Meta-analysis of heterogeneous Down Syndrome data reveals consistent genome-wide dosage effects related to neurological processes

  • Mireia Vilardell1, 2Email author,

    Affiliated with

    • Axel Rasche1,

      Affiliated with

      • Anja Thormann1,

        Affiliated with

        • Elisabeth Maschke-Dutz1,

          Affiliated with

          • Luis A Pérez-Jurado2, 3,

            Affiliated with

            • Hans Lehrach1 and

              Affiliated with

              • Ralf Herwig1Email author

                Affiliated with

                BMC Genomics201112:229

                DOI: 10.1186/1471-2164-12-229

                Received: 2 December 2010

                Accepted: 11 May 2011

                Published: 11 May 2011

                Abstract

                Background

                Down syndrome (DS; trisomy 21) is the most common genetic cause of mental retardation in the human population and key molecular networks dysregulated in DS are still unknown. Many different experimental techniques have been applied to analyse the effects of dosage imbalance at the molecular and phenotypical level, however, currently no integrative approach exists that attempts to extract the common information.

                Results

                We have performed a statistical meta-analysis from 45 heterogeneous publicly available DS data sets in order to identify consistent dosage effects from these studies. We identified 324 genes with significant genome-wide dosage effects, including well investigated genes like SOD1, APP, RUNX1 and DYRK1A as well as a large proportion of novel genes (N = 62). Furthermore, we characterized these genes using gene ontology, molecular interactions and promoter sequence analysis. In order to judge relevance of the 324 genes for more general cerebral pathologies we used independent publicly available microarry data from brain studies not related with DS and identified a subset of 79 genes with potential impact for neurocognitive processes. All results have been made available through a web server under http://​ds-geneminer.​molgen.​mpg.​de/​.

                Conclusions

                Our study represents a comprehensive integrative analysis of heterogeneous data including genome-wide transcript levels in the domain of trisomy 21. The detected dosage effects build a resource for further studies of DS pathology and the development of new therapies.

                Background

                Down syndrome (DS) is the most frequent genomic aneuploidy with an incidence of approximately 1 in 700 live-newborn [1] resulting from the presence of an extra copy of human chromosome 21 (HSA21). DS is characterized by a complex phenotype with features that are not fully penetrant. The most frequent manifestations, which are virtually always present, include mental retardation, morphological abnormalities of the head and limbs, short stature, hypotonia and hyperlaxity of ligaments. Other features occur with less frequency such as organ malformations, particularly of the heart (50% of DS newborns), several types of gastrointestinal tract obstructions or dysfunctions (4-5% of DS newborns), increased risk of leukaemia (20 × higher compared to the normal population), and early occurrence of an Alzheimer-like neuropathology [2, 3]. DS has been investigated with multiple functional genomics studies aiming to understand the molecular basis underlying the various aspects of the disease [47].

                The most commonly accepted pathogenetic hypothesis is that the dosage imbalance of genes on HSA21 is responsible for the molecular dysfunctions in DS, meaning that genes on the triplicated chromosome are overexpressed due to an extra chromosome 21, as demonstrated for selected genes like SOD1 and DYRK1A [8]. Recent global transcriptome studies with microarrays, however, have generated a more complex picture in the sense that not all HSA21 genes have an elevated expression level as expected [9, 10]. An alternative hypothesis is that the phenotype is due to an unstable environment resulting from the dosage imbalance of the hundreds of genes on HSA21 which determines a non-specific disturbance of genomic regulation and expression. The significantly higher inter-individual variability in DS, as compared to euploid, individuals supports this hypothesis [11]. Moreover, the two hypotheses could be coexistent [3]. In both hypotheses it is understood that besides alterations of gene expression of HSA21 genes there are numerous genome-wide effects that lead to the dysregulation of many non-HSA21 genes through molecular pathways and interactions.

                Many studies on the transcriptome and proteome levels have been conducted to understand the causal relationship between genes at dosage imbalance and DS phenotypes [12]. Gene expression profiles have been analysed from DS fetal [13] and adult human tissues [6]. Additionally, two classes of mouse models [14] have been developed for investigating the molecular genetics of DS, either mouse models with partial trisomies of the syntenic regions of HSA21 in mouse chromosomes 10, 16 or 17, such as Ts16 [15], Ts65Dn [16] and Ts1Cje mice [17], or transgenic mice for specific genes such as SOD1 [18]. Studies of gene expression profiles in human DS samples and mouse models have shown high genome-wide variability [11, 1922]. Furthermore, differences due to the applied experimental platforms, specific tissues, developmental stages or the triplicated segments under study introduce a high variation to the assessment of genome-wide effects of DS. Here, integrative and comparative studies are pivotal for the analysis of the complex nature of gene expression and regulation in DS at a more general level [2, 23].

                Meta-analysis was proven to be a valid strategy to extract consistent information from heterogeneous data, in particular with respect to complex phenotypes for example cancer [24], Alzheimer [25] and type-2 diabetes mellitus [26]. The purpose of meta-analysis is to compensate experiment-specific variations and to reveal consistent information across a wide range of experiments. To date, such a meta-analysis of DS data is missing.

                In this paper we describe a comprehensive meta-analysis from 45 different DS studies on human and mouse on the transcriptome and proteome level including quantitative data such as Affymetrix microarrays, RT-PCR and MALDI studies as well as qualitative data such as SAGE and Western blot analyses. We applied an established computational framework [26] and identified 324 genes with consistent dosage effects in many of these studies. As expected, we observed a high fraction of HSA21 genes (N = 77) but also a large amount of non-HSA21 genes (N = 247). Besides well investigated genes in the context of DS we detected a significant proportion of novel ones (N = 62). The 324 genes were further investigated using functional information, molecular interactions and promoter analysis revealing over-represented motifs of four transcription factors: RUNX1, E2F1, STAF/PAX2 and STAT3. In order to test the relevance of the 324 genes for more general brain phenotypes we used independent publicly available data on cerebral pathologies not related to DS and identified a subset of 79 DS genes that were differentially expressed in these studies. The detected dosage effects can be used as a resource for further studies of DS pathology, functional experiments and the development of therapies. All data have been agglomerated and made available through a web server that tracks results of the meta-analysis http://​ds-geneminer.​molgen.​mpg.​de/​ and that enables the community to validate any gene of interest in the light of the experimental data.

