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Proc Natl Acad Sci U S A. Jun 9, 2009; 106(23): 9425–9429.
Published online May 27, 2009. doi:  10.1073/pnas.0903909106
PMCID: PMC2687148
Medical Sciences

Functional genomic analysis of amniotic fluid cell-free mRNA suggests that oxidative stress is significant in Down syndrome fetuses


To characterize the differences between second trimester Down syndrome (DS) and euploid fetuses, we used Affymetrix microarrays to compare gene expression in uncultured amniotic fluid supernatant samples. Functional pathway analysis highlighted the importance of oxidative stress, ion transport, and G protein signaling in the DS fetuses. Further evidence supporting these results was derived by correlating the observed gene expression patterns to those of small molecule drugs via the Connectivity Map. Our results suggest that there are secondary adverse consequences of DS evident in the second trimester, leading to testable hypotheses about possible antenatal therapy for DS.

Keywords: antenatal therapy, Connectivity Map, gene expression, prenatal diagnosis, trisomy 21

Down syndrome (DS) is the most frequently occurring live born human autosomal trisomy, with an incidence of ≈1 in 800 births (1). DS was initially described in the mid 19th century on the basis of distinctive clinical features, such as mental retardation, “flat and broad face … narrow palpebral fissures … long, thick and roughened tongue … and (the skin) giving the appearance of being too large for the body” (2). Almost a century later, the underlying basis of the condition was recognized as being due to partial or complete trisomy of chromosome 21 (3). Subsequent work showed that the characteristic DS phenotype could be achieved by having 3 copies of the genes contained within chromosomal band 21q22, which became known as the DS critical region (4, 5).

Prenatal screening for DS has recently been recommended for all pregnancies in the United States (6), with the understanding that many women who are found to be carrying a DS fetus will opt to terminate the pregnancy. Virtually no attention has been paid to research involving ongoing DS pregnancies (7); however, functional analyses of the molecular changes in DS may allow development of testable hypotheses that could identify targets for novel treatments in utero.

To characterize more fully the differences between DS and euploid fetuses, we used Affymetrix microarrays to perform a functional genomic analytic comparison between 7 living second-trimester DS fetuses and 7 euploid controls, matched for gender and gestational age. Because cell culture may result in changes in gene expression (8, 9), we used cell-free fetal mRNA from uncultured amniotic fluid supernatant samples, which we have demonstrated to be a stable and reliable source of information regarding fetal gene expression (10, 11). Prior studies of fetal gene expression in DS have exclusively used cultured cells (1216), which may not reflect the fetus in vivo. Amniotic fluid is in direct contact with the fetal oropharynx, lungs, gastrointestinal tract, skin, and urinary system. It is the only body fluid that derives from multiple tissues and can be safely studied in living fetuses with a confirmed karyotype.

In the present study, functional analysis of the differentially-expressed genes highlights the importance of oxidative stress and several immediate downstream or compensatory processes (e.g., ion transport, signaling pathways) in the DS fetus. We also analyzed our results using the Connectivity Map (17, 18), a tool designed to identify compounds whose molecular effects either mimic or reverse an observed gene expression signature. The tool is based on a database that contains gene-expression profiles from human cell lines treated with 164 different bioactive small molecules (18). Our application of the Connectivity Map serves two purposes. Primarily, it provides further functional confirmation of the pathways identified using other tools. Additionally, in this perinatal/developmental context, these results suggest hypotheses regarding potential antenatal treatments for some of the changes identified in DS fetuses. Although fetal treatment would be unlikely to prevent the development of congenital anomalies (most of which occur in the first trimester) and mental retardation, the data presented here suggest that there are secondary adverse biological consequences of DS that are evident in the second trimester.


Differential Expression in DS vs. Controls.

