Genome Biology 2005, 6:R107
2005Mao et al.Volume 6, Issue 13, Article R107
Primary and secondary transcriptional effects in the developing
human Down syndrome brain and heart
Rong Mao*†, Xiaowen Wang‡, Edward L Spitznagel Jr§, Laurence P Frelin¶,
Jason C Ting¶, Huashi Ding‡, Jung-whan Kim¥, Ingo Ruczinski#,
Thomas J Downey‡ and Jonathan Pevsner*†¶¥
Addresses: *Program in Biochemistry, Cellular and Molecular Biology, Johns Hopkins School of Medicine, 1830 East Monument Street,
Baltimore, MD 21205, USA. †Department of Neuroscience, Johns Hopkins School of Medicine, 725 North Wolfe Street, Baltimore, MD 21205,
USA. ‡Partek Incorporated, St Charles, MO 63304, USA. §Department of Mathematics, Campus Box 1146, Washington University, St Louis, MO
63130, USA. ¶Department of Neurology, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD 21205, USA. ¥Pathobiology Graduate
Program, Johns Hopkins School of Medicine, 720 Rutland Avenue, Baltimore, MD 21205, USA. #Department of Biostatistics, Johns Hopkins
Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.
Correspondence: Jonathan Pevsner. E-mail: firstname.lastname@example.org
© 2005 Mao et al.; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Profiling human Down Syndrome<p>Microarray analysis of transcript levels in fetal cerebellum and heart tissues of Down Syndrome patients showed a disruption only of chromosome 21 gene expression.</p>
Background: Down syndrome, caused by trisomic chromosome 21, is the leading genetic cause
of mental retardation. Recent studies demonstrated that dosage-dependent increases in
chromosome 21 gene expression occur in trisomy 21. However, it is unclear whether the entire
transcriptome is disrupted, or whether there is a more restricted increase in the expression of
those genes assigned to chromosome 21. Also, the statistical significance of differentially expressed
genes in human Down syndrome tissues has not been reported.
Results: We measured levels of transcripts in human fetal cerebellum and heart tissues using DNA
microarrays and demonstrated a dosage-dependent increase in transcription across different
tissue/cell types as a result of trisomy 21. Moreover, by having a larger sample size, combining the
data from four different tissue and cell types, and using an ANOVA approach, we identified
individual genes with significantly altered expression in trisomy 21, some of which showed this
dysregulation in a tissue-specific manner. We validated our microarray data by over 5,600
quantitative real-time PCRs on 28 genes assigned to chromosome 21 and other chromosomes.
Gene expression values from chromosome 21, but not from other chromosomes, accurately
classified trisomy 21 from euploid samples. Our data also indicated functional groups that might be
perturbed in trisomy 21.
Conclusions: In Down syndrome, there is a primary transcriptional effect of disruption of
chromosome 21 gene expression, without a pervasive secondary effect on the remaining
transcriptome. The identification of dysregulated genes and pathways suggests molecular changes
that may underlie the Down syndrome phenotypes.
Published: 16 December 2005
Genome Biology 2005, 6:R107 (doi:10.1186/gb-2005-6-13-r107)
Received: 26 July 2005
Revised: 4 October 2005
Accepted: 21 November 2005
The electronic version of this article is the complete one and can be
found online at http://genomebiology.com/2005/6/13/R107
R107.2 Genome Biology 2005, Volume 6, Issue 13, Article R107 Mao et al.
Genome Biology 2005, 6:R107
Human autosomal abnormality is the leading cause of early
pregnancy loss, neonatal death, and multiple congenital mal-
formations [1,2]. Among all the autosomal aneuploidies,
Down syndrome (DS), with an incidence of 1 in approximately
800 live births, is most frequently compatible with postnatal
survival. It is characterized by mental retardation, hypotonia,
short stature, and several dozen other anomalies [3-5].
It has been known since 1959 that DS is caused by the tripli-
cation of a G group chromosome, now known to be human
chromosome 21 [6,7]. As for all aneuploidies, the phenotype
of DS is thought to result from the dosage imbalance of mul-
tiple genes. By the 1980s, a primary effect of increased gene
products, proportional to gene dosage, was established for
dozens of enzymes in studies of various aneuploidies .
More recently, microarrays and other high-throughput tech-
nologies have allowed the measurement of steady-state RNA
levels for thousands of transcripts in human DS cells [8-10]
and in tissues obtained from mouse models of DS [11-15].
