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Articles |
Divisions of
1 Psychological Medicine and
2 Medical Genetics, University of Wales College of Medicine, Heath Park, Cardiff CF4 4XN, UK.
3
Stanford DNA Sequencing and Technology Center, Palo Alto, CA 94304.
a Address correspondence to this author at: Department of Psychological Medicine, University of Wales College of Medicine, Heath Park, Cardiff CF4 4XN, UK. Fax 44 (0)1222 747839; e-mail odonovanmc{at}cardiff.ac.uk
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionAppendix AReferences |
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Methods: To maximize the relevance of our data for other investigators, we have screened 42 different amplimers from CFTR, TSC1, and TSC2. The samples consisted of 103 unique sequence heterozygotes and 126 wild-type homozygous controls.
Results: At the temperature recommended by the software, 96% (99 of 103) of heterozygotes and all of the wild-type controls were correctly classified. This compares favorably with sensitivities of 85% for single-stranded conformation polymorphism and 82% for gel-based heteroduplex analyses of the same fragments.
Conclusions: Software-optimized DHPLC is a highly sensitive method for mutation detection. However, where sensitivity >96% is required, our data suggest that in addition to the recommended temperature, fragments should also be run at the recommended temperature plus 2 °C.© 1999 American Association for Clinical Chemistry
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionAppendix AReferences |
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In our previous rigorously controlled blind analysis, DHPLC emerged as a highly sensitive and specific technique for detecting mutations (3). However, one of the main problems with successfully applying DHPLC is that the column temperature at which it is undertaken has to be chosen with care for each different PCR product. In previous studies (1)(3), the column temperature was selected empirically as the temperature at which the DNA product of interest was eluted from the DHPLC column ~1 min earlier in the analytic gradient compared with nondenaturing conditions. However, although highly sensitive analyses are achieved with this method, there are several major disadvantages inherent to this procedure. First, the use of operator judgement introduces a variable into the analysis that makes formal assessment of sensitivity across laboratories impossible. Second, the optimizing procedure, although brief, requires direct operator input, thus impeding automation and throughput. Third, some fragments have multiple melting domains, a fact that is easily ignored by the empirical temperature selection procedure.
In response to these concerns, we (N.H. and P.O.) have developed DHPLCMelt software to predict the optimal temperature for DHPLC. This is freely available at website http://insertion.stanford.edu/melt.html. We have also undertaken an analysis of the utility of this software for detecting mutations, using a blind study design. Furthermore, we have compared the sensitivity of software optimized DHPLC with single-stranded conformation polymorphism (SSCP) and heteroduplex analyses. To maximize the relevance of our data for other investigators, we have screened 42 different amplimers from three genes containing a broad spectrum of mutations.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionAppendix AReferences |
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i,int that the ith base pair is
closed given that the two strands are associated. The probability
i that the ith base pair is closed
is then the product of the associated site probability
i,int and the probability
ext that the strands are associated (i.e.,
i = i,int.·
ext). Details of the calculation of
ext and i,int are
given in Appendix A.
The site melting temperature Ti is
defined as the temperature at which the site is closed in 50% of
fragments. Thus, Ti is found by
calculating all i at a range of temperatures
and determining the temperature at which i =
0.5 for the site, to an accuracy of 0.5 °C. The RTm is then the
highest Ti in the fragment, and if the
range of Ti values is >5 °C, it
recommends running the fragment a second time at a temperature
5 °C lower.
parameters for the DHPLCMelt PROGRAM
The calculation of the probability of association
qext requires three parameters:
K, a, and b, whose meanings and usage
are discussed elsewhere (9). It should be noted that
K is not the equilibrium constant for dissociation, as might
be suggested by its notation.
The values K = 5000, a = -3.2, and
b = -2.8 have been shown empirically to give accurate
results for other systems (8). In our calculations, the
melting temperatures were largely insensitive to the values of these
parameters because site melting was driven more by internal
Watson-Crick bond breakage than by entire strand dissociation. Because
of this, we used these parameters without modification. In addition,
based on previous results (8), the loop entropy
function
The calculation of the values for i,int
requires Watson-Crick bonding enthalpies 
HATand HGC, an entropy of
bond breaking S, a mean cooperativity
parameter 
1/2, and 10 stacking free energies
GstM,N. By
fitting to DHPLC data for 12 polymorphisms detected between 55 and
59 °C on four different BRCA2 exons, values of
HAT = -7940 cal/mol
HGC = -9030 cal/mol
and
S = 25.2 cal/mol K
were obtained. We believe the enthalpy values are smaller in
absolute magnitude than published values (8) because of the
acetonitrile in the mobile phase. Additionally, a value of =
0.0003985 was used (8), and the stacking free energies were
taken from Gotoh and Tagashira (10).
