Genome Res. 13:1719-1727, 2003
©2003 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/03 $5.00
Methods
Customized Molecular Phenotyping by Quantitative Gene Expression and Pattern Recognition Analysis
Shreeram Akilesh,
Daniel J. Shaffer and
Derry Roopenian1
The Jackson Laboratory, Bar Harbor, Maine 04609, USA
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ABSTRACT
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Description of the molecular phenotypes of pathobiological processes in
vivo is a pressing need in genomic biology. We have implemented a
high-throughput real-time PCR strategy to establish quantitative expression
profiles of a customized set of target genes. It enables rapid, reproducible
data acquisition from limited quantities of RNA, permitting serial sampling of
mouse blood during disease progression. We developed an easy to use
statistical algorithm—Global Pattern Recognition—to readily
identify genes whose expression has changed significantly from healthy
baseline profiles. This approach provides unique molecular signatures for
rheumatoid arthritis, systemic lupus erythematosus, and graft versus host
disease, and can also be applied to defining the molecular phenotype of a
variety of other normal and pathological processes.
Expression profiling promises to provide insight into normal biological and
pathological processes (Alizadeh et al.
2000 ; Shaffer et al.
2001; van't Veer et al.
2002). The hope is that knowledge obtained from gene expression
patterns will predict disease outcome or indicate individualized courses of
therapy. The two technologies that have emerged as the most promising gene
expression tools are hybridization-based microarrays and quantitative
real-time RT-PCR (QPCR)analysis (Duggan et
al. 1999; Lockhart and
Winzeler 2000; Giulietti et al.
2001; Green et al.
2001). Microarrays permit the simultaneous analysis of a large
number of genes, but extensive replicate sampling can be labor-intensive.
Additionally, samples with limiting RNA (such as mouse peripheral blood or
laser-capture microdissection samples)can only be used following cDNA
amplification (Wang et al.
2000), which adds another processing step that could introduce
bias. This makes longitudinal microarray analysis of peripheral blood samples
from an experimental cohort technically challenging.
QPCR platforms using gene-specific primers provide highly sensitive and
reproducible expression quantification from small amounts of starting material
(Gibson et al. 1996;
Heid et al. 1996;
Schmittgen et al. 2000), but
have been limited in the number of genes analyzed practically. Therefore, we
combined the multiple gene approach of microarrays with the sensitivity of
QPCR to produce a high-throughput customized "ImmunoQuantArray"
(IQA), the first generation of which consists of 96 gene-specific QPCRs
designed to monitor genes associated with immune processes.
QPCR instruments monitor gene-specific amplicons with fluorescent dye
chemistry. The amplification curves typically have a sigmoidal shape in which
the exponential amplification phase reveals the number of PCR cycles required
to achieve a certain fluorescence intensity. A cycle threshold or Ct value for
each reaction is the number of cycles at which the reaction crosses the
fluorescence threshold. The fewer cycles required to reach a certain
fluorescence intensity, the lower the Ct value and the greater the initial
amount of input target cDNA. Genes that do not amplify during the 40-cycle PCR
are considered "off" and are given a Ct value of 40
(Heid et al. 1996).
QPCR data are usually interpreted as fold changes in gene expression.
Changes in gene expression are derived by normalizing the expression of a gene
to that of an appropriate "housekeeping" gene (assumed to be
invariant; Livak and Schmittgen
2001). This relative normalization procedure is presently regarded
to be the only practical option available for interpreting QPCR data. An
alternative, accurate quantification of input RNA/cDNA is challenging when
input RNA is of limiting quantities and impractical for scale-up
(Morrison et al. 1998).
To more reliably evaluate expression changes in QPCR data, we developed a
novel statistical algorithm—Global Pattern Recognition (GPR)—to
reveal significant changes in gene expression patterns. Inspired by
triangulation techniques to determine positional information in cartography
and astronomy, GPR goes through several iterations to compare the change of
expression of a gene normalized to every other gene in the IQA. By comparing
the expression of each gene to every other gene in the array, a global pattern
is established, and significant changes are identified and ranked.
