| May
1, 2005 |
| By:
Chris
Park, Devan
Correll, Christian
Jurinke, Paul
Oeth |
| Pharmaceutical
Discovery |
|
Allele-specific expression is an important link between individual
genetic variation and disease (1, 2). Comparing allele-specific
expression levels between groups, such as affected vs. unaffected,
provides a basis for identifying disease susceptibility alleles and
genes. To facilitate this process, quantitative methodologies for
assessing allele-specific expression using the MassARRAY ® have
been developed.
Introduction The process of
analyzing allele-specific expression is similar to that for conducting
disease-association screens with pooled DNAs (3, 4), with the exception
that cDNA is used instead of genomic DNA. The method compares ratios of
alleles by measuring the areas of primer extension signals for each
allele. For this method, it is important to establish that no
significant allele bias exists. This is achieved by assaying both cDNA
and genomic DNA samples from the same individual for the same cSNP. If
any bias is observed on the genomic level, then it should be accounted
for when comparing allele ratios in the cDNA.
A different method to compare the ratio of alleles on the cDNA level
is competitive PCR coupled with the MassEXTEND procedure (5). The
competitor and the cDNA of interest are co-amplified by polymerase chain
reaction (PCR) with the same kinetics (5). For allele-specific
expression, a third allele, representing the competitive template, is
introduced and its concentration titrated to determine the levels of
each of the wild-type alleles (6, 7). The concentration of the
transcript is calculated from the ratio of the resulting PCR products.
The difference in transcript levels correlated with each allele can be
precisely determined.
Here we describe the use of both methods to analyze allele-specific
expression of TP73 for the 629C/T SNP (rs1801174) using samples
independently shown to exhibit allele-specific expression (8).
Experimental Conditions Assay
design. Assays were designed using MassARRAY Design Software v2.0.7.
Sequence information was retrieved from dbSNP (www.ncbi.nlm.nih.gov/SNP/index.html).
DNA isolation. Genomic DNA was isolated using the PUREGENE™Genomic
DNA Purification Kit (Gentra Systems, Minneapolis, MN, USA) following
the protocol provided with the kit. Starting material was 5 × 107
cells.
RNA isolation. Total RNA was isolated using TRIZOL (Invitrogen
Life Technologies, Carlsbad, CA, USA) following the protocol provided
with the reagent. Starting material was 5 × 107 cells.
Reverse transcription. Reverse transcription was performed
using 1 µg of total RNA primed with random hexamers using Thermoscript
RT (Invitrogen Life Technologies).
PCR. PCR was performed using Hotstar Taq (Qiagen, Hilden,
Germany) in a 5 ul volume.
Competitive PCR. 1 µL of cDNA from the RT reaction and 1 µL
of competitor are used. PCR, EXTEND, dispensing and automated
measurement were performed according to SEQUENOM procedures (7).
Results Genomic DNA.
Heterozygote biases due to PCR and/or effects were determined using
genomic DNA isolated from three unrelated human lymphoblast cell lines
(1, 3). Frequencies of the two alleles were used to correct the
frequencies of alleles in the subsequent experiments. The average
frequency of the low mass allele (C allele) was 0.56 in samples GM10864
and GM10834. Sample GM10834 yielded a frequency of 0.57.
Figure 1. Overview of
allele-specific expression analysis from PCR to mass
spectrometry.
|
Relative allele-frequency analysis.
cDNA samples from the same individuals, after being corrected by the
genomic data, correlated to previously published results of the
allele-specific expression in the TP73 tumor suppressor gene (1). Sample
GM10864 exhibited the greatest difference between the expression of the
C allele and T allele (0.71:0.29). A smaller difference was seen in
sample GM12616, where the C allele was expressed only slightly more than
the T allele (0.57:0.43). Sample GM10834 expressed the T allele over the
C allele (0.29:0.71). The ratios of C to T alleles are shown in Figure
1.
Multi-logarithmic competitor
titration. Allele frequency analysis of cDNA provides a relative
ratio of alleles. The use of a competitor allows for the calculation of
transcript concentrations, which results in a more exact determination
of the differences.
In this experiment, the competitive
template was titrated over a nine-log range of concentrations from 100
pM (1 × 10-10M) to 1 aM (1 × 10-18M).
Figure 2. Corrected ratios of
alleles from genomic DNA, cDNA and cDNA with competitor.
|
The results obtained out of the
competitor experiment maintained the trends of allele ratios observed
via relative allele frequency analysis. Actual concentrations of alleles
can be calculated by regression plot analysis of the logarithmic
relationship between allele frequency and decreasing competitor
concentration. Using the conversion rate of 1 aM = 3 molecules, the
number of molecules can be calculated from the concentration obtained
from regression analysis, which provides a more quantitative
determination of the differences in transcripts. A comparison of both
results is provided in Figure 2. The results show that similar allele
ratios were observed between both methods for GM12616 and GM10834.
GM10864; the sample with the most significant use of a competitor allows
for standard curve quantitation, therefore the results from this
methodology are more accurate.
Conclusion
Differential allele expression is an important factor contributing to
the development of common diseases (1, 2). Here, methods are described
for quantitatively assessing allele-specific expression using the
MassARRAY system. Allele-specific expression was successfully assayed in
three individuals using a cSNP in the TP73 gene. The results match those
obtained in previous studies. Measuring allele-specific expression with
MassARRAY allows investigators to:
- Assay and compare individual cDNA samples. Assay a large
set of allele-discriminating marker SNPs from gene coding regions of
interest to determine if allele-specific expression is associated
with that particular disease model
- Assay and compare pooled cDNA samples. Pool cDNA
populations and assay each SNP to determine expression level
differences between alleles at the transcript population level. This
technique was successfully done with the samples in this study (data
not shown).
- Save time and consumables. Quickly assess if
allele-specific expression is pronounced in a diseased population
versus its control population while conserving study samples and
reagents.
- Quantify differences in allele-specific transcript numbers.
Determine the number of mRNA molecules converted to cDNA for each
specific allele using competitive PCR in conjunction with MassARRAY.
The introduction of this
quantitative approach coupled with the high-throughput capabilities of
MassARRAY offers a powerful tool for linking the roles of genomics, gene
expression and their manifestations in certain diseases.
References
1. H. Yan et al., Science 297, 1143 (2002).
2. S.H. Lo et al., Genome Res.
13, 1855-1862 (2003).
3. K. Tang et al., J. Proteome
Res. 3, 218-227 (2003).
4. N. Herbon et al., Genomics
81(5), 510-518 (2004).
5. C. Ding et al., BMC Genetics 5,
8 (2004).
6. C. Ding et al., PNAS 100(6),
3959-3064 (2003).
7. P. Oeth et al., Gene
expression analysis using MassARRAY. SEQUENOM application note.
Accessed at http://www.sequenom.com.
8. H. Yan, personal communication.
SEQUENOM Inc.
3595 John Hopkins Ct.
San Diego, CA 92121 USA
Tel. 877-4-GENOME
www.sequenom.com
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