                Results

                Genome-Wide Dosage Effects

                Genome-wide dosage effects were computed with the numerical scoring method described in Material and Methods. In total, 45 case-control experiments were interrogated (Additional file 1, Table S1), the alteration for each gene between the trisomic and normal states was scored in each experiment, gene scores were summarised across all experiments and the significance of the summarised scores was judged with a Bootstrap approach. This procedure resulted in a cut-off score value of 3.67 and identified 324 genes as being predominantly affected by DS. The thirty genes with the highest dosage effects, either on HSA21 or on other chromosomes, are listed in Table 1. The entire gene list is given in Additional file 1, Table S2.
                Table 1

                Top thirty DS dosage effects on A) HSA21 and B) other chromosomes

                A) Direct effects

                        

                Ensembl

                HUGO

                Score

                Entropy

                Chromo-some

                Start

                position

                End

                position

                Band

                CNV

                ENSG00000154734

                ADAMTS1

                18.487

                4.083

                chr21

                28208606

                28217728

                q21.3

                 

                ENSG00000159228

                CBR1

                17.518

                4.509

                chr21

                37442239

                37445464

                q22.12

                 

                ENSG00000159140

                SON

                15.920

                4.712

                chr21

                34914924

                34949812

                q22.11

                 

                ENSG00000142168

                SOD1

                15.817

                4.372

                chr21

                33031935

                33041244

                q22.11

                 

                ENSG00000182670

                TTC3L, TTC3

                15.637

                4.542

                chr21

                38445526

                38575413

                q22.13

                 

                ENSG00000142192

                APP

                15.489

                4.412

                chr21

                27252861

                27543446

                q21.3

                 

                ENSG00000159128

                IFNGR2

                15.006

                4.640

                chr21

                34757299

                34851655

                q22.11

                 

                ENSG00000182240

                BACE2

                14.156

                4.140

                chr21

                42539728

                42648524

                q22.2

                 

                ENSG00000156256

                USP16

                13.713

                4.378

                chr21

                30396950

                30426809

                q21.3

                 

                ENSG00000159131

                GART

                13.570

                4.564

                chr21

                34870940

                34915797

                q22.11

                 

                ENSG00000157540

                DYRK1A

                13.163

                4.405

                chr21

                38739236

                38887680

                q22.13

                 

                ENSG00000157557

                ETS2

                13.088

                4.440

                chr21

                40177231

                40196879

                q22.2

                 

                ENSG00000159231

                CBR3

                12.185

                3.954

                chr21

                37507210

                37518864

                q22.12

                YES

                ENSG00000159082

                SYNJ1

                11.880

                4.284

                chr21

                33997269

                34100359

                q22.11

                 

                ENSG00000142188

                TMEM50B

                11.516

                3.803

                chr21

                34804792

                34853499

                q22.11

                YES

                ENSG00000159110

                IFNAR2

                11.280

                4.360

                chr21

                34602206

                34656082

                q22.11

                 

                ENSG00000157538

                DSCR3

                11.254

                4.316

                chr21

                38591910

                38640262

                q22.13

                 

                ENSG00000157601

                MX1

                10.976

                3.545

                chr21

                42792231

                42831141

                q22.3

                 

                ENSG00000159267

                HLCS

                10.764

                4.183

                chr21

                38123493

                38362536

                q22.13

                YES

                ENSG00000159200

                RCAN1

                10.719

                3.356

                chr21

                35885440

                35987441

                q22.12

                 

                ENSG00000159147

                DONSON

                10.435

                4.295

                chr21

                34947783

                34961014

                q22.11

                 

                ENSG00000156261

                CCT8

                10.361

                4.560

                chr21

                30428126

                30446118

                q21.3

                 

                ENSG00000183486

                MX2

                10.179

                3.598

                chr21

                42733870

                42781317

                q22.3

                 

                ENSG00000154727

                GABPA

                9.936

                4.032

                chr21

                27106881

                27144771

                q21.3

                 

                ENSG00000160200

                CBS

                9.284

                3.907

                chr21

                44473301

                44497053

                q22.3

                 

                ENSG00000159216

                RUNX1

                9.129

                3.783

                chr21

                36160098

                37357047

                q22.12

                 

                ENSG00000183527

                PSMG1

                8.903

                3.733

                chr21

                40546695

                40555777

                q22.2

                 

                ENSG00000182093

                WRB

                8.837

                4.149

                chr21

                40752170

                40800454

                q22.2

                 

                ENSG00000154736

                ADAMTS5

                8.746

                4.221

                chr21

                28290231

                28338832

                q21.3

                 

                ENSG00000159197

                KCNE2

                8.654

                3.660

                chr21

                35736323

                35743440

                q22.11

                YES

                B) Indirect effects

                        

                Ensembl

                HUGO

                Score

                Entropy

                Chromo-some

                Start

                position

                End

                position

                Band

                CNV

                ENSG00000117289

                TXNIP

                8.790

                3.281

                chr1

                145438469

                145442635

                q21.1

                YES

                ENSG00000133110

                POSTN

                8.301

                2.437

                chr13

                38136722

                38172981

                q13.3

                YES

                ENSG00000118785

                SPP1

                7.232

                3.159

                chr4

                88896802

                88904563

                q22.1

                 

                ENSG00000113140

                SPARC

                7.035

                3.338

                chr5

                151040657

                151066726

                q33.1

                 

                ENSG00000125968

                ID1

                6.987

                3.164

                chr20

                30193086

                30194318

                q11.21

                 

                ENSG00000136235

                GPNMB

                6.943

                2.047

                chr7

                23275586

                23314727

                p15.3

                 

                ENSG00000171951

                SCG2

                6.747

                2.950

                chr2

                224461658

                224467221

                q36.1

                 

                ENSG00000135821

                GLUL

                6.604

                3.702

                chr1

                182350839

                182361341

                q25.3

                 

                ENSG00000123610

                TNFAIP6

                6.575

                2.377

                chr2

                152214106

                152236560

                q23.3

                 

                ENSG00000118523

                CTGF

                6.567

                2.996

                chr6

                132269316

                132272513

                q23.2

                 

                ENSG00000168209

                DDIT4

                6.477

                3.318

                chr10

                74033678

                74035794

                q22.1

                 

                ENSG00000162407

                PPAP2B

                6.350

                3.343

                chr1

                56960419

                57110974

                p32.2

                 

                ENSG00000038427

                VCAN

                6.240

                2.958

                chr5

                82767284

                82878122

                q14.2

                 

                ENSG00000151491

                EPS8

                6.194

                3.143

                chr12

                15773076

                15942510

                p12.3

                 

                ENSG00000189067

                LITAF

                6.185

                3.255

                chr16

                11641582

                11680806

                p13.13

                 

                ENSG00000164692

                COL1A2

                6.148

                2.852

                chr7

                94023873

                94060544

                q21.3

                 

                ENSG00000204388

                HSPA1B

                6.109

                2.550

                chr6

                31795688

                31798031

                p21.33

                 

                ENSG00000162692

                VCAM1

                6.012

                2.533

                chr1

                101185305

                101204601

                p21.2

                 

                ENSG00000154096

                THY1

                5.974

                3.244

                chr11

                119288888

                119293854

                q23.3

                 