Two additional modifications were made to the program after the
implementation of the described model with the given parameters. When
the program gave consistently high Ti
temperatures for sequences melting above 60 °C (probably because of
inadequate use of data in that range when fitting our parameters), the
decision was made to lower all Ti
temperatures above 60 °C according to the formula:
 |
In addition, upon recalibration of the ovens in the laboratory of P.O., all temperature predictions were adjusted downward 1 °C because the oven temperatures were actually 1 °C higher than those indicated on the column oven display.
computer usage
The computer usage time required to calculate the site
probabilities increases at a rate relative to the length of the DNA
segment squared, and the memory requirements are linearly
related to the length of the segment. A web query to calculate the
melting profile of an 800-bp segment of will usually run in <1 min.
samples
Two hundred twenty-nine PCR samples representing 42 different
amplimers of the cystic fibrosis transmembrane conductance regulator
gene (CFTR), and tuberous sclerosis complex TSC1
and TSC2 genes were supplied by A.J. and J.C. The samples
consisted of 103 heterozygotes and 126 wild-type controls. The DHPLC
analysts were blind to the nature and number of heterozygotes present
in the sample, although a wild-type control for each amplimer was
known. The mean size of fragments containing sequence variants was 308
bp (range, 173–630 bp). The sequence variants were 69 single-base
substitutions, 12 single-base insertion/deletions, 8 two-base
insertion/deletions, and 14 insertion/deletions of 
3 bases. All
mutations and polymorphisms have been described previously
(11)(12)(13), and full details of the heterozygote genotypes are
available at http://www.uwcm.ac.uk/uwcm/mg/tsc_db/ dhplc.html.
In the first phase, 65 samples from nine different amplimers were run at (a) the recommended temperature (RTm), (b) the RTm + 3 °C, and (c) the RTm - 3 °C. Those samples that appeared heterozygous at one or more of the temperatures were run at a series of column temperatures (range, RTm ± 5 °C) to detect the range of temperatures at which heterozygous status could be detected.
These data were used to determine the proportion of the DHPLC-detectable heterozygotes that could be detected at each temperature relative to the recommended temperature. In turn, these data were used to define the temperatures for the prospective phase, using 164 samples. These were the RTm and the RTm + 2 °C.
pcr
Genomic DNA was prepared from peripheral blood samples by standard
methods. PCR was carried out in 50-µL reaction volumes containing 100
ng of genomic DNA, 0.5 µmol/L primers, 0.2 mmol/L dNTP, 10 mmol/L
Tris, pH 8.3, 50 mmol/L KCl, 1.5 mmol/L MgCl2,
0.1 g/L gelatin, and 1U of AmpliTaq Gold Polymerase (Amersham
Pharmacia Biotech). Cycling conditions were 94 °C for 10 min,
followed by 32–33 cycles of 54–58°C for 1 min, 72 °C for 1 min,
94 °C for 30 s, and a final step of 72 °C for 10 min. Primer
sequences, product sizes, and annealing temperatures for amplification
of CFTR, TSC1, and TSC2 exons have
been published previously (11)(12)(13). Although DHPLC is
generally performed on crude PCR products, the products must be free of
oil contamination. Unfortunately, all PCRs had previously been
optimized on equipment requiring a mineral oil overlay to prevent
sample evaporation (11)(12)(13); therefore, to avoid the need
for reoptimization of the PCR reactions for thermocyclers with heated
lids, all PCR reactions were performed under mineral oil, which was
then removed using a QiaPCR purification kit (Qiagen Ltd) according to
the manufacturers specifications. This procedure does not alter the
resolution of heteroduplexes (Fig. 1
), but as the primer and dNTPs are removed by
purification, the size of the large early peak attributable to these
reagents is reduced. Fig. 1
was generated after peer review. Although
the column was identical to that used in this study (DNASep;
Transgenomic), our laboratory had converted from Rainin equipment
(below) to a WAVETM DHPLC instrument
(Transgenomic), and therefore, the gradients are slightly different
from those used for all other analyses presented.