We identified 2 sets of genes that differed between the trisomy 21 and euploid samples. First, we found 414 probe sets whose individual expression levels were significantly different via paired t tests (adjusted P value < 0.05) in samples matched for sex and gestational age. We call this the “Individual” gene set (detailed in Table S1). Only 5 probe sets among the Individual gene set were located on chromosome 21, corresponding to the genes CLIC6, ITGB2, RUNX1, and 2 ORFs of unknown function (C21orf67, C21orf86). Four of these five were up-regulated in DS; the exception was RUNX1, which was down-regulated in the DS samples. In the full individual gene set, 224 (54%) of the genes were up-regulated and 190 (46%) were down-regulated. There was widespread differential expression between trisomic and euploid fetuses, and clustering based on these genes alone, excluding chromosome 21 genes, is sufficient to separate the euploid and trisomic samples (Fig. 1).

Fig. 1.
Heatmap showing hierarchical clustering of control (C1-C7; solid red bar) and trisomic (T1-T7; solid blue bar) samples. Clustering is based on data from 409 probe sets: the 414 from the Individual gene set, minus the 5 from chromosome 21. If these five ...

Second, gene set enrichment analysis (GSEA) (19) identified a single chromosomal band, chromosome 21, band 22, whose genes were significantly up-regulated as a group [false discovery rate (FDR) q value = 0.006] in DS fetuses. For functional analysis, we selected the 82-gene “Leading Edge” subset (19) of the genes GSEA identified from band chr21q22 (see Materials and Methods and Table S2). Only 3 genes (CLIC6, RUNX1, and C21orf87) are common to both the Individual and Leading Edge gene sets.

To quantify the extent of differential expression of the known trisomic genes, we examined the changes in expression levels of all chromosome 21 probes on our microarrays. For each probe set, we computed the fold-change between its average expression level in the DS samples and its average expression in the controls. A histogram of these changes is shown in Fig. 2A, in which a bold vertical line marks the 1.5-fold up-regulation expected given the increased gene dosage. Overexpression was seen for 65% of the chromosome 21 probes (325 of 501). The mean and median fold-changes were 1.44 and 1.25, respectively, but the range is quite large (from 5-fold down-regulation to 16-fold up-regulation in DS). In contrast, only 50.4% (27,299 of 54,174) of the probes from chromosomes other than 21 are higher in DS (Fig. 2B); this is significantly different from the frequency of overexpression we see on chromosome 21 (χ2 test, P < 0.0006).

Fig. 2.
Histogram of fold-changes in gene expression between the average expression in DS and the average in the controls. (A) Histogram for all 501 probe sets representing genes on chromosome 21. The fold-changes are reported in log-scale (base 2), so that up- ...

Functional Analysis.

We performed pathway analysis of the Individual and Leading Edge gene sets in Database for Annotation, Visualization, and Integrated Discovery (DAVID) (20). Following the suggestions of DAVID's creators (21), we examined all functional annotations with a modified Fisher exact P value (the “EASE” score) <0.1 (see Materials and Methods). The full DAVID results for the 2 gene sets appear in Table S3 and Table S4. We observed several consistent patterns in differential expression in both the Individual and the Leading Edge gene sets (Table S5). Because of the limited overlap between these 2 sets and the size of the functional groups considered, the 2 gene sets can be seen as providing largely independent confirmation of the importance of these functional processes in DS. Therefore, we chose to focus only on functional processes implicated by both gene sets. Using this criterion, the following functions appear to be disrupted in DS (Table S5): oxidative stress, ion transport, G protein signaling, immune and stress response, circulatory system functions, cell structure, sensory perception, and several developmental processes.

Confirmation and Therapeutic Suggestions Using the Connectivity Map.

In an attempt to further confirm the importance of these functional processes, we used the Connectivity Map (17, 18) to identify compounds whose molecular signatures either mimic or counteract that of DS. We found 4 compounds with average connectivity scores >0.7 (indicating a high correlation with the DS molecular signature), and 9 compounds with average connectivity scores less than −0.7 (indicating a high negative correlation). The full results of the Connectivity Map analysis appear in SI Appendix.

