Introduction

North East India is endowed with enormous biodiversity of flora and fauna. Clerodendrum is a large, complex, and diversified genus that encompasses well-established pharmacological properties and its importance of ethnomedical assets was reported in many indigenous systems of medicines1. Globally, 540 Clerodendrum species were distributed in tropical and sub-tropical regions that include small trees, shrubs, and herbs2. Approximately, 23 Clerodendrum species were found in India, while 18 species occur in North East India3. The family of Clerodendrum was moved from Verbenaceae to Lamiaceae based on circumscription of evolutionary boundaries through molecular evidences4. Based on morphological variations, authors classified the genus into distinctive subgenera like Clerodendrum and Cyclonema, also numerous species were described by more than one authors such as C. floribundum Hort and C. floribundum R.Br., C. foetidum Bunge and C. foetidum D.Don, etc.5,6. Therefore, DNA barcoding techniques could function as a molecular identifier for proper documentation and classification of Clerodendrum. DNA barcoding uses short standardized region of DNA sequence(s) (either nuclear or/and cytoplasmic genome) for rapid authentication of discrete species and cost-effective in nature7. Unlike animals, the mitochondrial genes were an unsuitable choice of barcode marker in plants due to its low nucleotide substitutions rates. Subsequently, numerous nuclear and plastid genes were leading the focus of researchers for identifying plant species8. So far, no consensus emerged as the universal barcode for land plants9. However, the multi-locus combination of barcode could enhance the potential discriminatory rates between closely related species10. To date, no authenticated report on the practice of DNA barcoding in Clerodendrum sp. was cited. In this study, we collected only 9 species of Clerodendrum from different locations of North East India, and the rest of the species were not encountered during the fieldwork as they were extremely rare and only known from a small number of locations.

The accessibility of DNA barcoding in its practical application was constrained due to its difficulty in retrieval of information via direct scanning of DNA sequences11. The DNA sequences contain long strings of characters that were not practicable for data input. To resolve the issue, we attempted to develop two dimensional QR code by encoding the DNA sequences of Clerodendrum, which could further help any non-taxonomist to easily recognize the species in the field through direct scanning of DNA QR code label via mobile devices. Further, this study could lead to valuable aid in the conservation of biodiversity strategies and the improvement of the genus.

Results:

Amplification and sequencing success

The efficient PCR amplification and sequencing were regarded as a critical indicator for evaluating the barcode candidates. In this study, the success rate of PCR amplification for four loci (ITS2, matK, rbcL and ycf1) were 100% and sequencing rates were maximum for matK (95.7%) followed by ycf1 (94.6%), ITS2 (93.6%) and rbcL (90.4%) respectively (Table 1). A total of 352 new sequences from 9 Clerodendrum sp. were submitted to NCBI that includes 88, 90, 85, and 89 sequences of ITS2, matK, rbcL, and ycf1. The submitted sequences were analysed together with retrieved sequences of NCBI and attained a sum of 432 sequences that consist of 118, 106, 119, and 89 sequences of ITS2, matK, rbcL, and ycf1.

Table 1 Assessment of four barcodes and its combinations:
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Characteristic analysis of each barcode locus

The ambiguous terminal sequences were deleted from the aligned sequence. The length of aligned sequences for each locus and combination of locus were ranged from 307 bp of ITS2 to 2455 bp of ITS2 + matK + rbcL + ycf1. Among the single locus, matK had the maximum variable and parsimony-informative characters followed by ITS2. ITS2 + matK had maximum variability and parsimony informative sites (264/244) among the combinational locus (Table 1). In this study, the mean inter-specific distances were much higher than intra-specific distances. The pairwise intra-specific distances among the fifteen barcodes ranged from 0.0 to 0.044 ± 0.004 and the mean intra-specific distances was maximum for matK (0.021 ± 0.001) and least for rbcL + ycf1 (0.001 ± 0.000). Subsequently, the pairwise inter-specific distances were ranged from 0.0 to 0.151 ± 0.005 and the mean inter-specific distances was highest for matK (0.086 ± 0.005) and least for matK + rbcL (0.011 ± 0.003) (Table 2). In precise, matK reveal the highest mean intra- and inter-specific distances.

Table 2 Summary of the pairwise intra-specific and inter-specific distances in Clerodendrum genus.
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DNA barcode gap analysis

Fundamentally, an ideal barcode should show significant “barcode gap” that defined the spacer region between the range of inter and intra-specific divergences12. The existence of barcode gap were evaluated at a class interval of 0.005 distance units between inter and intra-specific divergences. Among the fifteen barcodes, significant barcode gap was observed in the plastid gene matK, nucleotide locus ITS2 and ITS2 + matK with the least overlap values, whereas the other genes revealed the unclear gaps with overlapped of intra- and inter-specific distances (Fig. 1).