                ENSG00000135919

                SERPINE2

                5.904

                3.048

                chr2

                224839765

                224904036

                q36.1

                 

                ENSG00000172201

                ID4

                5.887

                3.037

                chr6

                19837617

                19840915

                p22.3

                 

                ENSG00000114315

                HES1

                5.884

                2.874

                chr3

                193853934

                193856521

                q29

                 

                ENSG00000172893

                DHCR7

                5.883

                3.441

                chr11

                71145457

                71159477

                q13.4

                 

                ENSG00000204262

                COL5A2

                5.857

                3.174

                chr2

                189896622

                190044605

                q32.2

                 

                ENSG00000149257

                SERPINH1

                5.846

                3.146

                chr11

                75273170

                75283844

                q13.5

                 

                ENSG00000176697

                BDNF

                5.805

                2.416

                chr11

                27676440

                27743605

                p14.1

                 

                ENSG00000182551

                ADI1

                5.782

                3.081

                chr2

                3501693

                3523507

                p25.3

                 

                ENSG00000079739

                PGM1

                5.661

                3.251

                chr1

                64058947

                64125916

                p31.3

                 

                ENSG00000108821

                COL1A1

                5.527

                3.004

                chr17

                48260650

                48278993

                q21.33

                 

                ENSG00000187498

                COL4A1

                5.514

                3.396

                chr13

                110801318

                110959496

                q34

                 
                The meta-analysis identified genes that showed consistent changes in many of the different experiments rather than genes that were affected by a single (or few) experiment(s) (Figure 1A). This is an important fact since, for example, different mouse models have different coverage of triplicated HSA21 genes, and, thus, might introduce model-specific bias [14]. The consistency of the dosage effect was measured for each gene with an entropy criterion (see Materials and Methods) and Figure 1A reveals a strong preference for the selection of high-entropy genes. Highest scores were assigned to HSA21 genes (Figure 1B) what indicates that the meta-analysis scores reflect the effect of an extra chromosome 21 on gene expression (Table 1). While proportionally most dosage effects were identified for HSA21 genes (77 out of 324), the majority of genes (247 out of 324) was located on other chromosomes highlighting the genome-wide impact of DS (Figure 1C).
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_3346_Fig1_HTML.jpg
                Figure 1

                Characterization of dosage effects. A) Entropy (Y-axis) vs. score of dosage effect (X-axis) for all genes, B) Histogram of scores for all 255 HSA21 genes accessible with the experiments under study, C) Distribution of genomic locations of the 324 candidate genes, D) Cytogenetic location of 77 HSA21 genes that show significant dosage effects for all experiments (blue line). Additionally, the same meta-analysis approach has been conducted with human (green line) and mouse (red line) data separately. The yellow line plots the relative number of HSA21 genes within each band (gene density). Y-axis shows percentage of significant genes with respect to all genes annotated for the chromosomal band.

                Genome-wide dosage effects underlined the severe phenotypic consequences of DS caused by genes with a major role in human development (Additional file 2, Figure S1). Of the 247 non-HSA21 genes, 72 were associated with development, in particular with respect to organ development (62 genes, GO:0048513), tissue development (34 genes GO:0009888) and cell development (30 genes, GO:0048468). Amongst these genes were known interactors of HSA21 genes, for example REST (RE1-silencing transcription factor). REST modulates expression of genes encoding fundamental neuronal functions including ion channels, synaptic proteins and neurotransmitter receptors and has been linked to an inherited form of mental retardation. Recently, Canzonetta et al. [5] demonstrated that the region capable of affecting REST levels, in both mouse and human cells, could be assigned to the DYRK1A locus on HSA21 which was found among the top-scoring HSA21 genes (Table 1).

                TXNIP (thioredoxin interacting protein) had the highest dosage effect (8.79) of all non-HSA21 genes. It has weak association with DS yet (through S100B [27]) but could play a major role for several DS phenotypes. It is a key signalling molecule involved in glucose homeostasis [28], cardiovascular homeostasis [29] and leukaemia [30].

                Enrichment of genomic location with respect to the 324 genes was observed in regions of HSA21 and the respective syntenic regions on mouse chromosomes 16, 17 and 10 (Additional file 3, Figure S2). Moreover, in the human genome, additional enrichment on chr3q24 was computed containing the genes GYG1 (glicogenin), PLOD2 (involved in bone morphogenesis), PLSCR4 and CHST2 (involved in inflammatory response in vascular endothelial cells).

                Dosage Effects on HSA21

                Proportionally HSA21 contributed mostly to the detected dosage effects (Figure 1C). On the other hand, it is remarkable that only a third of all HSA21 genes (77 out of 255 studied here using the Ensembl genome annotation [31]) showed consistent effects across the different experiments (see also Discussion). While 57 genes had a positive score below the significance threshold of 3.67 indicating relevance with respect to specific experiments only, 121 genes had a score near zero indicating that dosage effects were either compensated or not detected with the selected experimental data (Figure 1B).

                HSA21 dosage effects included, for example APP (beta-amyloid precursor protein) involved in senile plaque formation in DS and Alzheimer's disease [3], SOD1 (superoxide dismutase 1), a key enzyme in the metabolism of oxygen-derived free radicals [3], DYRK1A (dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A) involved in neuroblast proliferation, crucial for brain function, learning and memory [32], RUNX1 (runt-related transcription factor 1) which plays a critical role in normal hematopoiesis [33], or GABPA (GA binding protein transcription factor, alpha subunit 60 kDa) encoding a DNA binding domain with a huge variety of targets including genes from different cell/tissue specificities and functions [34]. HSA21 genes were mostly up-regulated in gene expression studies (69 out of 77) with the exception of eight genes that were either variable or down-regulated (SLC5A3, MRPS6, B3GALT6, CBS, KCNJ6, KCNJ15, CLDN14, COL18A1). Possible explanations for this observation might be tissue-specificity of gene expression as in the case of MRPS6 which was mostly up-regulated in brain samples and down-regulated in other tissues like heart or kidney, or differences in human and mouse gene expression as in the case of CBS which was up-regulated in human but down-regulated in mouse experiments what might be caused by differential tissue specificity of the orthologous mouse gene [35].

                Three genomic regions on HSA21 were enriched with the significant genes using the MSigDB_c1 positional database: chr21q22, chr21q21 and chr21q11, located on the q-terminal arm (Figure 1D). This contradicts the hypothesis that a single region on HSA21 could be responsible for the molecular and phenotypic consequences of DS with only a few responsive genes [36, 37]. Rather our findings support studies that identified more than one HSA21 region causative for DS phenotypes so that the dosage effects were not uniformly distributed along the chromosome but rather enriched in certain regions on HSA21 similar to the results in [38, 39].