The compounds that potentially would reverse our observed DS molecular phenotype (and thus might be candidates for further hypothesis testing in vitro) include NSC-5255229, celastrol, calmidazolium, NSC-5109870, dimethyloxalylglycine, NSC-5213008, verapamil, HC toxin, and felodipine. Celastrol is an antioxidant and anti-inflammatory agent that has been suggested for use in treating Alzheimer disease, which prematurely affects many DS patients (22). Calmidazolium is a calmodulin inhibitor, which decreases sensitivity to calcium ion signaling, and has been considered for use in treating osteoporosis (23). Verapamil and felodipine are both calcium channel blockers, whereas dimethyloxalylglycine is a hydroxylase inhibitor thought to increase resistance to oxidative stress (24, 25). The 4 compounds that most mimic the DS phenotype also relate to potassium and calcium signaling or oxidation. The fact that these results also implicate oxidative stress and ion transport provides a third level of confirmation of the importance of these functional classes.


We have demonstrated that transcriptional profiling of RNA in uncultured amniotic fluid provides a unique molecular window into developmental disorders in the living human fetus. In addition to identifying genes relevant to the DS phenotype, we have used functional profiling to identify significantly disrupted biological pathways.

Among the functional pathway groups identified by both the individual and gene set analyses, several are amenable to a single explanation. Reactive oxygen species, especially hydrogen peroxide, are known to disrupt ion transport mechanisms, leading to problems with signal transduction through cell membranes, cell dysfunction, structural failure of membrane integrity, and ultimately to pathological symptoms, particularly in neural and cardiac tissues (26). We observe consistent evidence of several of these steps, including dysregulation of oxidative stress response genes, phospholipids, ion transport molecules, heart muscle genes, structural proteins, and DNA damage repair genes, in both the Individual and the Leading Edge gene sets (Table S5 and Fig. 3).

Fig. 3.
Putative network of pathways implicated in DS. Significantly implicated processes, based on DAVID functional analysis, are shown in boxes. Edges between boxes represent relationships between functional processes such as G protein signaling and disruptions ...

It has been suggested that oxidative stress plays an important role in DS (27). Because individuals with DS demonstrate pathology consistent with Alzheimer disease at an early age (28), links to the role of oxidative stress in Alzheimer's have been explored (27). Lockstone et al. (29) found that oxidative stress response genes were over-represented in adult but not fetal DS tissue, and suggested that this response might reflect adult-onset DS pathologies such as Alzheimer disease. More recently, a few groups (16, 30) have found oxidative stress response markers in fetal DS tissues, although neither study emphasized this particular result or considered the potential relationship between oxidative stress and other functional pathways. Esposito, et al. (14) identified oxidative stress and apoptosis genes in neural progenitor cell lines generated from the frontal cortex of second trimester DS fetuses. They suggested that up-regulation of the chromosome 21 gene S100B causes an increase in reactive oxygen species and stress-response kinases, leading to an increase in programmed cell death. Using a biochemical approach, other investigators demonstrated increased levels of isoprostanes, a marker of oxidative stress, in second trimester amniotic fluid samples from DS fetuses (31). Our study is the first functional analysis of the DS fetus that implicates not only oxidative stress, but potential intermediate consequences, such as defects in ion transport and G protein signaling.

In mouse models, at least one G protein coupled potassium channel protein (GIRK2) has been implicated in DS pathology (32, 33). Another study using adult mouse models has suggested a role for two other G protein dependent pathways in DS and Alzheimer disease (34). Our work, however, suggests a wider and more fundamental role for G protein signaling, involving a large number of proteins and appearing as early as the second trimester.