Figure 1
figure 1

Distribution of intra- and inter-specific Kimura 2-parameter (K2P) distances among all Clerodendrum samples for the four barcodes loci and their combinations.

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Species discrimination

For discriminating species using TaxonDNA, ITS2 + matK had the highest success rate for correct identification of species (Best match: 96.11%; Best close match: 96.11%; All species barcodes: 84.50%) followed by matK, ITS2, ITS2 + matK + ycf1, and rbcL + ycf1 had the lowest discriminatory rate (Best match: 36.34%; Best close match: 36.34%; All species barcodes: 28.78%) (Table 3).

Table 3 Species identification based on the ‘best match’, ‘best close match’ and ‘all species barcodes’ with TaxonDNA software.
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Phylogenetic analyses

The barcode loci were analysed with BI, ML and NJ phylogenetic trees and generated similar discriminatory results with reliable clade support. The PP (Posterior Probability) values based on BI tree were higher than the bootstraps values of ML and NJ trees. The rate of discriminatory success for single and multi-locus barcodes were estimated based on percentage of species resolution for each species and determined to be monophyletic. Both the single and multi-locus barcodes showed different levels of species discrimination varying from 33.3 to 93.2% (Table 4). Amongst the single locus, matK (BI-91.6, ML-91.6, NJ-91.6) followed by ITS2 (BI-84.6, ML-84.6, NJ-84.6) showed relatively high levels of discriminating success rates, whereas rbcL (BI-60.2, ML-55.2, NJ-59.6) had lowest level of discriminations. Combination of both ITS2 and matK resolved maximum success rate of discrimination (BI-93.2, ML-91.9, NJ-93.2) as compared with other combinatorial loci of barcodes. Hence, it could be concluded that species discrimination was high when matK was included among other combinations.

Table 4 Species discrimination rate of all barcodes loci in Clerodendrum species.
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The phylogenetic tree of ITS2 + matK was reconstructed with BI method and nodal support value of ML and NJ as depicted in Fig. 2. In the phylogenetic tree, Clerodendrum species were well separated from outgroup and considered to be monophyletic. The phylogenetic tree was divided into 9 clades with moderate to high bootstraps and PP supports values. The Clade 1 consists of C. colebrookianum with 0.71 of BI support and 100% of ML and NJ bootstrap. In Clades 3, 4, 6, 7, 8, and 9 form clear individual clusters for species of C. infortunatum, C. indicum, C. thomsoniae, C. philipinum, C. inerme, and C. serratum. The species of C. crytophyllum and C. canescent in Clade 2 (BI-1.00, ML-73%, NJ-89%) and C. japonicum and C. paniculatum in Clade 5 (BI-1.00, ML-100%, NJ-100%) were grouped together in each clade which signified that they were closely related.

Figure 2
figure 2

Phylogenetic BI tree inferred from ITS2 + matK region of Clerodendrum sp. Result for ML and NJ bootstrap analysis were mapped onto BI tree. The node number indicates BI/ML/NJ values. BI with PP > 0.5, ML and NJ with bootstrap > 50% were shown. The scale bar corresponds to 0.8 substitutions per 100 nucleotide positions.

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Thus, we tentatively proposed ITS2 + matK gene with significant barcode gap and strong discriminatory power could be the preeminent barcode for Clerodendrum species.

Two-Dimensional DNA barcode generation

At present, “DNA barcode” refers to the DNA sequences which were inadequate for storing data, recognition, and information retrieval. This could be resolved with the two-dimensional QR codes that could represent DNA barcode sequences efficiently. The ITS2 + matK barcode marker of Clerodendrum species were transformed into QR codes with a motive to benefit the diverse researchers with no prior knowledge of DNA barcoding (Fig. 3).

Figure 3
figure 3

DNA sequence based QR code for species represented as (A) C. colebrookianum, (B) C. infortunatum, (C) C. philipinum, (D) C. inerme, (E) C. indicum, (F) C. serratum, (G) C. thomsoniae, (H) C. paniculatum, (I) C. japonicum.