                Functional Annotation Using Gene Enrichment Analysis

                Functional annotation of biological pathways was retrieved from the ConsensusPathDB [40], a meta-database that summarizes the content of 22 human interaction databases. A total of 1,695 pre-defined pathways were screened with the 324 genes using gene set enrichment analysis [41]. A total of 277 pathways were found significantly enriched (family-wise error rate (FWER)<0.01) of which several pathways were associated with neurological and neuropathological processes (Table 2). These pathways referred mainly to (i) neurodegeneration (e.g. Huntington's disease, Alzheimer's disease or Parkinson's disease) and (ii) defects in synapsis (e.g. Axon guidance, NGF signaling). Furthermore, the results emphasized the role of tyrosine-kinase receptors in DS pathology (for example P75(NTR)- mediating signalling or NGF signalling via TRKA) which interact directly with BDNF (brain-derived neurotrophic factor). Moreover, our results showed gene dosage effects caused either directly by genes located on HSA21 (e.g. SOD1, APP, DONSON, TIAM1, COL6A2, ITSN1 and BACE2) or indirectly by HSA21 interactors, highlighting the intrinsic complexity of the DS pathology. For example, PIK3R1 de-regulation impacts on many of these pathways and is a direct interactor of IFNAR1, a significant DS gene. A similar effect can be observed for TPJ1A that has interactions with HSA21 genes JAM2 and CDLN8 both showing consistent dosage effects (cf. Figure 2A).
                Table 2

                Enriched neuropathological pathways.

                PATHWAY (Source Database)

                Pathway

                size

                P-value

                FWER P-value

                Genes on HSA21

                HSA21 Interactors

                Others

                HUNTINGTONS DISEASE (KEGG)

                159

                0

                0

                SOD1; DONSON

                REST

                BDNF; SOD2

                ALZHEIMERS DISEASE (KEGG)

                147

                0

                0

                APP; BACE2; DONSON

                PPP3CA; GSK3B

                CAPN2

                SIGNALLING BY NGF (REACTOME)

                209

                0

                0

                ITSN1; TIAM1

                PIK3R1; GSK3B

                RPS6KA2; RAP1A; KRAS

                AXON GUIDANCE (REACTOME)

                256

                0

                0

                COL6A2

                GSK3B;COL1A1; COL1A2; COL4A1; COL4A2

                COL5A2; DPYSL3; RPS6KA2; LAMB1; COL3A1; COL5A1; ALCAM; KRAS

                PARKINSONS DISEASE (KEGG)

                105

                0

                0

                DONSON

                 

                UBE2G2

                P75(NTR)-MEDIATED SIGNALING (PID)

                68

                0

                0

                APP

                PIK3R1

                BDNF

                NOTCH (NETPATH)

                61

                0

                0

                APP

                PIK3R1; GSK3B

                 

                NEUROTROPHIN SIGNALING PATHWAY (KEGG)

                121

                0

                0

                 

                PIK3R1; GSK3B

                BDNF; RPS6KA2; RAP1A; KRAS

                NGF SIGNALLING VIA TRKA FROM THE PLASMA MEMBRANE (REACTOME)

                127

                0

                0

                 

                PIK3R1; GSK3B

                RPS6KA2; RAP1A; KRAS

                MEMBRANE TRAFFICKING (REACTOME)

                87

                0

                0

                 

                TJP1

                GJA1; COPG

                NEUROTROPHIC FACTOR-MEDIATED TRK RECEPTOR SIGNALING (PID)

                60

                0

                0

                TIAM1

                PIK3R1

                BDNF; RAP1A; KRAS

                EPO SIGNALING (INOH)

                180

                0

                0

                 

                PIK3R1; GSK3B

                 

                CDC42 SIGNALING EVENTS (PID)

                68

                0

                0

                TIAM1

                PIK3R1; GSK3B

                EPS8; YES1

                L1CAM INTERACTIONS (REACTOME)

                93

                0

                0

                  

                LAMB1; ALCAM1; RPS6KA2

                http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_3346_Fig2_HTML.jpg
                Figure 2

                Molecular interactions of HSA21 genes. A) Interactions of HSA21 genes (red) with non-HSA21 genes (other colours). Same colours of the gene nodes refer to the same chromosome. B) Example of consistent down-regulation of DNAJB1 as a consequence of HSA21 imbalance visualized in the web browser.

                Dosage Effects on Transcriptional Regulation

                Dysregulation of transcriptional regulation is widely reported in DS [34]. Among the 324 significant genes were 13 transcription factors (TFs) (PSIP1, RBPJ, TCF4, HES1, ETS2, BACH1, RUNX1, GABPA, SNAI2, REST, LITAF, EGR1, FOS), 6 TFs (PSIP1, HOXC8, DLX5, HIVEP3, ZNF187, ATF6) had significant enrichment of their targets as retrieved by the TRANSFAC [42] database. Additionally, 57 TFs had significant enrichment of their interacting proteins when judged with physical interactions retrieved from the ConsensusPathDB [40]. In total, 70 different TFs were identified as being (directly or indirectly) affected by dosage imbalances. The list of TFs and their associated functional categories is given in Additional file 1, Table S3. GO categories indicate a broad impact of transcriptional regulation for neurological development, the central nervous system development (RUNX1 and TP53), nervous system development (DLX5, FOS, HES1, STAT3 and EP300), axonogenesis (DLX5, NOTCH1 and CREB1), neuron differentiation (HOXC8, NOTCH1 and RUNX1), negative regulation of neuron differentiation (HES1, NOTCH1 and REST) and regulation of long-term neuronal synaptic plasticity and learning or memory (EGR1 and JUN). Other prominent categories refer to organ development (RBPJ, ETS2, GABPA and SNAI2) and stress response (ATF6, FOS and RELA).

                We further analyzed the promoter sequences of the 324 genes for enrichment of transcription factor binding sites using the AMADEUS software [43]. Significant enrichment was computed for 4 TF motifs, E2F1, RUNX1, STAF/PAX2 and STAT3 (Table 3). Enrichment was evident for RUNX1, which is among the most studied genes implicated in DS. The implication of E2F1 in DS was also previously reported [34] and could be responsible for impaired cell proliferation documented for hippocampus, cerebellum and astrocytes of DS mouse models.
                Table 3

                Enriched TFBSs.

