, further reinforcing the lack of correlation between morphological and genetic variation found in this study. The absence of ITS polymorphism among C. ternatea genotypes suggested a high level of sequence conservation, indicating that other genetic regions, such as functional genes or regulatory elements, may be responsible for the observed morphological differences. We overcame this obstacle by using ISSR molecular markers, which are essential for evaluating genetic stability in diverse breeding lines and for describing genetic variation both within and between species ((Porth & El-Kassaby, 2014); (Aristya et al., 2019); (Hasan et al., 2021); (Yusuf et al., 2023)).

With an average of nine fragments generated from each of the eighteen ISSR primers, a total of 162 fragments with sizes ranging from 188 bp to 1583 bp were obtained (Table 2). A total of 62 polymorphic fragments were found among them, with an average of 3.61 polymorphic fragments for each primer. Each primer has a different number of polymorphic bands, ranging from 1 to 14. Specifically, the primer UBC-836 displayed eight monomorphic bands and no polymorphic bands, indicating a 0% polymorphism rate (Table 2). ISSR-6 had the largest polymorphism information content (PIC) value, measuring 93.33%, and a resolving power (RP) value of 10.00. This demonstrates the primer’s usefulness in genetic variation research and its capacity to identify C. ternatea genotypes. The 18 ISSR primers produced an RP value of 3.23 and a PIC value of 0.12 on average.

The effectiveness of ISSR depends on the compatibility of each primer sequence with the target genome. The bands produced reflect the presence of specific microsatellites in the genome and guide the amplification process to generate inter-microsatellite regions. It is highly probable that different amplified bands can be produced using the same ISSR primer sequences. For instance, similar ISSR primers studied by (Vijayan & Chatterjee, 2003) in mulberry produced fewer scored bands compared to our study in C. ternatea, though both were lower than in Mucuna pruriens (Devi et al., 2024). This is consistent with the low amplification results produced by UBC-807, UBC- 808, UBC-841, UBC-842, UBC-873, and UBC-880 in our study. The percentage of polymorphism is also influenced by the sample size, which correlates with the extent of the analyzed gene pool. Such variations were also observed in many Fabaceae species during ISSR-based analyses ((F et al., 2016); (, 2020); (Ojuederie et al., 2020); (Qahtan, 2021); (Reddy et al., 2022); (Aliabadi et al., 2023); (Helal et al., 2023)).

Identification based on ISSR markers in different populations of C. ternatea produced Jaccard’s coefficient similarity index from 0.80 to 0.98 (Bishoyi et al., 2014). In our study, the use of the informative primer ISSR-16 enhanced the ability to identify genetic variation among C. ternatea genotypes. Three identical repeated primer sequences—(AG)8C, (AG)8G, and (CT)8G—were observed in this study, Bishoyi et al. (2014), and Nurhasanah et al. (2023). The observed results for the repeated motifs (CT)8G and (AG)8C by Bishoyi et al. (2014) were closely related to our study in terms of polymorphism rate, resolving power, and PIC values. The PIC value of (AG) ₈ C, around 0.20, was also similar to Nurhasanah et al. (2023). For the motif (AG) ₈ G, we identified a higher polymorphism rate and PIC value, although the RP value was lower than Bishoyi et al. (2014) and Nurhasanah et al. (2023). Another method for analyzing genetic variation is the use of Simple Sequence Repeats (SSR), which can provide allelic information among genotypes (Gebhardt, 2007). SSR analysis has clearly validated the existence of allelic differences between blue and white flower color genotypes, based on the markers CcM2763 and CcM0830 (Naik et al., 2020).

The dendrogram plots constructed from 18 ISSR primers did not reveal specific clades, instead showing random plotting due to the very low genetic variance among C. ternatea genotypes. Two methods for measuring similarity, Jaccard’s (SJ) and simple matching (SSM) coefficients, produced similar results (Figure 4).

The red line in Figure 4, indicates the lowest similarity percentage among the white single genotype compared to the other genotypes, with 73% and 76% similarity according to SJ and SSM, respectively. A higher similarity percentage, ≥ 80%, was observed among genotypes marked with a blue line in Figure 4. The highest similarity percentage among C. ternatea genotypes was between the white double and purple double genotypes, with similarity values of 96% and 97% for SJ and SSM, respectively.

According to Jaccard’s coefficient, the greatest genetic distance was observed between the white single (WS) and purple single (PS) genotypes, with a value of 0.297 (Figure 5A). This was followed by genetic distances between white single (WS) and the light purple single (LPS), purple double (PD), white double (WD), and light blue single (LBS) genotypes, with values of 0.290, 0.284, 0.277, and 0.243, respectively. A similar trend was observed when using the Simple Matching Coefficient, though the dissimilarity values were generally lower compared to those derived from Jaccard’s method (Figure 5B).

ISSR markers are dominant and arbitrary, amplifying random regions of the genome without targeting specific genes responsible for morphological traits. As a result, the genetic variation detected does not necessarily correspond to the observed morphological differences. This lack of correlation suggests that while phenotypic variation in C. ternatea may be influenced by genetic factors, it is likely regulated by specific loci that are not captured by ISSR markers. Additionally, environmental factors and epigenetic modifications could contribute to the observed morphological diversity, further decoupling it from the genetic profiles revealed in this study.