Importantly, GPR takes advantage of biological replicates to extract
significant changes in gene expression, thus providing a novel alternative to
the use of relative normalization in QPCR experiments.
We show here that the IQA/GPR approach is a reliable analytical tool that
can be used to establish immunological gene expression profiles of normal mice
and in mice developing rheumatoid arthritis (RA), systemic lupus erythematosus
(SLE), and graft versus host disease (GVHD). Moreover, we show that one can
obtain temporal expression profiles from unamplified blood cDNA samples from
individual mice, thus making it possible to establish the relationship between
gene expression pattern and individual disease severity.
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RESULTS AND DISCUSSION
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Design and Validation of the ImmunoQuantArray
We generated a first-generation QPCR-based IQA consisting of 96 PCR
amplicons that survey, at the transcriptional level, genes associated with a
broad spectrum of immunological processes. We therefore selected sentinel
genes, whose altered expression correlates with innate or adaptive immune
responses, T-cell-mediated (T helper 1 and T helper 2)responses, humorally
mediated responses, and/or general inflammatory responses
(Table 1; primer sequences are
available online at
www.genome.org).
The SYBR Green detection system was used because it obviates the need for
expensive gene-specific TaqMan probes.
Reproducibility and Sensitivity of the IQA
We tested the ability of this system to generate reproducible data. cDNAs
derived from the samples being compared were analyzed using the IQA, and the
raw cycle threshold (Ct)values of each amplicon represented as a scatterplot.
The farther a gene deviates from the linear regression best-fit line, the
greater the difference in its level of expression between the two samples
being compared. Figure 1A is a
representative experiment comparing "biological replicate" cDNAs
from two 8-week-old C57BL/6J males from splenocytes (first panel)and whole
blood (second panel). This strong correlation was maintained whenever two
similar samples (e.g., age-, sex-, and strain-matched spleen cDNAs from two
animals)were compared (data not shown), indicating that variability between
genetically identical biological replicates is low. However, the correlation
breaks down when comparing splenocyte with blood Ct values, emphasizing the
contribution of tissue source to the gene expression pattern
(Fig. 1A, third panel). RNA
degradation and inconsistency of the reverse transcription reaction were not
significant issues when samples were collected and processed quickly and
uniformly (see Methods). These results demonstrated that the IQA system is
capable of generating reproducible data between biological replicates not only
from mouse spleen but also from microsamples (75 µL)of blood.
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Figure 1 Reproducibility and sensitivity of the ImmunoQuantArray. Raw Ct values for
each of the 96 genes in the IQA are plotted. The linear regression best-fit
line is shown and its correlation coefficient indicated. (A) Spleen
cDNAs (left panel) or blood cDNAs (middle panel) from two
C57BL/6J males. The average of the two spleen cDNA Cts is compared with the
average two blood cDNA Cts (right panel). (B) The average
spleen cDNA Cts of five BALB/cJ (left), three 129X1/SvJ
(middle), or five BXSB/MpJ-Yaa+ (right)
males was compared with the average spleen cDNA Cts of five C57BL/6J males.
The point lying closest to the ordinate is 18S rRNA.
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The influence of background genetics on the molecular phenotypes was also
tested. Using splenocyte cDNA from 8-week-old male animals, the average
expression level (average Ct values)of cDNAs prepared from five C57BL/6J
animals was compared with the average from five BALB/cJ, three 129X1/SvJ, and
five BXSB/MpJ animals (Fig.
1B). The departure of the correlation coefficient from unity
indicated that genetic background altered the gene expression patterns.
Rationale for Implementing the GPR Algorithm
Although scatterplot analysis (Fig.
1)provided some insight into gene expression patterns, it was
restricted to one-by-one gene comparisons, and would only be expected to
provide reliable data after accurate quantification of input RNA/cDNA.