                TF

                Description

                Cromo-some

                P-Value

                Binding motif

                Strand

                E2F1

                E2F transcription factor 1

                chr20

                9.3*10-18

                [C/t][C/a][G/c]C[c/a][C/g][G/c][C/T][G/c]A

                -

                RUNX1

                runt-related transcription factor 1

                chr21

                4.0*10-18

                [C/a/t][T/a/g][G/C]{A}[G/c]{A}T[C/A][G][C/a/t/g]

                +

                STAF/

                PAX2

                paired box 2

                chr10

                8.4*10-18

                [A/g][A/g]A[C/T/a][T/g/a][T/c][C/t][C/g][C/a]

                +

                STAT3

                signal transducer and activator of transcription 3 (acute-phase response factor)

                chr17

                8.4*10-17

                GAA[A/T][C/T]G[C/T][C/g/t][A/T][C/T/g]

                +

                Binding motifs have been represented using the IUPAC nomenclature and incorporating lower case for low frequency bases.

                Dosage Effects and Molecular Interactions

                Molecular interactions among the 324 significant genes on HSA21 and on other chromosomes exhibited a complex network supporting the important role of physical interactions as transmitter of dosage effects (Figure 2A). The consequences of HSA21 triplication on the interacting genes was fairly stable as Figure 2B demonstrates. For example, DNAJB1 (DnaJ (Hsp40) homolog, subfamily B, member 1) and PPP3CA (protein phosphatase 3, catalytic subunit, alpha isozyme, data not shown), both interacting with SOD1, were consistently and significantly down-regulated in the human microarray experiments as the fold-changes and P-values indicate. Opposite trends were observed for TJP1 and RHOQ.

                Assessing General Relevance of DS Dosage Effects for Neurological Processes

                We were further interested in identifying, among the 324 genes, those which were relevant for other brain disorders. To achieve this, we interrogated 19 independent data sets derived from publicly available microarray data (Additional file 1, Table S4). These studies followed heterogeneous research questions on different cerebral pathologies and identified a total of 623 differentially expressed genes. Gene set enrichment analyses [41] with the 324 genes and the corresponding lists of differentially expressed genes were significant for 10 of these 19 studies with 79 overlapping genes (Figure 3A). Furthermore, we used the HSA21 database http://​chr21.​molgen.​mpg.​de/​hsa21[4], a resource of RNA in situ hybridizations in postnatal mouse brain sections, in order to provide independent supporting evidence of brain expression of these 79 genes as shown for example for BACH1 (basic leucine zipper transcription factor 1) and TTC3 (tetratricopeptide repeat domain 3) (Figure 3B and 3C).
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_3346_Fig3_HTML.jpg
                Figure 3

                Brain-related dosage effects. A) Venn diagram showing the overlap of the 324 significant genes with 623 genes identified by independent mouse studies related to brain phenotypes; B) RNA in situ hybridisations of BACH1 in postnatal mouse embryonic brain slices. C) In situ hybridisation of TTC3 in the same tissue. Images kindly provided by the HSA21 consortium ([4]; http://​chr21.​molgen.​mpg.​de/​hsa21). D) Hierarchical clustering of 79 genes related to non-DS general brain disorders with the DS gene expression data sets. Clustering was performed with the J-Express 2009 software using Pearson correlation as similarity measure and complete linkage as update rule.

                Additionally, we investigated the expression patterns of the 79 genes across the DS microarray experiments used for this meta-analysis and could identify brain-related signatures, for example, a clear up-regulation in brain tissues for the cluster containing C14orf147, IVSNS1ABP, B2M, TPJ1, SPARC, CTGF, COL4A1 and FSTL1 (Figure 3D).

                Novel Dosage Effects

                To identify DS-relevant "novel" dosage effects we excluded from the 324 genes (i) HSA21 genes, (ii) genes that interacted with HSA21 genes, as well as (iii) genes that were associated with DS in the literature (Table 4). Remaining candidates (N = 62) comprised BDNF-related genes (SST), MAPK-pathway genes (KRAS, IGF1R, GNG11 and RAP1A), genes related with leukemia (SFRP1) and Rho-Proteins (DHCR7 and RAB21). SST was found as co-expressed in previous studies with TAC1 [44] which is also significant in our meta-analysis and both showed a strong correlation across DS studies (Figure 4A).
                Table 4

                Novel DS dosage effects

                Ensembl

                HUGO

                Score

                Entropy

                Chromo-some

                Start

                position

                End

                position

                Band

                CNV

                ENSG00000133110

                POSTN

                8.301

                2.437

                chr13

                38136722

                38172981

                q13.3

                YES

                ENSG00000135919

                SERPINE2

                5.904

                3.048

                chr2

                224839765

                224904036

                q36.1

                 

                ENSG00000172893

                DHCR7

                5.883

                3.441

                chr11

                71145457

                71159477

                q13.4

                 

                ENSG00000135744

                AGT

                5.467

                2.799

                chr1

                230838269

                230850043

                q42.2

                 

                ENSG00000159176

                CSRP1

                5.424

                3.136

                chr1

                201452658

                201478584

                q32.1

                 

                ENSG00000178695

                KCTD12

                5.344

                2.373

                chr13

                77454312

                77460540

                q22.3

                 

                ENSG00000183087

                GAS6

                5.129

                2.904

                chr13

                114523524

                114567046

                q34

                 

                ENSG00000164106

                SCRG1

                5.127

                2.728

                chr4

                174309300

                174320617

                q34.1

                 

                ENSG00000166923

                GREM1

                5.073

                1.486

                chr15

                33010175

                33026870

                q13.3

                YES

                ENSG00000163754

                GYG1

                4.933

                3.129

                chr3

                148709128

                148745419

                q24

                 

                ENSG00000155380

                SLC16A1

                4.878

                2.927

                chr1

                113454469

                113499635

                p13.2

                YES

                ENSG00000166033

                HTRA1

                4.811

                3.101

                chr10

                124221041

                124274424

                q26.13

                YES

                ENSG00000145632

                PLK2

                4.785

                2.811

                chr5

                57749809

                57756087

                q11.2

                 

                ENSG00000115380

                EFEMP1

                4.726

                2.257

                chr2

                56093102

                56151274

                p16.1

                 

                ENSG00000060237

                WNK1

                4.637

                2.765

                chr12

                862089

                1020618

                p13.33

                YES

                ENSG00000103888

                KIAA1199

                4.581

                0.885

                chr15

                81071684

                81244117

                q25.1

                 

                ENSG00000113810

                SMC4

                4.462

                3.372

                chr3

                160117062

                160152750

                q25.33

                 

                ENSG00000198356

                ASNA1

                4.431

                3.067

                chr19

                12848306

                12859137

                p13.2

                 

                ENSG00000122952

                ZWINT

                4.415

                3.266

                chr10

                58116989

                58121036

                q21.1

                 

                ENSG00000157005

                SST

                4.401

                1.954

                chr3

                187386694

                187388187

                q27.3

                 

                ENSG00000117519

                CNN3

                4.384

                3.253

                chr1

                95362507

                95392834

                p21.3

                 

                ENSG00000107104

                KANK1

                4.352

                2.508

                chr9

                470291

                746105

                p24.3

                YES

                ENSG00000151414

                NEK7

                4.329

                1.848

                chr1

                198126121

                198291550

                q31.3

                 

