Extract
Note: Please read the complete
full text with Figures and Tables at
Introduction
Traditional Chinese medicine (TCM) has been used for
thousands of years in China. It represents the collective
wisdom of the Chinese people to utilize nature for
survival[1]. With the advantages of multi-target effects, low toxicity and
the current appeal for more ＆natural＊ remedies, TCM has
become widely used as an alternative to treat various complex
and chronic diseases. Authentication of Chinese medicinal
materials is an old but important issue. With the
unprece-dented development of modern biological methods,
identification of species relationships using traditional anatomical
and physiochemical methods is being supplemented by DNA
fingerprinting techniques, such as random amplified
polymorphic DNA (RAPD) analysis.
The fruit of Lycium species, especially
Lycium barbarum, has been used in TCM to improve eyesight, protect liver and
kidney, and to replenish vital essence. The fruit of the
Lycium species are all red in color with very similar physical
appearance and anatomical structure. Chemical analysis methods,
such as high-performance liquid chromatography, have been
used for different species of Lycium, but have failed to
differentiate the Lycium species.
Recently, a Fourier-transform infrared spectroscopy
method was used to identify 7 species and 3 varieties of
Lycium[2]. The method provides a novel approach for the
identification and differentiation of plants used in TCM. Such
a technique can serve as a rapid, simple, reliable and
non-destructive analytical method for differentiating
Lycium species. Alternatively, with the development of modern
biotechnology, a polymerase chain reaction (PCR)-based
method using random primers (10-mers), known as
RAPD[3,4], has become another method for the study of
TCM[5,6]. RAPD analysis is simple and effective. It has many advantages,
such as it only requires a minute quantity of DNA; no prior
sequence data are needed, many primers can be used; and
the method is relatively simple. However, RAPD suffers the
drawback of low reproducibility, which has severely
hampered the popularity of this method. Usually, PCR products
are analyzed by agarose gel electrophoresis followed by
densitometric analysis of the DNA banding pattern. In the
present study, the lab-on-a-chip (Agilent 2100 Bioanalyzer
DNA 7500 assays, Agilent Technologies, Palo Alto, California, USA ) was used to analyze the PCR products of
Lycium samples, including 7 species and 3 varieties. After
preprocessing using the Agilent 2100 Bioanalyzer, including
baseline adjustment and alignment, data were exported as
XML files, which were imported into the Matlab software.
All further analyses were performed using Matlab. We will
clarify these analyses in detail in the following sections.
Materials and methods
Sample preparation Twelve Lycium samples were used
in this study. Dried fruits of Lycium were rinsed with 70%
ethanol and distilled water for surface sterilization. They
were then ground in liquid nitrogen with a mortar and pestle.
Information for the samples is summarized in Table 1. Samples
of Lycium barbarum, LB and LBB, were treated as positive
controls in the RAPD analysis. The remaining 10 samples,
which represent 7 species and 3 varieties, were used in our
data analysis.
Genomic DNA extraction The cetyl triethylammonium
bromide (CTAB) extraction method, a modified protocol of
Draper and Scott[7], was used for the extraction of DNA.
Lycium powder was mixed with 600 µL of 1℅CTAB extraction
buffer [50 mmol/L Tris-HCl (pH 8.0), 0.7 mol/L NaCl,
10 mmol/L ethylenediaminetetracetic acid (EDTA), 1% CTAB], which
was preheated at 60 ∼C. The mixture was then incubated for
30 min with occasional shaking. After cooling to room
temperature, the mixture was extracted with an equal volume
of chloroform/isoamyl alcohol (24:1, v/v). After
centrifugation at 13 000℅g for 15 min at room temperature, the
supernatant was collected and a 0.1 mol/L volume of 10% CTAB
solution (10% CTAB, 0.7 mol/L NaCl) and an equal volume
of chloroform/isoamyl alcohol were added for another
extraction. After centrifugation at
13 000℅g for 15 min at room temperature, the supernatant was collected and extracted
with an equal volume of CTAB precipitation buffer
[50 mmol/L Tris-HCl (pH 8.0), 10 mmol/L EDTA, 1% CTAB]. It was
then allowed to stand at room temperature for at least 1 h,
before being centrifuged again at 13 000℅g for 15 min at
room temperature. The pellet was resuspended in 400 µL
of 1 mol/L NaCl, which was pre-heated at 60 ∼C. For
further purification, it was extracted again with an equal volume of
chloroform/isoamyl alcohol. After centrifugation at
13 000℅g for 15 min at room temperature, the supernatant was
collected and 800 µL of cold absolute ethanol was added. The
solution was allowed to stand at -20 ∼C overnight for
precipitation of DNA. To recover the DNA, the suspension
was centrifuged at 13 000℅g for 20 min at 4 ∼C. The pellet
was collected and washed twice with 75% ethanol. After
that, the pellet was air-dried and resuspended in 30 µL
distilled water.