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dhplc, sscp, and heteroduplex analysis
DHPLC was performed on a PEEK/Titanium HPLC system (3)
purchased from Rainin. Between 5 and 10 µL of crude PCR product was
loaded on a DNASep column (Transgenomic) and was then eluted from the
column by an acetonitrile gradient in a 0.1 mol/L triethylamine acetate
buffer (TEAA), pH 7, at a constant flow rate of 0.9 mL/min. The
gradient was created by mixing eluents A (0.1 mol/L TEAA, 0.1 mmol/L
Na4EDTA) and B (250 mL/L acetonitrile
in 0.1 mol/L TEAA). Eluted DNA fragments were detected with a
Dynamax UV-C detector (Rainin). Column temperature was controlled using
a Rainin column heater model CH-1. Oven temperatures were selected
using the software available at
http://hardy-weinberg.stanford.edu/dhplc/melt.html (Ver. March 1998)
and are listed at http://www.uwcm.ac.uk/uwcm/mg/tsc_db/dhplc.html.
The temperatures given here refer to direct measurements of column
temperature, using a thermocouple. Analytic gradients were 3.5 min long
with linear increments of reagent B at a rate of 1.8%/min. After each
analysis, the column was cleaned with 95% reagent B for 40 s and
reconditioned for 40 s with eluent containing reagent B at a
proportion 5% less than the starting percentage for the analysis.
After injection, the proportion of reagent B was increased over 30
s to the start percentage of reagent B. Gradients were chosen
empirically to elute the PCR products 1.5–3 min into the analytic run.
To asses the relative performances of DHPLC, SSCP, and heteroduplex analysis under a single set of conditions, the sensitivity of DHPLC using just the RTm was compared to SSCP and gel-based heteroduplex analysis performed under our standard assay conditions (12).
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionAppendix AReferences |
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phase 1
Of the 65 samples analyzed in phase 1, 32 were heterozygotes. The
proportion of heterozygotes that could be detected at each temperature
relative to the RTm is given in Table 1
. The median number of temperatures at which heterozygosity
could be detected was 8, the mode was 6, and the range was 4–11.
Because we did not analyze samples beyond 5 °C on either side of the
RTm, these values represent minimum values. Indeed, 25 of 32
heterozygotes were still detectable at either the highest or lowest
temperatures studied. Only one sample was detectable at less than six
different temperatures, and this sample was still detectable at the
highest temperature. There was no temperature relative to the RTm that
allowed all variants to be detected (Table 1
). The temperatures
relative to the RTm that allowed the most efficient mutation detection
were the RTm and the RTm + 1 °C, each of which allowed 31 of 32
variants to be detected. However, all heterozygotes could be detected
using a combination of the column temperatures RTm and RTm + 2 °C.
Thus, for the phase 2 of the study, both of these temperatures were
used.
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phase 2
Of 164 samples in phase 2, 71 were heterozygotes. All but three
could be detected at the RTm. The three heterozygotes that were not
detected at the RTm were, however, resolved at the RTm + 2 °C.
Conversely, five heterozygotes could not be detected at the RTm +
2 °C. However, all of these were easily resolved at the RTm.
comparison of dhplc, sscp, and gel-based heteroduplex analysis
Of the 103 heterozygotes assayed, 99 (96%) were detected at the
RTm by DHPLC, 88 (85%) were detected by SSCP, and 84 (82%) were
detected by gel-based heteroduplex analysis under optimized conditions
(see Materials and Methods and Table 2
). Of the 69 single-base substitutions, 66 were detected at the
RTm by DHPLC, whereas only 58 and 51 were detected by SSCP and
heteroduplex analysis, respectively. The two 2-bp deletions that SSCP
failed to detect were in 630-bp fragments.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionAppendix AReferences |
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To best determine the most efficient and most sensitive mutation
detection strategy, our study had two phases. In phase 1, fragments
were run at multiple temperatures to determine the temperature range at
which the heteroduplexes could be detected and also the temperature
relative to the RTm that allowed the highest proportion of
heteroduplexes to be detected. Our data from this phase of the study
suggest that ~97% of different heteroduplexes can be detected at the
RTm. However, for 100% detection, our results suggested that this
should be supplemented by a run at the RTm + 2 °C. Note that these
temperatures relative to the RTm are "optimal" in the sense that
they allow the greatest proportion of heteroduplexes to be identified,
not in the sense that resolution of a given heteroduplex is maximal.
This is illustrated in Fig. 2
, which clearly shows that some of the
heteroduplexes are better resolved than others.