                ENSG00000044574

                HSPA5

                4.261

                3.449

                chr9

                127997132

                128003609

                q33.3

                 

                ENSG00000128590

                DNAJB9

                4.251

                3.241

                chr7

                108210012

                108215294

                q31.1

                 

                ENSG00000127920

                GNG11

                4.226

                2.747

                chr7

                93551011

                93555831

                q21.3

                 

                ENSG00000008083

                JARID2

                4.161

                3.203

                chr6

                15246527

                15522253

                p22.3

                 

                ENSG00000119938

                PPP1R3C

                4.159

                3.036

                chr10

                93388199

                93392858

                q23.32

                 

                ENSG00000049245

                VAMP3

                4.146

                3.036

                chr1

                7831329

                7841492

                p36.23

                 

                ENSG00000120694

                HSPH1

                4.129

                3.310

                chr13

                31710762

                31736502

                q12.3

                 

                ENSG00000168214

                RBPJ

                4.127

                3.291

                chr4

                26321332

                26436753

                p15.2

                 

                ENSG00000162909

                CAPN2

                4.111

                3.020

                chr1

                223889347

                223963720

                q41

                YES

                ENSG00000166147

                FBN1

                4.106

                2.070

                chr15

                48700505

                48937918

                q21.1

                YES

                ENSG00000100941

                PNN

                4.081

                3.380

                chr14

                39644425

                39652422

                q21.1

                 

                ENSG00000132640

                BTBD3

                4.074

                3.478

                chr20

                11871371

                11907257

                p12.2

                YES

                ENSG00000128708

                HAT1

                4.064

                3.158

                chr2

                172778958

                172848599

                q31.1

                YES

                ENSG00000176105

                YES1

                4.047

                2.855

                chr18

                721588

                812327

                p11.32

                 

                ENSG00000152377

                SPOCK1

                4.025

                3.083

                chr5

                136310987

                136835037

                q31.2

                 

                ENSG00000136026

                CKAP4

                4.018

                2.754

                chr12

                106631659

                106641908

                q23.3

                 

                ENSG00000198121

                LPAR1

                3.979

                2.858

                chr9

                113635543

                113800738

                q31.3

                 

                ENSG00000140443

                IGF1R

                3.951

                3.376

                chr15

                99192200

                99507759

                q26.3

                 

                ENSG00000198730

                CTR9

                3.891

                3.310

                chr11

                10772803

                10801287

                p15.3

                 

                ENSG00000162616

                DNAJB4

                3.869

                3.035

                chr1

                78444859

                78483648

                p31.1

                 

                ENSG00000104332

                SFRP1

                3.825

                2.587

                chr8

                41119483

                41166992

                p11.21

                 

                ENSG00000116473

                RAP1A

                3.824

                2.769

                chr1

                112084840

                112259313

                p13.2

                 

                ENSG00000172500

                FIBP

                3.804

                3.309

                chr11

                65651211

                65656010

                q13.1

                YES

                ENSG00000133703

                KRAS

                3.801

                3.338

                chr12

                25358182

                25403854

                p12.1

                 

                ENSG00000163032

                VSNL1

                3.798

                3.099

                chr2

                17720393

                17838285

                p24.2

                 

                ENSG00000134684

                YARS

                3.765

                3.431

                chr1

                33240840

                33283754

                p35.1

                 

                ENSG00000105854

                PON2

                3.764

                2.862

                chr7

                95034179

                95064510

                q21.3

                 

                ENSG00000148943

                LIN7C

                3.763

                3.033

                chr11

                27516124

                27528303

                p14.1

                 

                ENSG00000162734

                PEA15

                3.747

                3.418

                chr1

                160175127

                160185166

                q23.2

                 

                ENSG00000103187

                COTL1

                3.731

                3.304

                chr16

                84599200

                84651683

                q24.1

                YES

                ENSG00000198648

                STK39

                3.722

                3.439

                chr2

                168810530

                169104651

                q24.3

                 

                ENSG00000100577

                GSTZ1

                3.713

                2.759

                chr14

                77787230

                77797939

                q24.3

                 

                ENSG00000080371

                RAB21

                3.707

                3.312

                chr12

                72148658

                72181150

                q21.1

                YES

                ENSG00000136108

                CKAP2

                3.688

                2.960

                chr13

                53029495

                53050485

                q14.3

                 

                ENSG00000066583

                ISOC1

                3.686

                2.655

                chr5

                128430442

                128449721

                q23.3

                 

                ENSG00000143420

                ENSA

                3.681

                3.276

                chr1

                150573327

                150602098

                q21.3

                 

                ENSG00000114353

                GNAI2

                3.680

                3.138

                chr3

                50263724

                50296787

                p21.31

                YES

                ENSG00000140105

                WARS

                3.671

                2.994

                chr14

                100800125

                100842680

                q32.2

                 

                ENSG00000018625

                ATP1A2

                3.670

                2.733

                chr1

                160085549

                160113381

                q23.2

                 
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_3346_Fig4_HTML.jpg
                Figure 4

                Novel DS dosage effects visualised with the web browser. A) SST and TAC1 have been previously reported as acting in a complex. The deregulated profile of these genes correlates was shown here with the fold-change view of the web browser. B) HSPA5 is a novel gene for DS implicated in neurodegeneration which is also a target of the ATF6 TF whose target set was enriched with significant genes. The histogram displays the p-values for this gene in individual studies. C) KANK1, a gene previously related with paternally inherited cerebral palsy, shows a consistent trend of up-regulation in the considered studies as shown with the fold-change view of the web browser.

                Novel candidates are associated with neurodegenerative disorders including Alzheimer's disease (VSNL1), prion disease (SCRG1, HSPH1, HSPA5 (Figure 4B) and CTR9) and age-related degeneration (GAS6 and GNG11). Moreover, candidates could explain evident DS features (Additional file 1, Table S5): (i) genes related to neurogenesis and neurite outgrowth (LPAR1 [45], LIN7C, JARID2, GREM1, SERPINE2, IGFR1 and SPOCK1) that could be related with mental retardation or cognitive impairment, (ii) genes involved in synapsis (AGT, KRAS, ATP1A2, GNAI2, SST and LIN7C) (iii) cytoskeletal related proteins (KANK1 [46]; Figure 4C), CKAP2, CKAP4, HAT1, NEK7 and VAMP3), (iv) macular degeneration genes [47] or genes (HTRA1 and EFEMP1) associated with age-related visual problems [48], (v) genes (AGT, CNN3, FBN1, RBPJ, PON2, POSTN, RAP1A, WNK1 and STK39) that were related with cardiac impairments and could be candidates to explain this DS characteristic [49], and (vi) genes related with cancer (BTBD3 [50], DNAJB4 [51], FIBP [52] and GSTZ1 [53]) [54].

