Transcript DNA barcoding in medicinal plants: Testing the potential of a

Zunera Shabbir
PhD 1st semester
Department of Botany
PMAS AAUR
DNA barcoding in medicinal plants: Testing the potential of a
proposed barcoding marker for identification of Uncaria
species from China
About 5 to 50 million plants and animals are
living on earth, out of which less than 2
million have been identified. In recent years
new ecological approach called DNA
barcoding has been proposed to identify
species and ecology research.
DNA barcoding, a system for fast and
accurate species identification which will
make ecological system more accessible.
DNA barcoding is a taxonomic method that
uses a short genetic marker in an organism's
DNA to identify it as belonging to a
particular species.
History
DNA barcoding first came to the attention
of the scientific community in 2003 when
Paul Hebert’s research group at the
University of Guelph published a paper
titled "Biological identifications through
DNA barcodes".
• The short DNA sequence is taken from standard region of
genome to generate DNA barcode. DNA barcode is short
DNA sequence made of four nucleotide bases A
(Adenine), T (Thymine), C (Cytosine) and G (Guanine).
• Each base is represented by a unique color in DNA
barcode as shown in figure. Even non experts can identify
species from small, damaged or industrially processed
material.
There are an estimated 300,000 plant species in
the world (IUCN, 2012) but relatively few of
these can be identified based on traditional plant
identification methods.
Accurate classification and identification of this
large number of species remains a significant
challenge even for specialist taxonomists.
The emergence of DNA barcoding has had a
positive impact on biodiversity classification
and identification (Gregory, 2005).
DNA barcoding is a technique for
characterizing species of organisms using a
short DNA sequence from a standard and
agreed-upon position in the genome.
(http://barcoding.si.edu/DNABarCoding.htm).
Schematic timeline of plant barcoding history and possible
developments. CO1, cytochrome c oxidase 1; cp, chloroplast; ITS,
internal transcribed spacer.
• The genus Uncaria, belongs to the family Rubiaceae, is mainly
distributed in tropical Asia and Australia, with a distribution of
12 species in China. All 12 Uncaria species, known as “Gouteng”, have long been used in traditional Chinese medicines
(Yu et al., 1999).
• Consequently, the reserves of mainstream species have
significantly decreased, and the market is becoming more and
more irregular, facing issues such as adulteration, substitution
and lower-quality products selling at top-quality prices (Yu et
al., 1999).
Uncaria rhynchophylla
• Many
Uncaria
plants
are
morphologically
similar,
making
identification of their origin species
difficult.
• A reliable and quick method for the
identification of Uncaria species needs to
be developed for their further research
and utilization.
• In this research paper, they have chose
five DNA regions (rbcL, matK, trnHpsbA, ITS and ITS2) that have
previously been proposed as DNA
barcodes to evaluate and verify the
feasibility of authenticating Uncaria
medicinal plants and establishing a
suitable DNA barcoding protocol for
them.
Taxon sampling
• Ten of 12 (according to the Flora of China, 2013) species of
Uncaria were collected from the field, individual cultivators
and botanical gardens.
• Multiple samples of each species were collected to ensure that
both the morphological and geographical ranges of each taxon
were covered. The total number of samples was 54 and all
corresponding voucher samples are in the Herbarium of the
Yunnan branch of IMPLAD (Institute of Medicinal Plant
Development, Chinese Academy of Medical Sciences).
DNA extraction, PCR amplification and sequencing
•
•
•
•
Total DNA was isolated from silica gel-dried leaves
following the manufacturer's instructions for the
Plant Genomic DNA Kit (DP305, Tiangen Biotech
Co, China). Specific DNA fragments were
amplified via standard polymerase chain reaction
(PCR).
The primers synthesized for PCR and sequencing
were as described by Chen et al. (2010), and the
reaction conditions are presented in Table.
PCR amplification was performed in 20 mL
reaction mixtures containing 10-30ng of genomic
DNA template, 1 PCR buffer with 1.5 mM MgCl2,
0.2 mM of each dNTP, 0.2 mM of each primer and
1.5 U Taq DNA Polymerase.
The PCR products were purified using PCR
Cleanup Kit (AXYGEN, USA) and sequenced in
both directions with the primers used for PCR
amplification on a 3730XL sequencer (Applied
Biosystems, USA).
In this study, they initially tested five potential DNA barcode sequences,
including psbA-trnH, ITS, ITS2, matk, and rbcL, but during the further
analysis procedure, the matk was ignored because of the low PCR
amplification efficiency.
DNA sequence data analysis
• To ensure sequencing accuracy, the raw sequencing results
were corrected and assembled using CodonCode Aligner 3.0
(CodonCode Co., USA). Low-quality sequences (according to
Chen et al., 2010) and primer sequences were deleted.
• For ITS2, they used Hidden Markov Models (HMMs) to
remove possibly contaminated sequences from fungi (Keller
et al., 2009). Candidate DNA barcodes were aligned by
Clustal X V2.0.