Our results contribute to an ongoing debate regarding the extent of transcriptional changes due to trisomy 21. Despite the many prior studies of gene expression in DS, the precise mechanism by which the additional set of chromosome 21 genes disrupts normal development and results in the phenotype of DS remains unknown. Consistent with several previous studies (30, 35, 36), we observe that the trisomic genes generally showed increased expression in DS, with average up-regulation of nearly 1.5-fold (Fig. 2A). However, this effect is highly variable, with nearly a third of the trisomic genes actually down-regulated on average (but few significantly so). We see widespread differential expression of genes from the other diploid chromosomes (Fig. 2B), consistent with the results of Gardiner (37) and Lockstone et al. (29). Hierarchical clustering of our samples based on expression levels of the 409 Individual genes not located on chromosome 21 completely separates the DS samples from the controls (as seen in Fig. 1). In contrast, Mao et al. (30), who studied gene expression from frozen fetal heart and brain tissue, found that explicit classification of their samples worked only when based on the expression levels of the trisomic genes. Differences in expression patterns between their work and the present study may reflect the different tissues involved.

Although previous reports identified significant differential expression of trisomic genes, our analysis did not. We note that, because most of the amniotic fluid RNA is cell-free, care should be taken when comparing our results to previously published transcriptomic profiles of material that used fetal cells or tissue (12, 13, 15, 16, 30, 38, 39). This discrepancy may also be due to comparison with data derived from mouse models of DS, which are more genetically homogeneous than our human population sample. However, most likely the difference is due to our use of a strict statistical cutoff for differential expression, including adjustment for multiple testing of >54,000 probe sets. We found relatively few chromosome 21 genes with such consistent expression in our diverse population that the evidence for their moderate up-regulation exceeded this strict significance cutoff. Fortunately, GSEA was developed precisely to detect such consistent but modest expression changes. With no a priori bias, the GSEA tool identified the DS critical region as the only strongly (q < 0.05) up-regulated chromosomal band in the DS samples.

The addition of the Connectivity Map analysis confirmed the pathways implicated by DAVID and suggested possible testable hypotheses to develop novel treatments for DS, starting with an in vitro approach to explore the effects of either the Connectivity Map-implicated compounds or other compounds with similar effects on oxidation or ion transport. The work described here serves as proof of concept that gene expression profiles from living second trimester human fetuses with developmental disorders can lead to a better understanding of the early etiology of disease and the secondary consequences of congenital anomalies, and may suggest future innovative approaches to treatment.

Materials and Methods


The Institutional Review Boards at Tufts Medical Center and Women and Infants' Hospital approved the study. Residual second trimester amniotic fluid (AF) supernatant samples were obtained from women carrying singleton fetuses undergoing fetal genetic testing for routine clinical indications. All samples were anonymous, although the karyotype result and gestational age were known. AF was stored at −80 °C until RNA extraction. The initial study set consisted of AF samples with the following confirmed metaphase karyotypes: 47,XX,+21 (n = 4); 47,XY,+21 (n = 5); 46, XX (n = 6); 46, XY (n = 6). The gestational ages of the samples ranged from 16 4/7 to 21 4/7 weeks.

RNA Extraction.

RNA was extracted from 10 mL of AF with 30 mL of TRIzol LS Reagent (Invitrogen) and 8 mL of chloroform. After RNA extraction, RNA was purified and DNA was removed using the RNeasy Maxi Kit, including the DNase step according to the manufacturer's protocol (QIAGEN). RNA was precipitated using 3M NaOAc and 100% ethanol, and 80% ethanol was added after 4-h incubation at −20 °C. cDNA, synthesized from extracted RNA, was amplified and purified using the WT-Ovation Pico RNA Amplification System (NuGEN) and the DNA Clean & Concentrator-25 (Zymo Research). The quality and quantity of amplified cDNA was measured on the Agilent Bioanalyzer 2100 Expert software (Agilent) with the RNA 6000 Nano kit (Agilent) .

Fragmentation, Labeling, and Hybridization.

The FL-Ovation cDNA Biotin Module V2 (NuGEN) was used to obtain fragmented and labeled cDNA (minimum 5 mcg) that was suitable for hybridization to Affymetrix U133 Plus 2.0 arrays (Affymetrix). Arrays were washed, stained with streptavidin-phycoerythrin, scanned with the GeneArray Scanner, and analyzed using the GeneChip Microarray Suite 5.0 (Affymetrix). Array quality was assessed in R (version 2.7.2) using the simpleaffy package in BioConductor (version 1.7; available at www.bioconductor.org). Three arrays with scaling factors >22 and <15% present calls were discarded.