Most of these studies have confirmed a primary gene dosage
effect. We previously measured RNA transcript levels in fetal
trisomic and euploid cerebrum samples, and in astrocyte cell
lines derived from cerebrum . We observed a dramatic,
statistically significant increase in the expression of trisomic
genes assigned to chromosome 21.
The secondary, downstream consequences of aneuploidy are
complex. A major unanswered question is the extent to which
secondary changes occur in DS as a consequence of the aneu-
ploid state. On chromosome 21, gene expression may be reg-
ulated by dosage compensation or other mechanisms such
that only a subset of those genes is expressed at the expected
50% increased levels. For genes assigned to chromosomes
other than 21, the effect of trisomy 21 (TS21) could be rela-
tively subtle or massively disruptive. It has been hypothesized
that gene expression changes in chromosome 21 are likely to
affect the expression of genes on other chromosomes through
the modulation of transcription factors, chromatin remode-
ling proteins, or related molecules [5,17,18]. Recent studies in
human and in mouse provide conflicting evidence, with some
studies suggesting only limited effects of trisomy on the
expression of disomic genes, whereas other studies indicate
pervasive effects (see Discussion).
In the present study, we assessed five specific hypotheses
relating to primary and secondary transcriptional changes in
DS. First, which, if any, chromosomes exhibited overall dif-
ferential expression between TS21 and controls? Our previ-
ous study in human tissue [8,16] suggested the occurrence of
dosage-dependent transcription for chromosome 21 genes,
but not for genes assigned to other chromosomes. The
present report addressed whether this phenomenon applies
to multiple tissues in DS.
Second, which, if any, genes assigned to chromosome 21
exhibited differential expression between TS21 and controls?
Third, which, if any, genes on chromosomes other than chro-
mosome 21 exhibited differential expression between TS21
and controls? Previous studies by other groups [8,9,19,20]
and by us  lacked sufficient statistical power to identify
significantly regulated genes in DS. The present study identi-
fied such genes by using a larger sample size, by combining
previous data from cerebrum and astrocytes  with gene
expression data from additional tissue types (cerebellum and
heart), and by using analysis of variance (ANOVA).
Fourth, can we classify tissue samples as TS21 or controls
using genes on chromosome 21 or genes on chromosomes
other than 21? Classification is a supervised learning tech-
nique that provides a powerful statistical approach to address
the question whether only chromosome 21 or the entire tran-
scriptome is involved in DS. Fifth, which, if any, functional
groups of genes exhibited overall differential expression
between TS21 and controls? Such analysis may reveal biolog-
ical processes that are perturbed in DS.
In this study we measured gene expression in heart and cere-
bellum, two regions that are pathologically affected in DS.
Total brain volume is consistently reduced in DS, with a dis-
proportionately greater reduction in the cerebellum [21,22].
Furthermore, a significant reduction in granule cell density in
the DS cerebellum has been reported for both human and the
Ts65Dn mouse model of DS . Another prominent pheno-
type of DS is congenital heart defects. TS21 has the highest
association with major heart abnormalities among all chro-
mosomal defects, and 40% to 50% of TS21 children have
heart defects [24,25]. Of those children with heart abnormal-
ities, 44% to 48% are specifically affected with atrial ventricu-
lar septal defects (AVSDs) . Other commonly affected
tissues in the DS heart include the valve regions, such as pul-
monary and mitral valves [27,28]. Barlow et al.  assessed
congenital heart disease in DS patients with partial duplica-
tions of chromosome 21, and established a critical region of
over 50 genes. The expression levels of these genes in fetal
TS21 heart samples have not yet been assessed.
Our data showed consistent, statistically significant overall
dosage-dependent expression of genes assigned to chromo-
some 21. Analysis of these data identified genes with most
consistent dysregulation of expression in different TS21 fetal
tissue and cell types, most of which were independently con-
firmed by quantitative real-time PCR. We successfully classi-
fied tissue samples using expression data from chromosome
21 genes, but not with the data on non-chromosome 21 genes.
Statistical analyses on our microarray data also indicated tis-
sue-specific, regulated functional groups of genes, which may
provide initial clues to perturbed biological pathways in TS21.