Biological replicate comparison of two cohorts of animals could be performed
by plotting the average Ct values for each gene (as shown in
Fig. 1B), but it was a
suboptimal method for identifying significant changes in gene expression
between two experimental groups.
The common mode of comparative analysis for QPCR data is the use of a
single normalizer gene with which the expression of all genes is compared.
This mode of analysis is greatly complicated by the fact that housekeeping
genes commonly used as normalizers (e.g., GAPDH,  -actin, and HPRT)
themselves can change in expression when comparing tissues or cells in
different states of activation (Bustin
2000; Schmittgen et al.
2000; Goidin et al.
2001; Hamalainen et al.
2001). 18S rRNA is another normalizer that intuitively and
experimentally seems more stable, but for unknown reasons, even 18S can vary
in comparison to other genes when analyzed by sensitive QPCR techniques (e.g.,
Fig. 1B, left panel;
Bustin 2000). Any small
variation in the normalizer amplification would therefore compromise the
analysis of the complete QPCR data set.
Ideally, we wanted to compare the expression profiles of experimental
groups of animals with those of the control groups such that the comparison
was not contingent on the expression stability of a single normalizer gene.
Using the expression data from the 96 genes in the IQA as a foundation, we
developed the GPR algorithm to discern statistically significant changes in
gene expression. After filtering the data (see Methods), GPR normalizes each
eligible gene against every other gene that is eligible as a normalizer, thus
eliminating the reliance on single-gene normalization. Conceptually, GPR
resembles standard ANOVA techniques but differs in its implementation
(Kerr and Churchill 2001). We
initially applied ANOVA techniques to our QPCR data sets (data not shown). In
ANOVA, to normalize sample-to-sample variability, the average Ct value of the
96 genes for each sample is subtracted from each gene's Ct value. However, PCR
dropouts or genes that are "off" (with a Ct of 40)are necessarily
included in this average, adversely skewing the entire data set. Because GPR
considers each gene individually and filters out such null data, it is not
adversely affected by PCR dropouts as is ANOVA. In a typical experiment, a
96-well IQA QPCR is run for each of up to 10 samples—five control and
five experimental biological replicates. GPR then uses a T-test to
evaluate gene expression between control and experimental group biological
replicates on a gene-by-gene basis. Because GPR ranks genes based on
biological replicate consistency, those genes whose expression differs
significantly when comparing control and experimental cohorts will be
identified regardless of whether the changes are large or small.
Validation of the GPR Algorithm by Bootstrap Analysis
We used bootstrap analysis (Efron and
Tibshirani 1998)to evaluate the reliability of GPR to detect
nonrandom changes in gene expression. After using GPR to analyze a set of IQA
results, we shuffled the data on a gene-by-gene basis for 250 iterations and
analyzed the randomized data set with GPR after each shuffling. This random
resampling generated a bootstrap probability distribution of GPR scores. The
GPR scores obtained by analyzing the experimental data (observed scores)were
tested to see if they could have arisen simply by chance in a randomized data
set. If the observed GPR score did not appear once in 250 shufflings of the
data set, the probability of that particular gene having significantly changed
by chance alone is less than 1/250, or p < 0.004.
The KRN T-cell receptor transgenic strain, when bred to NOD/Lt, produces
transgene-bearing F1 mice that develop a severe autoimmune
disorder with distinct similarities to RA
(Kouskoff et al. 1996;
Korganow et al. 1999).
Table 2A lists the 12
top-ranked genes identified by GPR and associated bootstrap analysis when
comparing blood cDNAs from transgenic (KRNxNOD) F1 and
nontransgenic control littermate cohorts. The highest GPR scores were also
highly significant (p ≤ 0.004)when compared with the bootstrap
scores generated by randomly resampling the data set.
As a negative control, we then subjected an IQA data set with minimal
expected expression differences to similar bootstrap analysis.