• The output data were processed to calculate the Kimura-2Parameter (K2P) distances for each region using MEGA v4.1
(Kumar et al., 2008). The neighbor-joining (NJ) method
(Saitou and Nei,1987) was selected for the construction of
phylogenetic trees.
A total of 1000 bootstrap replicates were calculated for the NJ tree
construction (Saitou and Nei, 1987). To compare inter-specific
divergence and intra-specific variation, average inter-specific distance
and smallest inter-specific distance were calculated to characterize interspecific divergence, average intra-specific distances and coalescent
depth were calculated to determine intra-specific variation using a K2P
distance matrix, and the software utilized was TaxonDNA 1.0 (Meier et
al., 2006).
The methods of species identification is Best Close Match (Meier et al.,
2006). To increase the species identification accuracy of the potential
barcodes, the sequence data deposited in the DNA database of GenBank
was used.
Results
1. Efficiency of PCR amplification, sequencing
and sequence character analysis
• The sequence lengths range from 219 to 719 bp. The shortest locus is
ITS2, at 219-222 bp. The average GC content of each locus is also
different, the highest one is ITS2 (reaching up to 66.2%), and the
lowest is psbA-trnH (with a content of 27.0%).
• The efficiencies of PCR amplification and success rates of sequencing
are important indicators for the evaluation of DNA barcodes.
• In this study, the efficiency of PCR amplification, from high to low, is
100.0% (ITS2), 70.4% (rbcL), 66.7% (ITS), and 64.8% (psbA-trnH).
• For the success rate of sequencing, the four loci are all 100.0%. Based
on the efficiency of PCR amplification and the sequencing, the most
suitable DNA barcode for Uncaria is ITS2.
2. Assessment of barcoding gap
An ideal DNA barcode, the distributions of genetic variation
between intra- and inter-specific should be as distinct and nonoverlapping as possible.
The results (data not shown) showed that: for three candidate
DNA barcodes (ITS, rbcL, psbAtrnH), there was significant
overlap in the distributions of intra- and inter-specific variation.
Only ITS2 exhibits distinct gaps between the distributions of
intra- and inter-specific variation that could be useful for the
authentication of Uncaria plants.
Neighbor-joining trees produced from pairwise Kimura 2-parameter distances of
ITS2 for analytical specimen. Numbers on branches represent NJ support
values (%).
Conclusion
• An ideal DNA barcode should meet the following criteria:
1. The inter-specific genetic variability and differentiation is obvious, and
the intra-specific divergence is inconspicuous;
2. The sequence is short enough and easy to be amplified and sequenced;
3. The candidate DNA barcode should have conserved regions that are
convenient for the design of universal primers.
• In the study we assessed the potential of five proposed DNA regions
(ITS2, ITS, rbcL, matk and psbA-trnH) for differentiating Uncaria
species, the results showed that the ITS2 region is most appropriate as a
DNA barcode.
• For matk, difficulties in PCR amplification and sequencing are the most
significant problems faced as a candidate DNA barcode. The results
showed that matk had the lowest amplification (only 37.0%) and
sequencing efficiency. Therefore, the matk region is not suitable for
identifying Uncaria, even though it has been suggested as a core DNA
barcode for plants (CBOL PlantWorking Group, 2009).
• Among the other four candidate DNA barcodes that we tested,
although three regions (ITS, rbcL, and psbA-trnH) had higher PCR
amplification (66.7%, 70.4%, 64.8%, respectively) and sequencing
efficiency (all were 100.0%), the authentication efficiencies were
lower as a result of intra-specific divergences were similar to or
exceeded the interspecific variation.
• The ITS2 region has been proposed as a universal DNA barcode for
plant and animal identification in previous studies (Chen et al., 2010;
Yao et al., 2010). In this study, ITS2 showed 100.0% amplification
and sequencing efficiency, the interspecific and intra-specific
variations were moderate and the inter-specific variation was higher
than intra-specific divergence.
• In addition, among the five tested candidate DNA barcodes, ITS2
showed the highest ability of species identification on the basis of the
Best Close Match (94.4%) and tree methods.
• The results also indicated that the higher authentication ability of ITS2
between the Uncaria species means that this region has a greater
potential as a barcode than other candidate DNA barcodes.
Reference:
Zhang, Z. L., Song, M. F., Guan, Y. H., Li, H. T., Niu, Y.
F., Zhang, L. X., & Ma, X. J. (2015). DNA barcoding in
medicinal plants: Testing the potential of a proposed
barcoding marker for identification of Uncaria species
from China. Biochemical Systematics and Ecology, 60, 814.
Li, X., Yang, Y., Henry, R. J., Rossetto, M., Wang, Y., &
Chen, S. (2015). Plant DNA barcoding: from gene to
genome. Biological Reviews, 90(1), 157-166.