Estimation of DNA concentration and purity
The concentration and purity of DNA was checked by UV
spectroscopy at wavelengths 230 nm, 260 nm, and 280 nm. The quality of DNA (5 µL) was also checked by electrophoresis on a 1% agarose gel in 1×TBE (Total Binding Energy) buffer.
RAPD and optimization of PCR conditions The RAPD reactions were carried out as described by Nadeau et al[8] in a 25 µL reaction mixture containing DNA (10 ng每100 ng),
0.1 mmol/L dNTP, 25 pmol primer, 1×Taq buffer [20 mmol/L (NH4)2SO4, 75 mmol/L Tris-HCl (pH 8.8), 0.01% Tween-20,
2.5 mmol/L MgCl2, 0.001% gelatin (w/v)] and 0.5 U Taq DNA polymerase. The tubes were placed in the thermocycler (PTC-100TM, MJ Research Inc Waltham, Massachusetts, USA) and subjected to the following profiles: denaturation for 5 min at 94 °C, then 45 cycles at 94 °C for 1 min, 35 °C for 1 min, 72 °C for 2 min, final extension at 72 °C for 5 min. In order to optimize the RAPD conditions, the PCR conditions were varied using 100-fold, 500-fold, 1000-fold and 5000-fold dilutions of genomic DNA, 2 mmol/L or 2.5 mmol/L MgCl2, 1 U or 1.25 U Taq polymerase in the presence or absence of the final extension step. The PCR products were monitored by 1.5% agarose gel electrophoresis and the DNA fragments were visualized by staining with ethidium bromide. The amplification products (1 µL) were also analyzed with the lab-on-a-chip system, the Agilent 2100 Bioanalyzer, using the DNA 7500 assays kit according to the manufacturer＊s instructions.
Results
Preprocessing of raw data Raw lab-on-a-chip DNA fingerprints can be treated as multi-dimensional vectors or functional curves. Because they are usually observed and stored discretely, in the present study, the vector form was adopted to represent the DNA fingerprint. We let (ti, xi), i=1, ..., T denote the discrete observations for a DNA fingerprint, where xi is the fluorescence intensity measured at migration time ti and T is the number of migration time points. Customarily, fluorescence intensity is referred while ommitting migration time in representing DNA fingerprint, namely, (x1, ..., xT) represents a fingerprint, where a is the transpose of a vector for a.
A complete preprocessing for raw fingerprint data includes baseline adjustment, alignment, linear interpolation match and normalization issues. In order to eliminate the background and peak drift effect for DNA fingerprints, we used the up-to-date Agilent 2100 Bioanalyzer[9] software to adjust baseline and handle alignment based on the ladder, lower marker and upper marker. Figure 1 shows the alignment process.
After baseline adjustment and alignment, we exported the data into an XML format, which could be opened by Microsoft Excel. The XML files were then imported by the software Matlab, which was our main tool for further analysis.