                These examples show that the meta-analysis approach identified multiple additional genes that might be involved in DS pathology. In order to enable the community to check any particular gene of interest for DS relevance in the studies under analysis, we have agglomerated all information of the meta-analysis into a WEB-interface http://​ds-geneminer.​molgen.​mpg.​de/​. Examples of possible views and information are shown in Figure 4.

                Discussion

                The statistical meta-analysis approach was described previously by Rasche et al. [26]. The score computed with meta-analysis correlates with entropy (Figure 1A) indicating the ability to identify general dosage effects across many experiments that might be of more phenotypic relevance than very specific ones. Additional file 4, Figures S3A and B provide an overview of the different sources of data, including two organisms (human and mouse), different tissues (brain, heart and others), different stages of development (adult, postnatal, embryonic) and different mouse models (Ts65DN, Ts1Cje, Tc1). It is per se interesting that, in spite of such heterogeneity, common dosage effects could be identified at all and it should be highlighted that whole-genome data was fairly robust across experiments. Additional file 4, Figure S3D shows the overall correlation of the quantitative values of PCR and microarray values averaged from all experiments with only few genes in the non-concordant sectors of the graph (red points).

                The score used in this analysis allows detecting genes that could be either up- or down-regulated in different studies. An overview of the fold-changes for the genes across the different experiments is given in Additional file 1, Table S6. Because genes might change their expression level depending on the developmental state, tissue or because of other variables, we expected that this flexibility allows checking the hypothesis of random disturbances as well as the hypothesis of increased expression of HSA21 genes. We detected a clear enrichment of up-regulated genes on the q-terminal part of HSA21 (Figure 1D and Additional file 3, Figure S2). However, not a single region was identified but rather several smaller regions on HSA21 that agglomerate a large amount of significant dosage effects. This finding was also elaborated before (Korbel et al. [38] and Lyle R et al. [39]) using two independent data sets to characterize the molecular HSA21 regions in a set of DS-patients with partial duplications.

                We studied 255 HSA21 genes matched with the probe sets from the microarrays. Of these only 77 showed consistent dosage effects (Figure 1). While 165 HSA21 genes had score values different from zero indicating response in some of the microarray studies, 90 HSA21 genes were not responsive at all and provide evidence for a strong mechanism of dosage compensation. On the other hand, these figures could also reflect the limitation of detecting reliable fold-changes of low magnitude with microarray technology. Furthermore, experiments covered only a limited amount of tissues so that it is likely that some genes were missed simply because they were not responsive in the tissues under analysis. However, having brain as the dominant whole-genome sample source this should ensure expression of most of the genes. Microarray data was focused on the Affymetrix platform in order to reduce variance arising from platform inconsistencies. We have also compared our results with additional studies including own previous research [9] and others [55] and found relevance of selected dosage effects with respect to other tissues as well (data not shown). Additional cross-validation was performed with an independent microarray data set [10]. These authors compared human lymphoblastoid cell lines derived from DS patients and normal controls with a custom-made HSA21 array. Yahya-Graison et al. [10] divided the expression ratios in four classes: class I and class II genes were significantly up-regulated, while class III and class IV genes were either compensated or showed variable response. Our meta-analysis revealed a high-degree of concordance taking into account that the cell model, platform and the methodology used were completely different. The meta-analysis scores were significantly higher for class I and II genes than for class III and IV genes (P-value <0.01, Additional file 5, Figure S4). 25 out of 39 class I-II genes revealed a significant score in our meta-analysis (75%).

                In this study we monitored molecular interactions of HSA21 genes that might function as drivers of dosage effects (Figure 2A). For example, (i) TJP1 (Tight junction protein ZO-1) interacts with two HSA21 genes, JAM2 and CLDN8, (ii) FOS (FBJ murine osteosarcoma viral oncogene homolog) interacts with HSA21 genes ETS2, SUMO3, RUNX1 and indirectly with ERG, (iii) RHOQ (ras homolog gene family, member Q) interacts directly with ITSN1 and TIAM1 and indirectly with SYNJ, and (iv) PIK3R1 interacts directly with IFNAR1 and indirectly with IFNAR2. It should be emphasized that current information on molecular interactions is far from complete, thus we either might miss important interactions not yet detected and/or we might count false positive interactions due to the high error rates of current annotations of interactions.

                Several of the DS genes (N = 79) extrapolated to more general neurological phenotypes (Figure 3A). The dendrogram (Figure 3D) shows further interesting profiles of these genes in the DS samples under analysis: (i) differential gene expression in the cerebellum region versus whole "brain" or cerebrum areas which has been reported in other studies (e.g. Moldrich et al. [56]), (ii) different patterns of gene expression associated to particular developmental stages (P0, P15 and P30); these changes were reported before by Dauphinot el al. [57], and (iii) differences in ES studies.

                We further analyzed human and mouse studies separately and found 182 significant dosage effects using only human and 107 dosage effects using only mouse data. The Venn diagram in Additional file 4, Figure S3C clearly shows the benefit in detecting additional dosage effects when mixing the two species. Overlapping dosage effects were detected for 29 genes with both analyses (Additional file 1, Table S7). Results for the human and mouse specific analyses can be found in Additional file 1, Tables S8 and S9. It should be noted here that comparisons between human and mouse using microarrays are inherently difficult and have limitations since the probes for the orthologous mouse and human genes do not correspond well. Furthermore, gene expression variation is generally higher in human individuals compared to mouse inbred strains. Nonetheless, the 107 genes found in the analysis of mouse data (derived from the different mouse models for trisomy 21) represent a core set of genes responsive across different DS mouse models and, thus, could be highly relevant for DS pathogenesis.

                In addition to genes commonly related to DS, we have identified novel genes that can be associated with DS phenotypes, in particular with neural development and neurodegeneration. To our best knowledge, this study is the first meta-analysis of genome-wide transcript levels along with other data domains in DS research. The agglomerated data can be accessed through the WEB server at http://​ds-geneminer.​molgen.​mpg.​de and the identified dosage effects are a resource for further functional testing and therapeutic development.

                Conclusions

                We have identified a set of 324 genes with consistent dosage effects from 45 different experiments related to DS. Since the meta-analysis was enriched with brain experiments, we were able to detect a high fraction of genes related to neuro-development, synapsis and neuro-degeneration. Moreover, our results give more information about known and new pathways related to DS and also about 62 novel candidates. The results of the meta-analysis as well as the source data have been made accessible for the community through a WEB interface.