Table 2B compares GPR results
derived from three consecutive bleeds of one C57BL/6J mouse compared with
consecutive bleeds of another C57BL/6J mouse. In contrast to the (KRNxNOD)
F1 blood data, 96-gene GPR analysis of the C57BL/6J blood
cDNAs yielded only a single difference (Tnf, GPR score 0.489,
indicating that this gene was significantly different from 49% of the eligible
normalizer genes). In analyzing more than 50 IQA data sets, we observed that
genes with GPR scores falling below 0.4 lose reliability regarding their
change in expression because the values are based on too few normalizers.
Because the bootstrap distribution was generated by randomizing of the GPR
scores, genes falling well below 0.4, such as Tnfrsf1a and
Tnfrsf1b in Table 2B,
occasionally appear as significant. Typically, these genes have a very low
level of expression (i.e., Ct values close to 40)and/or are statistical noise.
However, as shown in Table 2B,
genes with GPR scores ≥0.4 are always highly significant by bootstrap
analysis. Taken together, the results indicate that the GPR can reliably
identify genes with expression changes between biological replicates in
control and experimental cohorts.
Molecular Phenotype of the (KRNxNOD) F1 Model for
RA
Blood samples from (KRNxNOD) F1 transgenic animals
showed reduced levels of the T-cell-specific genes Cd4, Cd3e, Cd5,
and Zap70 expression compared with nontransgenic littermates
(Table 2A). This result is
consistent with the fact that adult transgenic animals have lower numbers of
CD4 T-cells compared with nontransgenic littermates
(Kouskoff et al. 1996).
Up-regulation of the antibody Fc receptor common  -chain
(Fcer1g)used by the inflammatory Fc receptors FcRI and
FcRIII also correlates with the presence of the transgene and disease.
Notably, these proinflammatory Fc receptors are required to precipitate
disease in the (KRNxNOD) F1 serum transfer model
(Ji et al. 2002). Other genes
reported as significantly changed in Table
2A are interesting candidates for further study. Analysis of other
lymphoid tissues and longitudinal peripheral blood analysis of these mice may
identify other genes transcriptionally activated/repressed at specific stages
of disease progression. The results show that IQA/GPR analysis from blood
samples can reveal expression alterations in genes consistent with the
progression of autoimmune arthritis in the (KRNxNOD) F1
model.
Serial Molecular Phenotyping of BXSB-Yaa SLE
SLE is a heterogeneous disease syndrome with common features of B- and
T-cell activation leading to the elaboration of pathogenic autoantibodies. The
BXSB/MpJ strain develops a chronic form of SLE that is severely aggravated in
males carrying the SB allele at the Y-linked
autoimmune accelerator (Yaa) locus
(Murphy and Roths 1979). Using
BXSB male mice carrying a wild-type Y-chromosome as controls, we examined
splenocyte cDNA samples from cohorts of BXSB-Yaa males over a 14-wk
time course (Fig. 2). Most
notably, Il10 expression increases substantially at week 14, a time
at which the disease first becomes evident. Increased IL10 production is
strongly associated with SLE in both humans and mouse SLE models
(Grondal et al. 2000;
Moore et al. 2001). Expression
of Il4, Ifnb, Ifng, and Tnfrsf6 (Fas), all of which
have been associated with SLE (Nousari et
al. 1998; Wong et al.
2000; Bijl et al.
2001; Theofilopoulos et al.
2001), are up-regulated prior to 14 wk, but with some oscillation.
Because the animals in the two groups were genetically matched except for the
Yaa locus, these data indicate a molecular phenotype of the
Yaa-driven acceleration of the BXSB SLE disease model.
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Figure 2 Serial molecular phenotyping of BXSB-Yaa SLE. Spleen cDNAs from
cohorts of three to five BXSB/MpJ-Yaa males and age-matched
BXSB.B6-Yaa+ controls were subjected to IMQ/GPR analysis
at weeks 4, 6, 8, and 14. The fold changes (normalized to 18S rRNA) of genes
that received a GPR score ≥0.4 and were significant after normalization to
18S rRNA are plotted.