Overall, the data support a model in which the aneuploid state
increases the expression of chromosome 21 genes, with
Genome Biology 2005, Volume 6, Issue 13, Article R107 Mao et al. R107.3
Genome Biology 2005, 6:R107
Figure 1 (see legend on next page)
PC number 1 (41%)
PC number 2 (21.2%)
PC number 3 (17.2%)
PC number 2 (21.2%)
PC number 1 (53.9%)
PC number 2 (23.5%)
PC number 3 (6.88%)
PC number 2 (23.5%)
R107.18 Genome Biology 2005, Volume 6, Issue 13, Article R107 Mao et al.
Genome Biology 2005, 6:R107
formed using an 'outer' cross-validation that was used to
obtain accuracy estimates, and a nested, 'inner' cross-valida-
tion that was used to select genes and tune classifier
Expression data analysis: functional group testing
Most of the probe sets on the Affymetrix GeneChip® human
U133A microarray can be assigned to one or more functional
groups with a unique ID number based upon GO annotations
[31-33]. GO IDs are organized in a tree-like structure via par-
ent-child relationships. The top level has only one group:
'Gene_Ontology', which is then sub-divided into three groups
at the second level, including
cellular_component, and molecular_function. To assess the
statistical significance of gene expression differences in dis-
tinct functional groups, we implemented a novel t test proce-
dure that we named a 5T analysis (tree-travel, transform, t
test). This algorithm differs from web-based tools such as
GoMiner , FatiGO , GO:TermFinder , or GOTree
Machine , which define genes as either regulated or not,
and employ a Fisher's exact test or hypergeometric distribu-
tion analysis. Under the usual assumptions, namely inde-
pendence and normality of the error, a t test offers more
power than a test with a dichotomized outcome. Our algo-
rithm also differs from methods such as MAPPFinder 
that assess the significance of a user-defined, predetermined
set of genes of interest.
A detailed description of the 5T method is presented in Addi-
tional data file 3 . Briefly, the first step is tree-travel: for
each probe set, we parsed its GO annotations, and generated
a list of functional groups located in the top six levels of GO
tree structure. In the transform step, we generated a list of
probe sets assigned to a functional group and a list of probe
sets not assigned to this functional group ('non-group mem-
bers'). In the t test step, for each functional group with three
or more members in a tissue/cell type, we performed a t test
on this group and non-group members using log ratio gene
expression values. The process was repeated for all the func-
tional groups. We then sorted all the functional groups in a
tissue/cell type based on their p values from the t tests. To
avoid discarding potentially useful information, we also per-
formed Wilcoxon's rank test to assess the statistical signifi-
cance of differentially regulated functional groups having
only one or two members.
We also applied an alternative statistical test to the data based
upon a permutation principle. We started with a list of probe
sets assigned to a particular functional group. We then ran-
domly selected an equal number of probe sets from all probe
sets on the microarray and calculated the mean log ratio val-
ues. This random selection was repeated 100 times. The aver-
age of the mean log ratio values was calculated, and compared
to the mean log ratio value of that particular functional group.
The permutation test was performed on all functional groups.
Quantitative real-time PCR
Total RNA was isolated from frozen tissues or astrocytes
using RNeasy® Midi Kit (Qiagen) and followed by cDNA syn-
thesis using Invitrogen SuperScript™ First-Strand System for
RT-PCR (Invitrogen Life Technologies, Carlsbad, CA, USA).
Quantitative real-time PCR was performed by a 7900HT
Sequence Detector System (Applied Biosystems, Foster City,
CA, USA) or LightCycler (Roche Molecular Biochemicals,
Indianapolis, IN, USA). Primer sequences are described in
Additional data file 8. The expression level of the HPRT
housekeeping gene was used for normalization. Detailed
methods are provided in Additional data file 3 .
Additional data files
The following additional data are included with the online
version of this article. Additional data file 1 is a word docu-
ment entitled 'Information on samples used in microarray
studies'. It lists information on 25 samples such as race, gen-
der, and postmortem interval. Additional data file 2 is a word
document entitled 'Results of test for whether individual
genes assigned to any chromosome were differentially
expressed in TS21 relative to euploid samples'. This table
describes FDR results shown
chromosome. Additional data file 3 is a word document enti-
tled 'Additional methods'. This file provides detailed methods
for the following topics: Expression data analysis: class pre-
diction; Error estimation using nested cross-validation;
Selection of predictor genes for classification; Expression
data analysis: functional group testing; and Quantitative real-
time PCR. The functional group testing section includes the
description of a novel algorithm for functional group
analyses. Additional data file 4 is a word document that pro-
vides figure legends for the Additional data file 5 and 7 fig-
ures. Additional data file 5 is an EPS file entitled 'Permutation
test on GO functional groups'. This figure shows the results of
permutation tests, providing evidence that the functional
groups we identified are likely to have been identified with a
probability far greater than is expected by chance (as deter-
mined by a series of random permutations of the data). Addi-
tional data file 6 is a word document entitled 'Results of
Wilcoxon rank test for analysis of functional group regula-
tion'. This table provides results of a Wilcoxon rank test that
is appropriate for functional groups having a small size.