It was easy to detect the lower marker and upper marker that
define the informative region for each fingerprint. Therefore,
we could concentrate on the observations between the lower
and upper markers. However, fingerprint data after baseline
adjustment and alignment was shown with relative
migration time, which was different from the unaligned data, that
is, fingerprint data with the same length of interval between
the lower marker and upper marker but with different number
of migration time points. Here linear interpolation was used
to match all of the fingerprints such that they had the same
number of migration time points between the lower marker
and upper marker. This method is called linear interpolation
match (LIM). Our main purpose for using LIM was to render
the fingerprints directly comparable. Another reason was to
make the data format suitable for our statistical analysis.
Details about the LIM method are described using Lycium samples as follows.
After exporting Lycium sample data from Agilent 2100
Expert software, the total 110 fingerprints produced by 11
primers and the corresponding number of migration time
points were denoted by
xi(k) and
nk,j,k=1, ..., 11; i=1, ..., 10.
Because these fingerprints shared the common interval
between the lower marker and the upper marker, we first chose
the minimum number {nk,j}, denoted as
m, and then made the migration time points the same for all of the other
fingerprints in the following way. We uniformly partitioned
m每1 cells on interval [0,1]. Similarly, for any fingerprint that had
s migration time points, we uniformly partitioned
s每1 cells on interval [0,1]. The total migration time points corresponded
to 0, 1/(s-1), 2/(s-1), ..., 1. For any
i/(s-1), 0<i<s-1, we found j
such that i/(s-1) located on an exclusive interval
[j/(m-1), (j+1)/(m-1)]. Liner interpolation of the fluorescence intensity value
at corresponding retention time j/(m-1) and
(j+1)/(m-1) was changed to the i-th fluorescence intensity value for the newly
matched fingerprint. Following the above rule, all of the
fingerprints were matched with the same number of migration
time points. After performing LIM, the common peaks of
both ends, which were parts of the lower and upper markers,
were removed as these peaks were of no concern. For
simplicity, the number of migration time points of these
fingerprints was still noted as T.
Because the outcomes of most statistical methods are
strongly influenced by the scale and range of the fingerprint
data, it was necessary to normalize all of the fingerprint data
before analysis. To accomplish this goal, the sample mean
and variance of the fingerprint
x=(x1, ...,
xT)＊ were calculated as follows:
 (1)
 (2)
All of the fingerprints were normalized by subtracting the sample mean and then dividing by the standard deviation,
namely the following form:
 (3)
The effect of this normalization is shown in Figure 2. From then on, fingerprint i referred to the registered fingerprint after normalization.
Primer sorting by the revealed fingerprint diver-sity DNA fingerprinting performed by RAPD using different primers may have had different detected diversities. The diversity of fingerprints obtained from a primer, what we call dispersivity, can be regarded as an index to indicate whether this primer is effective or not. As an example, for our tested DNA fingerprints of Lycium samples representing 7 species and 3 varieties, all DNA fingerprint data were denoted by:
X=(X(1), ..., X(11)),
where X(k)=(X1 (k), ..., X10(k)), k=1, ..., 11, k denotes k-th primer, and the total primer number 11, 10 denotes the total number of Lycium samples, namely, 7 species and 3 varieties. Xi(k) is a T-dimensional column vector that denotes the fingerprint of Lycium sample from species i obtained from primer k. T is the number of migration time points. Initially, the information about dispersivity revealed by primer k was contained in the data set X(k). Usually the more peaks in the data set X(k) obtained form primer k, the higher the dispersivity. However, that is not always true because the difference in peak location and the variation in peak height and peak shape among fingerprints obtained from primer k can also affect the corresponding dispersivity. It is necessary and important to integrate all of the factors in the evaluation of the dispersivity for each fingerprint set X(k).
In the present study, we used the concept of entropy, introduced by Shannon and Weaver[10], to quantify the dispersivity revealed by primer k. For a discrete probability distribution p, the motivated definition of entropy by statistical mechanics is:
 (4)
were . In the context of probability, h(p) is regarded as a measure of the information carried by p, with high entropy corresponding to much uncertainty. This uncertainty score reflects the property of dispersiity. Intuitively, it manifests itself as the way we describe dispersivity.