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Discussion

An efficient barcode should be easily amplified, sequenced and resolve with high species discrimination and identification13,14. The four barcode markers used in this study were the universal plant markers with suitable length and cost effective15. In the present study, we analysed both the sequences of Clerodendrum sp. and repository sequences of GenBank records. Among the four-barcode locus, matK and ycf1 produces high quality of sequences as compared to ITS2 and rbcL. Among the markers, matK performed the best with high species resolutions and clear barcoding gaps followed by ITS2. The efficacy of matK was also supported in previous researches as core barcode for many plants genera due to its high amount of variability and results in a high rate of molecular evolution as compared to the other barcode coding regions16,17. Moreover, ITS2 was considered as a complementary marker to the core barcodes18,19 and many studies had reported its high rate of variability in discriminating the species20,21. Many researchers proposed ITS2 as a standard marker for identification of more than 6,600 plant specimens from 753 genera and universal barcode for medicinal plant species22,23.

The plastid gene ycf1 was recently proposed as an effective barcode marker in angiosperms due to its high amount of variability24. This gene was reported to be a probable phylogenetic marker for plants like pines, orchid, etc.25. Simultaneously, the chloroplast coding region rbcL was proposed as a universal primer for ferns, mosses, and angiosperms7,26. But in some recent studies, rbcL was reported to be incongruous barcode marker due to its low inter-specific variations even in closely related species20,27. Conversely, in this study both the markers restrained the lowest number of variation site, parsimony informative sites and species discriminating rates. The complexities of these chloroplast markers prevent discrimination of species, as it represents only the maternal inheritance variation28. Thus, it could be suggested that ycf1 and rbcL region were not suitable for DNA barcoding in Clerodendrum species.

Several combinations of two, three and four barcodes were analysed in this study. The combination of matK + rbcL was proposed to be the universal barcode for all the land plants by CBOL in 2009, but in this study, it possesses lowest species resolutions among all the combinatorial barcode markers due to its low substitution rates. In contrast, the combination of ITS2 + matK represents the highest percentage of species resolution with clear barcode gaps as compared to both single and combination of markers and also relate similarities with the previous findings29,30.

The barcoding gap that exists between the highest intra-specific value and the lowest inter-specific value could depict the limits of species variation within a genus and a threshold limit of species can be set31,32. Overlaps of the threshold value signify cryptic species and probably show insignificant variation with the barcode. Among the single and multi-locus barcode, matK and ITS2 + matK posses’ clear barcode gap compared to the other barcode markers. The statistics of best match, best close match and all species barcodes using TaxonDNA was used to evaluate the rate of species identification9,33 and observed that ITS2 + matK followed by matK posses high rate of species discriminations. Based on phylogenetic tree methods, ITS2 + matK specifies maximum rate of species resolution in Clerodendrum. Similar levels of resolvability by different tree-methods were reported in Lamiaceae34.

In recent trends, DNA barcode encounters limitation in its practical applications due to the lack of information compression and retrieval of information through direct scanning of DNA sequences35. Therefore, an easy innovative format and rapid retrieving barcode information is in need. Barcode technology was well established in manufacturing and retailing industries for a couple of decades. The QR code contains meaningful information in both vertical and horizontal direction more than the data carried by vertical lines of barcodes (stores maximum of 20 digits). This technique could detect symbols that lead to a specific product. If this technology was applied to represent the sequences of DNA barcodes then it could lead to efficient retrieval of information with the largest coding capacity and high compression ratio, as reported by Liu et al. In this study, ITS2 + matK barcode sequences for 9 Clerodendrum sp. were converted to QR codes with vivid sequence information. The QR code could monitor the different species of Clerodendrum from its origin even in the field; ensure the mislabelling and safety of its commercial product.

Hence, the barcode marker ITS2 + matK could be used as superlative locus to determine the species boundary in Clerodendrum. Optimization of these results for all the species of Clerodendrum was not advisable as this study was constraint to North East region of India but this could lay the foundation for the universal use of DNA barcoding in plants. The success rate of species identification would be more confirmed if more species were included further36. Therefore, a potential solution for identifying species based on geographical location and sampling size should be further investigated. In the upcoming years, these findings would be potentially helpful in delineating the large genus of Clerodendrum.

Methods

Sample collection and genomic DNA extraction

A total of 94 samples from 9 species of Clerodendrum were collected from different locations of North East India. The numbers of collected samples for each species of Clerodendrum were depicted in Fig. 4. Tender leaf samples were collected and lyophilized at − 110 °C for 48 h. Genomic DNA for the collected samples were extracted using modified CTAB method37. The quantity and quality of the extracted DNA were evaluated in Bio-spectrophotometer (Eppendorf, Germany), analysed in 0.8% agarose gel electrophoresis and visualized in gel documented system (G:BOX, Syngene, U.K.).