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Molecular Phenotype of Mice Undergoing GVHD
GVHD is a prototypic T-cell-mediated disease in which donor CD4 and CD8
T-cells respond to host alloantigens, proliferate, attack, and destroy
multiple host organs, and undergo apoptotic cell death. C57BL/6J-derived bone
marrow and spleen cells cause acute GVHD when transferred into lethally
irradiated allogenic male 129P3/J recipients
(Korngold and Sprent 1983). To
understand the transcriptional changes associated with GVHD, we sampled and
analyzed peripheral blood leukocytes from mice undergoing acute GVHD and
compared them with the same source of leukocytes transferred into irradiated
syngeneic C57BL/6J mice. Figure
3 depicts the fold changes of genes identified as significant by
GPR analysis. Of the 96 genes analyzed, markers of T-cell activation Lck,
Zap70, Cd4, Cd8, and Cd3 were strongly up-regulated, as was the
key acute GVHD cytokine Ifng
(Ferrara 2000)and the receptor
IL-12Rb1, which regulates IFN- production
(Losana et al. 2002). The cell
cycle control gene Map2k2 and the apoptotic/antiapoptotic gene
Bcl2l were down-regulated as a consequence of allogenic cell transfer
(Fig. 3). These results
highlight the amount of coherent information that can be obtained from serial
blood analysis.
Although the approach outlined here is a logical method for confirmation
and accurate quantification of genes whose expression appears to have changed
based on microarray analyses, a more general application is as a routine
analytical tool to perform high-throughput quantitative expression analysis of
a customized gene set. Major strengths of this approach include the
sensitivity of QPCR techniques to accurately assess the expression of a
customized gene set from limited RNA sources (e.g., mouse peripheral blood),
the exploitation of multiple biological replicates to extract significant
expression changes, and the obviation of the need for single-gene-based
normalization. Significant expression changes were evident even though the
blood and spleen samples analyzed were comprised of heterogeneous cell types.
Despite the fact that the expression changes observed could thus be a
consequence of variation of cell types and/or changes in expression level on a
per cell basis, the varied gene expression patterns observed were consistent
with the pathological processes analyzed. Longitudinal analysis from limiting
biological samples is not yet practical with microarrays without amplification
of the cDNA (Wang et al.
2000). However, the limited sample needed for QPCR allows serial
sampling of individual mice to arrive at molecular profiles that predict
disease onset and severity. Importantly, the IQA requires no sophisticated
equipment other than quantitative PCR equipment and a Microsoft Excel-capable
computer. The platform is flexible such that genes can be added or subtracted
from the set according to the needs of the investigator, and can readily be
expanded to a 384-well format. Finally, although we have applied the system to
probing the molecular signatures of immunological diseases, the same approach
can be used to establish accurate molecular phenotypes of a wide variety of
nonimmunological normal and disease processes.
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METHODS
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Mice
Age- and sex-matched C57BL/6J, BALB/cJ, 129X1/SvJ, 129P3/J,
BXSB/MpJ-Yaa+, and BXSB/MpJ-Yaa mice were
obtained from our research colony or from the Jackson Research System's
production facility at the Jackson Laboratory, Bar Harbor, Maine. KRN T-cell
receptor transgenic mice were a kind gift from D. Mathis and C. Benoist.
Hemizygous KRN transgenic males (on a C57BL/6J background)were bred to NOD/LtJ
females, and progeny were typed by PCR. Nontransgenic (arthritis-free)animals
were compared with transgenic (arthritic)littermates for IQA experiments. All
animal experiments were approved by the Jackson Laboratory's Animal Care and
Use Committee (ACUC).