The dispersivity revealed by primer k is defined as the following way:
Step 1. Compute the sample covariance matrix
 where
 (5)
Step 2. Calculate the eigenvalues of S(k) and note them as l(k)=(l1(k ), ..., l10(k))'
Step 3. Make the sum of eigenvalues equal to 1, namely,
Setp 4. Compute entropy of l(k), namely,
h(l(k)) is defined as the
dispersivity revealed by the primer k. The definition of dispersivity is very intuitive. In the first
step, we used the covariance matrix S(k) to describe all of the
variant information for the fingerprints obtained from primer k. The variant information was then extracted by computing
the eigenvalues of S(k). These eigenvalues,
l(k), can be regarded as the information reconstructed from X(k) and can be interpreted by the principal component analysis (PCA). Sort
l1k, ...,
l10k as
l(1)(k) ³ ... ³ l(10)(k), for the fixed primer k, the closer to 1 for
which can be considered as the contribution rate for the first
principal component, the stronger for the first principal
component to synthesize the information of X(k). For example, if fingerprints
l1(k), ...,
l10(k) are very similar, it will result in the
contribution rate for the first component being close to 1
and the sum of the remaining 9 being near to 0, which means
that the first component can synthesize almost all of the
information. This case is not expected, because it reflects
little diversity for X(k). We desired that the contribution rates
for all of the components were scattered, not concentrated,
and allowed for the entropy, which has the property that the
more dispersive it is for the probability distribution, the larger
the corresponding entropy value. Finally, we defined the
dispersivity revealed by primer k as
Here all the
lj(k) denote the contribution rates subject to the
sum being equal to 1. From the definition of dispersivity,
some eigenvalues may be equal to 0, which will result in no
meaning due to the function. In such cases, we defined
0℅ln(0)=0, which is reasonable because l℅ln(l) will approach 0 if
l approaches 0 from the positive direction.
For our Lycium data, 11 primers were used to perform the
analysis. A direct question is which primer is the best one as
far as dispersivity is concerned? To some extent, the best
primer would be able to differentiate the
Lycium samples to the maximum degree. The dispersivity revealed by primer
k was given by
h(l(k)), simply noted as
D(X(k))=h(l
(k)), where
l(k) is dependent on
X(k). The best primer was chosen as the
one that maximized the value of
D(X(k)). Memory the best
primer and the corresponding dispersivity value as
S1 and DS1, respectively.
After choosing the primer S1, we then wanted to chose a
second primer from the remaining 10, such that the
fingerprints obtained from these 2 primers had the maximum
diversity. In order to do this, we had to define the dispersivity
of the 2 primers. For a striking example, if fingerprints
X(i) and
X(j) obtained from primers
i and j, respectively, were very similar, that would mean that primers
i and j almost revealed the same dispersivity. Furthermore, if primer
i revealed the maximum dispersivity and primer
j revealed the second maximum dispersivity, then primer i would be picked as the best
primer, while in most cases, primer j would not be selected as
the primer that reveals the maximum diversity combined with
primer i, because
X(i) and
X(j) have much mutual information.
This indicated to us that we should remove overlapping
information before choosing primers from the remaining 10.
The method of space projection was utilized to solve the
information overlap.
The dispersivity revealted by primers i and
j, on condition that primer i was chosen, was defined as
D(X(i),(1-X
(i)(X(i))
+)X(j)), where
A+ denotes the pseudoinverse of matrix
A[11] and can be numerically calculated by Matlab. The computation
of D(A,B) was similar to
D(A) except for substituting
A union B for A. For simplicity, we define
CP(X(i)|Xj
)=(1-X(i)(X(
i)+)Xj. Matrix
CP(X(i)|Xj
) was corthogonal to the space expanded by
X(i). That means that it contained extra information relative
to X(i). The eigenvalues of covariance matrix for
X(i) union
CP(X(i)|Xj
) were the union of eigenvalues of covariance
matrix for X(i) and
CP(X(i)|Xj
). For our Lycium data, on the condition that primer
S1 was chosen, the second primer cosen was he one that corresponded to the maximum value of
D(X(S1),CP(X
(S1)|X(k)),
kÎ{1,2, ..., 11}/{S1}, were
A/B denotes the set that includes the elements belonging to
A but not to B. Without loss of generality, the second primer chlosen is
recorded as S2, the corresponding dispersivity value is
denoted by
DS1,S2.