Seven samples from DS fetuses remained: 5 males and 2 females. Five gender-matched controls were matched within 4 days of gestational age of the corresponding DS samples; the other two were collected 10 and 12 days earlier than the respective DS samples. A total of 7 DS and 7 matched controls were further analyzed.

Analysis and Statistics.

Normalization was performed with the threestep command from the AffyPLM package in BioConductor, using ideal-mismatch background/signal adjustment, quantile normalization, and the Tukey biweight summary method (40). This summary method includes a logarithmic transformation (40), improving the normality of the data. Identification of individual differentially-expressed genes was performed via 2-sided, paired t tests using the multtest package in BioConductor, with the Benjamini–Hochberg adjustment for multiple testing (41).

Gene Set Enrichment Analysis (GSEA) (19) was performed using the GSEA software version 2.0 and MSigDB version 2.4. This analysis identifies consistent differential expression of sets of genes defined in the MSigDB database. We examined both the functional, curated gene sets (MSigDB collection c2) and gene sets defined by chromosomal bands (MSigDB collection c1), but only the chromosomal band analysis yielded sets that were significant with a false discovery rate (FDR) <0.05. The full results of the chromosomal band analysis appear in Table S6.

To identify the most differentially expressed genes from these statistically significant gene sets, we chose the “leading edge subset,” a group of the most-up-regulated genes in the gene set (19). Specifically, the leading edge subset of a gene set contains the genes that contribute the most to the set's enrichment score (ES), a statistic reflecting the degree to which a gene set is over-represented at the top or bottom of a list of genes ranked by their differential expression.

Functional analysis of gene lists was performed in DAVID (20), using the Panther functional annotation classes (42) in addition to the default pathway selections, which include Gene Ontology (GO) terms (43), pathways defined from the KEGG (44) and BioCarta (www.biocarta.com) databases, InterPro protein families (45), and Protein Information Resource keywords (46). We chose to use DAVID's EASE score rather than the more stringent Benjamini–Hochberg FDR cutoff for the DAVID results because the FDR adjustment assumes independence of the functional pathways (which, in practice, overlap heavily by design), and adjusting for multiple testing in such cases is controversial (47). Therefore, to reduce the possibility of false-positive associations, we focus on only those functional processes represented in the DAVID output for both the Individual and Leading Edge gene sets.

Hierarchical clustering was performed in R, using complete-linkage hierarchical clustering (the hclust function in the stats package), and heatmaps created via the heatmap.2 function in the gplots package, using the “scale = ‘row”’ option to z-score normalize the rows. To identify compounds with molecular signatures that might mimic or mitigate the effects of DS, we used Connectivity Map build 1.0, which contains a database of 564 expression profiles representing the effects of 164 compounds on 4 cancer cell lines, using the Affymetrix U133A microarrays (18). Because the U133 Plus 2.0 arrays used in the present study contain a superset of the probe sets on the U133A arrays, we ran the Connectivity Map analysis using only those probe sets that were common to both arrays.

Supplementary Material

Supporting Information:


We thank the maternal-fetal medicine staffs of both Tufts Medical Center and Women and Infants' Hospital for performing the amniocenteses, Ms. Helene Stroh for technical support, and Dr. Jill Maron for her advice. This work was supported by Eunice Kennedy Shriver National Institute of Child Health and Human Development Awards R01HD042053-06 (to D.W.B.) and R01HD058880-01 (to D.K.S.), and a Tufts Mellon Research Fellowship (to D.K.S.).


The authors declare no conflict of interest.

Data deposition: The data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE16176).

This article contains supporting information online at www.pnas.org/cgi/content/full/0903909106/DCSupplemental.


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