Induction of GVHD
Eight-week-old recipient male 129P3/J (experimental group) and female
C57BL/6J mice (control group)were irradiated with split doses of 450 cGy from
a 137Cs source within a 4-h interval, and injected with a mixture
of bone marrow and spleen cells from female C57BL/6J mice as described
(Choi et al. 2002).
cDNA Synthesis
To minimize sample preparation variation, all samples for a given
experiment (from both control and experimental groups)were processed in
parallel. Solid tissues were collected into RNALater (Ambion), used
immediately, or stored at -20°C for not more than 3 d. Total RNA was
purified from solid tissue using the RNAqueous 4-PCR kit (Ambion)and
DNase-treated following the manufacturer's recommendations. Total RNA was
purified from 75 µL of blood, collected by a retro-orbital bleed into
heparinized 100-µL capillary tubes, using the 6100 RNA Prep Station (ABI),
and DNase-treated following the manufacturer's recommendations. Synthesis of
cDNA from 5–10 µL of total RNA was carried out using the Retroscript
kit (Ambion)following the manufacturer's recommendations. To minimize
variation in sample preparation, the cDNA was stored at -20°C and was used
for QPCR within 3 d of preparation.
PCR Amplicon Development
Primer sets (MWG Biotech)were designed using Primer Express v1.5 (Applied
Biosystems, ABI)following recommendations appropriate for use in the ABI Prism
7700 Sequence Detection System. Selected primers were searched against GenBank
via the NCBI BLAST algorithm to ensure specificity to the desired gene target.
Each PCR product was subjected to bidirectional sequencing using each
end-specific primer on the ABI Prism 3700 Sequencer. SYBR Green dissociation
curves were generated via the 7700 to further ensure the generation of a
single PCR product under experimental reaction conditions. Primer sequences
are available online at
www.genome.org.
Real-Time Quantitative PCR
ImmunoQuantArray 96-well plates were prepared by the addition of 0.7 µL
of 1 µM Primers per well. To each well was then added 9.3 µL of PCR
master mix, which contained 525 µL of 2x SYBR Green Master Mix (ABI),
384 µL of dH2O, and 70.4 µL of cDNA (typically a 1:10 for
blood or 1:20 for spleen dilution of stock cDNA). The plate was sealed using
an Optical Adhesive Cover (ABI), and the fluid was spun down in a swinging
bucket centrifuge. Real-time PCR data were collected on the ABI Prism 7700
Sequence Detection System v1.7 using the default reaction conditions (1 cycle
at 50°C for 2 min, 1 cycle at 95°C for 10 min, 40 cycles at 95°C
for 15 sec and at 60°C for 1 min). The baseline and threshold were set to
experimentally determined values and the Experimental Report data (a table of
Ct values for each of the 96 reactions)were exported for GPR analysis.
Global Pattern Recognition Algorithm
The GPR algorithm is implemented as a Microsoft Excel macro to identify
significant changes in gene expression profiles within a 96-well real-time PCR
data set using the Cycle Threshold (Ct)values generated by the ABI Prism 7700.
GPR compares the Ct of each candidate gene individually with the Ct of every
other gene in the 96-gene IQA data set that is eligible as a normalizer. Doing
so allows stratification of genes both as a function of the magnitude of the
change and the reproducibility of the Ct values within each of the two
experimental groups.