We could then obtain the order of primers, denoted
S1, ..., S11. The corresponding dispersivities for the primers
were denoted DS1, ...,
DS1, ..., S11. For illustrating our space
projection method, in Figure 3, we plot the original
fingerprint for primer S9, namely,
X(S9), extra information relative
to primer S1, namely,
CP(X(S1)|X
S9), and extra information relative to primers
S1, ..., S8, namely,
CP{X(S1),
CP(X(S1)|X
(S2)), ...,
CP[X(S1),
CP(X(S1)|X
(S2), ...,
CP(X(S1)|X
(S8))]}|X(S9)
. Comparing the plots in Figure 3, the information provided by primer
S9 gradually decreased as the mutual parts relative to primers
chosen earlier S1, ..., S8 were removed one by one. This
property can be interpreted by the projection theory and
Figure 3 is an intuitive manifestation.
Construction of the dendrogram for
Lycium samples From Figure 4, the x coordinate denotes the primers according to
the order in which they were chosen and the y coordinate
denotes the corresponding dispersivity. For example, 2
denotes primers S1 and S2, and the corresponding y-value is
DS1,S2, which shows that the dispersivity increases as the
newly chosen primer is added. Furthermore, the extent of the
increase becomes smaller and smaller. That means that
adding a primer can increase the amount of information available
while the last primer chosen provides less information than
the previous primer. Generally, the more primers chosen, the
better the conclusion drawn, such as in distinguishing and
constructing the dendrogram for our Lycium samples of 7
species and 3 varieties. However, because the last primers
chosen provided relatively little information, it was not
necessary to pick all 11 of the primers to construct the
dendro-gram. In the present study, we decided on the primer number
that contained almost all of the information to distinguish
and construct the dendrogram for Lycium samples using
argmin(DS1, ...,
Si)/(DS1, ...,
S11)>95%. For our Lycium samples,
(DS1, ..., S11)＊=(1.9476, 2.3398, 2.6624, 2.8600, 3.0690, 3.2071,
3.3133, 3.3998, 3.4380, 3.4611, 3.4772)＊. The number of primers
to use can be obtained by simple computation according our
proposed rule. Because, (3.2072/7.3772)=0.9223<0.95,
(3.3133/3.4772)=0.9529>0.95, 7 primers, S1, ...,
S7, were chosen for final analysis. We used the 95% rule to decide the number of
primers. Generally, in most data analyses (such as PCA) it is
possible to use other rules, for example, the 80% rule, to
decide the number of principal components. However, we
used the larger number 95% because our proposed
dispersi-vity is a sensitive index. This means that the dispersivity
changes only a small amount, while the fingerprint changes
much. It is therefore better to widen the boundaries for
choosing in case some important primers are lost.
In the present study, we used the hierarchical cluster
method to construct a dendrogram of Lycium species.
Because each Lycium sample was analyzed using 7 primers, the
primer information was merged by weighting. The weights
were assigned by
w1=DS1/DS1, ...,
S7, wk=(DS1,...,
Sk-DS1, ...,
S(k-1))/
DS1, ...,
S7, k=2, ..., 7. Using the obtained weights of primers,
the distance of each Lycium sample from species
i and j was defined by:
were
d(xi(k)
, xj(k))=1-r
2(xi(k),
xj(k)),
s(xi(k)
, xj(k)) and
s(xi(k)) are defined by (5) and (2).
r(xi(k),
xj(k)) is the correlation coefficient between
xi(k) and
xj(k), which is
often used to measure the similarity of fingerprint. In order
to use distance matrices to onstruct the dendrogram, we
transformed the data by
1-r(xi(k)
, xj(k))2
.