Although the DHPLC laboratory remained blind to the true nature of the samples (homozygote or heterozygote), it was not possible to retain blindness between chromatograms of the same sample taken at different temperatures; therefore, although the DHPLC operators rated each chromatogram separately, we cannot exclude the possibility that the raters utilized information from other chromatograms, which might lead to an inflation of the apparent sensitivity of DHPLC at any given temperature. For this reason, a second prospective study was performed based on the above recommendations, i.e., running samples at the RTm and the RTm + 2 °C.
The prospective phase of this study confirmed the findings of the first phase, i.e., all heterozygotes could be detected using two temperatures. Furthermore, use of the RTm suggested by the program enabled 68 of 71 heterozygous individuals (96%) and all homozygous individuals to be correctly designated. These values compare favorably with SSCP and heteroduplex analysis. Using these methods, we detected 85% of variants by SSCP, whereas 82% could be detected by heteroduplex analysis. However, because most of the sequence variants we have screened in this study were originally detected by SSCP or heteroduplex analysis, our estimate of the sensitivities of these two methods is likely to be inflated. Consequently, we are likely to have underestimated the superiority of DHPLC over these two commonly used methods. Of course, there are other highly sensitive methods (other than sequencing) for mutation detection, e.g., denaturing gradient gel electrophoresis (14) and its derivative technique constant denaturant gel electrophoresis (15). Although we have not performed a direct comparison between DHPLC and these methods, our results indicate that the sensitivity of DHPLC is comparable. However, unlike denaturing gradient gel electrophoresis, DHPLC is automated and does not require expensive gas chromatography clamps, labor-intensive optimization, or the production of gels; it therefore is a favorable alternative for high sensitivity mutation detection in both research and diagnostic environments.
This study therefore confirms previous work showing that DHPLC is a highly sensitive and specific method for detecting unknown sequence variation. More importantly, we have also shown that available software allows all variants to be detected without empirical optimization of the running conditions. This is important for several reasons. First, removing the uncertainty about selection of optimal temperature on the basis of operator judgment will allow researchers to empirically assess the sensitivity with which a particular DHPLC comparative sequencing study has been performed in their own and other laboratories. This is particularly important in situations in which candidate genes for diseases are "excluded" or in diagnostic applications. Second, the removal of optimization steps is a further move toward full automation and will allow an increase in throughput. Third, the removal of an optimization step will allow relatively less experienced personnel to "service" the DHPLC apparatus, a factor that is also important for high-throughput applications.
It is not clear why some of the fragments were not detectable at the
RTm. Three of the undetected mutations were single-base changes (C
T,
TG, and CT) and one was a single-base insertion (ins T). The
fragment sizes containing the undetected variants were 253, 291, 425,
and 477 bases. Only two of these fragments were larger than the mean
fragment size (308 bases), all were detectable at a higher temperature,
all amplimers contained other variants that were detected, and eight of
eight variants in fragments larger than the largest of these were also
detected. Therefore, it does not seem likely the fragment size is
responsible for our failure to detect these variants at the RTm. In
addition, all of the undetected mutations were in melt sites calculated
to be within 1 °C of the RTm; therefore, the location of
mutations in melt sites that are quite different from the RTm
cannot be responsible. The most likely possibility is that the software
does not allow for some local variables that might stabilize
mismatches, e.g., hairpin loop formation (N. Hansen and P. Oefner,
unpublished observation), but this is the subject of further modeling
and we hope to incorporate this in future versions of DHPLCMelt.
It is important to note that the temperatures we describe in this study are the measured column temperatures rather than those indicated by the oven. In the case of the Rainin instrument, we found that over the range of temperatures used in this study, the column temperature was 1 °C lower than indicated by the oven, whereas the measured column temperature in the WAVE (Transgenomic) DHPLC instrument in our laboratory was as indicated by the oven display. We would, therefore, recommend that to take advantage of our data, other researchers measure the column temperature achieved by their particular instrument.
Based on the results of this study, we would make the following recommendations regarding the optimal strategy for applying DHPLC. For most applications, e.g., the generation of single nucleotide polymorphism maps and most candidate gene studies, analysis at the RTm is most appropriate because sensitivity at the RTm is ~95%. However, when 100% sensitivity and specificity are required, e.g., when a gene has already been identified as pathogenic, analysis at the RTm and the RTm + 2 °C is recommended.
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Appendix A |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionAppendix AReferences |
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The values for 
are updated at each step using:
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The recursive relation for the site probabilities
1,int begins with:
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The values for 
and µ must also be updated using the
equations:
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Finally, ext is calculated using the
equations:
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Acknowledgments |
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Footnotes |
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References |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionAppendix AReferences |
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