GPR first filters data into overlapping gene and normalizer bins. This
filtering process is controlled by a user-defined Cycle Cutoff (CC)value (set
at 37.5 for all experiments shown). The CC is the PCR cycle number above which
data are disregarded. A number of 37 cycles approaches single-copy detection,
and thus leads to variable data. Consequently, using the CC eliminates these
noisy data. Using the CC, GPR culls the data with the Normalizer Filter and
the Gene Filter. A gene passes through the Normalizer Filter if all
observations in both control and experimental groups fall below the cycle
cutoff value (e.g., an eligible normalizer—Group 1 Ct values: 33.4,
31.1, 31.5; Group 2 Ct values: 33.9, 34.2, 33.6). A gene passes through the
Gene Filter if all observations in either control or experimental groups fall
below the cycle cutoff value (e.g., an eligible gene, but not an eligible
normalizer— Group 1 Ct values: 32.4, 33.1, 31.8; Group 2 Ct values:
37.9, 39.1, 40). Each eligible gene is then normalized in turn to each
eligible normalizer by computing a  Ct value (Ctgene =
Ctgene - Ctnormalizer). For each gene-normalizer
combination, the individual Ct values generated for the control and
experimental groups are compared by a two-tailed heteroscedastic
(unpaired)Student's t-test, and a "hit" is recorded if
the p-value from the t-test falls below a user-defined
p-value (e.g., 0.05). Thus data from biological replicates are
compared directly at the Ct level at each round of normalization. Each
candidate gene, when processed through GPR, is significantly different when
compared with certain normalizers and insignificant when compared with others.
The total number of normalizer "hits" for each gene is tallied and
used to sort the genes in the 96-well array with the genes changed in
comparison to the largest number of normalizer genes ranking highest. The GPR
score indicates the fraction of normalizer genes against which the candidate
gene was found to be significantly different. Analysis of more than 50 data
sets indicates that a GPR score of 0.4 or higher (statistically different when
compared with 40% or more of the eligible normalizers)reliably identifies the
genes having undergone significant change (see Results and Discussion). After
ranking genes by GPR score, the direction and magnitude of change of a
particular gene with respect to the control group can then be approximated by
comparing the average Ct values of the two groups after normalization
to 18S rRNA by the 2-Ct method
(Livak and Schmittgen 2001).
The GPR algorithm implemented in Excel (and documentation)is available for
download at
http://www.jax.org/research/roop/gene_expression.html.
Bootstrap Probability Distribution
Ct values from IQA data sets for the two comparison groups were randomly
resampled on a gene-by-gene basis and then processed by the GPR algorithm. The
resultant GPR scores for each gene were recorded for each of the 250 random
resamplings to generate a bootstrap probability distribution
(Efron and Tibshirani 1998). An
observed GPR score above the 99.5th percentile of the gene-specific bootstrap
probability distribution was considered significant (bootstrap p <
0.005, corresponding to ≤1 value in the bootstrap distribution being higher
than the observed GPR score). The p-value for each gene was computed
as the number of scores in the bootstrap distribution higher than the observed
GPR score divided by the number of random resamplings.
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Acknowledgements
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The authors thank Gary Churchill and Jason Stockwell for expert advice and
ANOVA analysis; Thomas Sproule for expert mouse colony management; and Carol
Bult, Wayne Frankel, and Jason Stockwell for manuscript review. This work was
supported by grants from the National Institute for Diabetes, Digestive and
Kidney Diseases (NIDDK) and the Alliance for Lupus Research (ALR). S.A. was
supported by a fellowship from the Shelby Cullom Davis Foundation.
The publication costs of this article were defrayed in part by payment of
page charges. This article must therefore be hereby marked
"advertisement" in accordance with 18 USC section 1734 solely to
indicate this fact.
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Footnotes
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Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.533003.
1 Corresponding author.
E-MAIL
dcr{at}jax.org;
FAX (207) 288-6383. 
[Supplemental material—The primer sequences for genes included in the
ImmunoQuant Array (and listed in Table
1) are available online at www.genome.org. The GPR algorithm,
documentation, and sample data sets are available at
http://www.jax.org/staff/roopenian/labsite/index.html.The following
individuals kindly provided reagents, samples, or unpublished information as
indicated in the paper: D. Mathis and C. Benoist.]
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access to the GPR algorithm and documentation.
Received November 13, 2002;
accepted in revised format April 1, 2003.
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ABSTRACT
RESULTS AND DISCUSSION
129P3/J). Five syngeneic transplant recipients (C57BL6/J