Unweighted pair-group method using arithmetic average
(UPGMA) linkage, which defines the cluster distance, is
advocated for constructing a dendrogram. Details about the
UPGMA clustering method can be found in Sokal and
Michener[12]. Using the above distance measure between
fingerprints and the UPGMA cluster method, we visualized
the cluster results using the dendrogram shown in Figure 5.
For evaluating our procedure to decide the number of
primers, we used primers S1, ..., S8 to construct the dendro
gram as above, further more, we use the first ordered 9, 10,
and 11 primers to construct dendrogram, respectively. All of
these were similar to Figure 5. This indicated that the first 7
primers contained almost all of the information provided by
the total 11 primers. Furthermore, when using primers S1, ..., S6, the outcome shows little difference relative to Figure 5
(Figure 6). It manifests our picking procedure for primer
number is so delicate.
Discussion
Phylogenetic relationship construction based on RAPD
fingerprint data generated from a number of primers was
investigated in this study. We proposed an index called
dispersivity using entropy theory to describe the diversity
of RAPD fingerprints obtained by several primers. Generally,
this index can be applied to other DNA fingerprint data, such
as arbitrarily primered PCR and DNA amplification
finger-printing, and to chromatographic fingerprints, such as
chromatogram, spectrum and mass spectrum. Based on this
index, the order for choosing primers is obtained using the
orthogonal projection method and entropy theory. Our
proposed method provides the guidelines for primer picking in
the consequent experiments. An on-line analysis program
on RAPD primer selection is provided on the web site:
http://math.nenu.edu.cn/shiz/yinxiaolin.files/program.htm, which
can be downloaded easily if required.
Acknowledgements
We are grateful to Dr Daniel KWONG (the Chemistry
Department of Hong Kong Baptist University) for his
valuable comments on the manuscript.
References
1 Chan K, Lee H. The way forward for Chinese medicine. London:
Taylor and Francis; 2002.
2 Peng Y, Sun SQ, Zhao ZZ, Leung HW. A rapid method for
identification of genus Lycium by FTIR spectroscopy. Spectrosc
Spect Anal 2004; 24: 679每81.
3 Welsh J, McCelland M. Fingerprinting genomes using PCR with
arbitrary primers. Nucleic Acids Res 1990; 18: 7213每8.
4 Williams CE, Ronald PC. PCR template-DNA isolated quickly
from monocot and dicot leaves without tissue homogenisation.
Nucleic Acids Res 1994; 22: 1917每8.
5 Shaw PC, Wang J, But PPH. Authentication of Chinese
medicinal materials by DNA technology. London:
Word Scientific Publishing; 2002.
6 Zhang AJ, Wong NS, Ha WY, Hu YH, Fang KT. Authentication
of traditional Chinese medicines using RAPD and functional
polymorphism analysis. In: Fang KT, Liang YZ, Yu RQ, editors.
Proceedings of the 1st Conference on Data Mining and
Bioinfor-matics in Chemistry and Chinese Medicines; 2003 Apr 6每16; Shenzhen, China; 2003. p 81每98.
7 Draper J, Scott R. Plant genetic transformation and gene expres-sion. London: Blackwell Scientific Publishing; 1988.
8 Nadeau JH, Bedigian HG, Bouchard G, Denial T, Kosowsky M, Norberg R, et al. Multilocus markers for mouse genome analysis: PCR amplification based on single primers of arbitrary nucleotide sequence. Mamm Genome 1992; 3: 55每64.
9 Agilent [http://www.home.agilent.com]: Agilent Technologies. 2100 Bioanalyzer Expert User's Guide; c2000-2005 [updated 2004 Mar 1; cited 2005 May 10]. Available from: http://www.chem.agilent.com/scripts/LiteraturePDF.asp?iWHID=36738
10 Shannon CE, Weaver W. The mathematical theory of com-munication. Urbana, IL: University of Illinois Press; 1949.
11 Rao CR, Mitra SK. Generalized inverse of matrices and its applications. New York: Wiley; 1971.
12 Sokal RR, Michener CD. A statistical method for evaluating systematic relationship. Univ Kans Sci Bull 1958; 28: 1409每38.
|