Figure 4
figure 4

Graphical representation of collected Clerodendrum samples.

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PCR amplification, sequencing and sequence download

The extracted DNA samples were amplified with ITS2, matK, rbcL, and ycf1 in polymerase chain reaction (PCR) system (Applied Biosystem). The PCR mixtures (20ul each) contained 10 ng of template DNA, 10X PCR buffer with 1.5 mM of MgCl2, 2.5 mM dNTP, 1 unit/uL Taq DNA polymerase, 5 pmol of each primer and adjusted the final volume with nuclease free ddH2O. The PCR conditions for each selected barcode primer were listed in (Table 5). For bidirectional sequencing, the amplified products were sent to Eurofins Genomics India Private Limited Company using the same primers to resolve ambiguities.

Table 5 Details of primer used in the study.
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Additionally, we retrieved all the sequences (ITS2, matK, rbcL and ycf1) of Clerodendrum from NCBI database. The downloaded sequences were filtered based on the criteria that: (i) sequence length less than 300 bp and (ii) sequences lacking specific voucher names. According to our survey, some species contain less than five sequences in NCBI while some species had maximum number of sequences for a specific barcode region. Therefore, the representatives for each species were restricted between five to eighteen samples. The taxa, voucher names and accession number used in this study were provided in (Table S1).

Data analysis

The sequences of each barcode were aligned with MUSCLE (https://www.ebi.ac.uk/Tools/msa/muscle) and edited manually in BioEdit v7.1.3.038. For ITS2 region, the sequences were subjected to Hidden Markov Model (HMM) to remove the conserved 5.8Sand 28S DNA sequences39. The edited sequences were compared with available nucleotide sequences of GenBank database and submitted to NCBI and BOLD databases with project code-NECLE (Table S2). The analyses of genetic pairwise distances were computed in MEGA X40 with Kimura-2-parameter (K2P) model. The K2P was considered as the most favourable model for small distance calculations41. Differences between intra- and inter-specific distances with four single barcodes were evaluated using pairwise distance matrix in MEGA X software. An ideal barcode could be determined with the presence of barcoding gap, that compared the intra- and inter- specific distance distribution for each barcode candidate with an interval distance of 0.05 in TaxonDNA with ‘pairwise summary function’42. In TaxonDNA, best match, best close match, and all species barcodes functions were intended to examine the accurate identification proportion of each barcode. The ‘Best match’ analyses determine the closet adjoining match for a known sequence. If the examined sequences were from the analogous species then the identification was considered correct whereas incorrect if the sequences belong to different species29.

Phylogenetic analysis

The species discriminatory efficacy of each single and multi-locus barcode candidates was assessed with three tree-based method, which include the Neighbour-joining (NJ) tree, Maximum likelihood (ML) tree and Bayesian inference (BI) tree. The NJ methods of all markers were conducted using MEGA X43,44. The reliability of node was supported by bootstrap test of 1,000 pseudo-replicates with K2P distance parameter. For ML analysis, the phylogenetic trees were constructed in RAXMLGUI v1.3.1, a graphical front-end for RAXML45. The clade support was assessed using ML with thorough bootstrap analyses, run 10 times starting from random seeds under GTRGAMMA model and 1,000 non-parametric bootstrap values46. The species forming separate clusters in the tree with bootstrap support > 50% were considered to be distinct. The analysis of BI trees was conducted in MrBayes v3.2.747. The best substitution models of each locus were selected according to Akaike information criterion (AIC) with jModeltest version 2.1.748. The model suggested by jModeltest was GTR + I + G for all the tested barcode except GTR + G model for matK. The two replicate runs of Markov chain Monte Carlo (MCMC) were run for 5,000,000 generations with four simultaneous chains (one cold and three hot chains), and trees were sampled at every 1000th generations. The adequate posterior probability (PP) distribution of samples were determined, when the split frequency of average standard deviation was lower than 0.01. Subsequently, the stationary was determined in Tracer v1.7.149 and the first 25% trees were discarded as burn-in and a 50% majority-rule consensus tree was constructed and PP was considered as node support values. All the topologies of trees were visualized in FigTree v1.4.4. Percentages of species resolutions were calculated from the reconstructed tree in order to resolve the monophyletic nature of the clades. Callicarpa americanawas used as an outgroup.

Generation of QR code

The two-dimensional QR code consists of black modules with three squares on the corner of the code on white background and could involves 7,089 numeric, 4,296 alphanumeric characters, and 2,953 bytes of binary data50. In this study, the QR code image for the candidate barcode marker was generated by DNA QR Code Web Server35.