Additional data file 7 is a tif file entitled 'Relative amounts of
ZNF294 transcripts present in the fetal TS21 and euploid cer-
ebrum samples detected by quantitative real-time PCR'. This
figure shows a typical quantitative real-time PCR result, in
which the level of a transcript is significantly up-regulated in
a trisomic sample. Additional data file 8 is a word document
entitled 'Primer sequences and other information of the
quantitative real-time PCR experiments'. This table includes
Additional data file 1Information on samples used in microarray studiesLists information on 25 samples such as race, gender, and postmor-tem interval. Click here for file Additional data file 2Results of test for whether individual genes assigned to any chro-mosome were differentially expressed in TS21 relative to euploid samplesThis table describes FDR results shown for each individual chromosome. Click here for fileAdditional data file 3Additional methodsDetailed methods for the following topics: Expression data analy-sis: class prediction; Error estimation using nested cross-valida- tion; Selection of predictor genes for classification; Expression data analysis: functional group testing; and Quantitative real-time PCR. The functional group testing section includes the description of a novel algorithm for functional group analyses.Click here for file Additional data file 4Figure legends for the Additional data file 5 and 7 figuresFigure legends for the Additional data file 5 and 7 figures. Click here for fileAdditional data file 5Permutation test on GO functional groups This figure shows the results of permutation tests, providing evi-dence that the functional groups we identified are likely to have been identified with a probability far greater than is expected by chance (as determined by a series of random permutations of the data).Click here for file Additional data file 6 Results of Wilcoxon rank test for analysis of functional group regulationThis table provides results of a Wilcoxon rank test which is appro- priate for functional groups having a small size. Click here for fileAdditional data file 7 Relative amounts of ZNF294 transcripts present in the fetal TS21 and euploid cerebrum samples detected by quantitative real-time PCR This figure shows a typical quantitative real-time PCR result, in which the level of a transcript is significantly up-regulated in a tri-somic sample. Click here for file Additional data file 8Primer sequences and other information of the quantitative real-time PCR experimentsThis table includes oligonucleotide sequences.Click here for file
for each individual
Genome Biology 2005, Volume 6, Issue 13, Article R107 Mao et al. R107.19
Genome Biology 2005, 6:R107
The authors thank Ok-Hee Jeon (Johns Hopkins School of Medicine, Balti-
more, MD, USA), Mark van der Vlies (Kennedy Krieger Institute, Baltimore,
MD, USA), Mary Ann Wilson (Kennedy Krieger Institute, Baltimore, MD,
USA), Francisco Martínez Murillo (Johns Hopkins School of Medicine, Bal-
timore, MD, USA), Rafael Irizarry (Johns Hopkins Bloomberg School of
Public Health, Baltimore, MD, USA), and Jing Lin (Partek Incorporated, St
Charles, MO, USA) for assistance in generating and analyzing data. We
thank H Ronald Zielke (Brain and Tissue Bank, University of Maryland, Bal-
timore, MD, USA) and Robert Vigorito (Brain and Tissue Bank, University
of Maryland, Baltimore, MD, USA) for supplying fetal tissue and cell lines.
We thank Scott Zeger (Johns Hopkins School of Public Health, Baltimore,
MD, USA) for advice on statistical analyses, and George Capone (Kennedy
Krieger Institute, Baltimore, MD, USA), Kirby D Smith (Johns Hopkins
School of Medicine, Baltimore, MD, USA), Roger H Reeves (Johns Hopkins
School of Medicine, Baltimore, MD, USA), and N Varg for helpful
discussions and comments on the manuscript. JK is a Howard Hughes Med-
ical Institute Predoctoral Fellow. IR is supported in part by the NIH grant
CA 074841. JP is supported by R01 HD046598, an MRDDRC grant from
the National Institutes of Health, and a grant from the Taishoff Foundation.
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