                Material and methods

                Selection and integration of DS resources

                Data sets were selected from heterogeneous technical platforms, different model systems (human cell lines, human tissues, mouse models) and different developmental stages (Additional file 1, Table S1). For each gene and for each source we computed a numerical value that measures its dosage effect. Data categories were either qualitative or quantitative. Qualitative data incorporated a total of 30 published manuscripts including reviews and semi-quantitative studies as well as two SAGE studies [21, 58] and were summarised to one score point in order to avoid over-scoring. Here, a "1" referred to the case that the gene was found as DS relevant in one (or more) studies. Quantitative data from differential gene expression studies such as Affymetrix microarrays, RT-PCR, MALDI and other quantitatives techniques were evaluated in order to extract comparable information across the different studies. We considered Affymetrix studies that provided the raw data (CEL file level). Raw data were extracted from Gene Omnibus Expression (GEO, [59]), Array Express [60] or were retrieved from the author's web pages (in total 16 data sets including human tissues and four different mouse models (Ts65Dn, Ts1Cje, Tc1 and Ts + HSA21). Furthermore, we incorporated 18 RT-PCR and MALDI data sets for which the authors displayed the information for all genes under study (either significant or not).

                Mapping of gene IDs

                A central pre-requisiste of any meta-analysis approach is the consolidation of the different ID types, for example coming from different organisms and from different versions of chips. We used the Ensembl database (version 56) as the backbone annotation for all studies. IDs were mapped to human Ensembl gene IDs. Mapping and merging of the information was done within R and the BioConductor package. In total, information on 19,388 ENSEMBL genes was mapped.

                Mapping SAGE IDs

                Differential expressed tags were extracted from additional files of the studies. Identifiers (based on sequences) were cross-tagged with the information displayed in the updating tables (SAGEmap_Hs and SAGEmap_Mn) from the SAGE site ftp://​ftp.​ncbi.​nlm.​nih.​gov/​pub/​sage/​mappings.

                Transcriptome data pre-processing and normalization

                We incorporated only case-control studies in the meta-analysis in order to derive expression fold-changes. Affymetrix gene chip annotations were adapted from the latest genome annotation (version 12). Affymetrix data were normalized with GC RMA. For transcriptome case-control studies three pieces of information were stored for each gene; (i) the fold-change (DS vs. controls), (ii) the standard error of the fold-change from the replicated experiments in that study and (iii) the expression p-value (presence-call) that indicates whether or not the gene is expressed in the target samples under study. For RT-PCR and MALDI experiments we computed the fold-change of the mean expression (DS vs. controls) as well as the reported standard error of the ratio. When mean and standard variation for each group (DS and controls) was provided we calculated the ratios as well as their associated standard errors.

                Scoring DS dosage effects across studies

                In order to score the different categories of information such as binary counts and quantitative gene expression values, we summarized the scores of the individual experiments for each category. For microarray studies, the score of the i-th gene in the j-th study, sij, was computed as described in Rasche et al. [26]:
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_3346_Equa_HTML.gif

                Here rij is the fold-change, pij is the average detection p-value and eij is the standard error of the ratio derived from the experimental replicates of the study. Thus, the fold-change is weighted with its reproducibility across the experimental replicates and with the likelihood of the gene being expressed in the study's case or control samples.

                For RT-PCR and MALDI studies we applied the following equation:
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_3346_Equb_HTML.gif

                Here rij is the fold-change and eij is the standard error of the ratio.

                The total score of the gene was computed as the sum across all individual study scores.

                Sampling for significance

                In order to assess the significance of the overall gene scores we generated random scores by re-sampling the scores 50,000 times with replacement within the same study. Using the random distribution as background we assigned as significant those genes that were above the 99.9 percentile of the background distribution.

                Judging consistency of dosage effects

                For each gene, entropy of the score distribution was computed in order to quantify the relevance of the gene across many experiments. Let sij be the score of the ith gene in the jth study, then Ei is a measure for the uniformity of the score distribution over the individual experiments:
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_3346_Equc_HTML.gif

                High entropy is assigned to a gene if many experiments contribute to the overall score whereas low entropy is assigned if only a few experiments contribute to the overall score.

                Enrichment analysis

                Gene Set Enrichment Analysis (GSEA, [41]) of the 324 genes was performed with respect to pre-defined human pathways agglomerated from 22 pathway resources from the ConsensusPathDB ([40], http://​cpdb.​molgen.​mpg.​de. Over-representation analysis of TF target sets was performed with Fisher's test based on annotation from TRANSFAC [42]. Motif enrichment analyses were performed using AMADEUS [43] with significant genes as target sets and all the genes considered in the meta-analysis as background set.

                Selection of independent brain experiments

                In order to proof general brain relevance of the 324 genes, we collected DS-independent gene expression studies to decipher brain features, performed with Affymetrix technology and, with experiments deposited in GEO or ArrayExpress (Additional file 1, Table S4). Mostly, these experiments were performed in mouse tissues. For each study we collected one or more resulting gene lists that were evaluated using Gene Set Enrichment Analysis (GSEA, [41]) against the complete list of 19,388 genes ranked by score.

                Abbreviations

                DS: 

                Down Syndrome

                HSA21: 

                human chromosome 21

                TF: 

                Transcription Factor

                PCR: 

                Polymerase Chain Reaction

                RT-PCR: 

                real-time Polymerase Chain Reaction

                MALDI: 

                Matrix-Assisted Laser Desorption/Ionization

                SAGE: 

                Serial Analysis of Gene Expression

                GO: 

                Gene Ontology

                ES: 

                Embryonic Stem Cells

                ID: 

                Identifier

                GEO: 

                Gene Omnibus Expression

                GSEA: 

                Gene Set Enrichment Analysis

                CNV: 

                Copy Number Variation

                TFBS: 

                Transcriptor Factor Binding Site

                Declarations

                Acknowledgements

                We want to express our gratitude to all researchers that made DS data available for the community. The free access to high quality experimental data is the necessary pre-requisite for all integrative studies. Furthermore, we apologize for all data sets that could not be integrated into the analysis because of specific constraints such as chip platforms, access to raw data etc. We thank Bernhard Herrmann for giving access to the in situ mouse brain images shown in Figure 3. We thank Marie-Laure Yaspo for discussions, James Adjaye for proof-reading of the manuscript and Reha Yildirimman and Atanas Kamburov for computational support. This work was funded by the European Commission within its 6th Framework Programme with the grant AnEUploidy (LSHG-CT-2006-037627), by the Max Planck Society and the Beatriu de Pinos postdoctoral fellowship (2008 BP-A 00184).

                Authors’ Affiliations

                (1)
                Department of Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics
                (2)
                Unitat de Genètica, Universitat Pompeu Fabra, y CIBER de Enfermedades Raras (CIBERER), Parc de Recerca Biomèdica de Barcelona
                (3)
                Programa de Medicina Molecular I Genetica, Hospital Vall d’Hebron

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