Integrated Quantitative Transcriptome Maps of Human Trisomy 21 Tissues and Cells

Abstract

Down syndrome (DS) is due to the presence of an extra full or partial chromosome 21 (Hsa21). The identification of genes contributing to DS pathogenesis could be the key to any rational therapy of the associated intellectual disability. We aim at generating quantitative transcriptome maps in DS integrating all gene expression profile datasets available for any cell type or tissue, to obtain a complete model of the transcriptome in terms of both expression values for each gene and segmental trend of gene expression along each chromosome. We used the TRAM (Transcriptome Mapper) software for this meta-analysis, comparing transcript expression levels and profiles between DS and normal brain, lymphoblastoid cell lines, blood cells, fibroblasts, thymus and induced pluripotent stem cells, respectively. TRAM combined, normalized, and integrated datasets from different sources and across diverse experimental platforms. The main output was a linear expression value that may be used as a reference for each of up to 37,181 mapped transcripts analyzed, related to both known genes and expression sequence tag (EST) clusters. An independent example in vitro validation of fibroblast transcriptome map data was performed through "Real-Time" reverse transcription polymerase chain reaction showing an excellent correlation coefficient (r = 0.93, p < 0.0001) with data obtained in silico. The availability of linear expression values for each gene allowed the testing of the gene dosage hypothesis of the expected 3:2 DS/normal ratio for Hsa21 as well as other human genes in DS, in addition to listing genes differentially expressed with statistical significance. Although a fraction of Hsa21 genes escapes dosage effects, Hsa21 genes are selectively over-expressed in DS samples compared to genes from other chromosomes, reflecting a decisive role in the pathogenesis of the syndrome. Finally, the analysis of chromosomal segments reveals a high prevalence of Hsa21 over-expressed segments over the other genomic regions, suggesting, in particular, a specific region on Hsa21 that appears to be frequently over-expressed (21q22). Our complete datasets are released as a new framework to investigate transcription in DS for individual genes as well as chromosomal segments in different cell types and tissues.

Keywords: Down syndrome; human chromosome 21; integrated transcriptome map; meta-analysis; trisomy 21.

FIGURE 1

Graphic representation of the TRAM software workflow. The software allows the import and analysis of gene expression profile datasets in tab-delimited text format. Gene expression values are assigned to individual loci following conversion of all types of gene identifiers (IDs) into official gene symbols, and submitted to an intra- and inter-sample normalization. The value for each locus is the mean value of all available normalized values for that locus. The expression ratio obtained from the comparison of two different conditions is graphically displayed for each chromosomal segment, expressed as ratio of the mean of the expression values of the loci included in that segment. Over- and under-expressed regions are then determined following statistical analysis.

FIGURE 2

DS/normal expression ratio for each chromosome in terms of mean expression level derived from all the genes on that chromosome in each transcriptome map.

FIGURE 3

Correlation between observed end expected gene expression ratios. The graph shows the correlation between the gene expression ratios observed (Y-axis) and expected (X-axis) calculated by bivariate analysis by JMP 5.1 program. Assuming that the variables have a bivariate normal distribution, the ellipse (Bivariate Normal Ellipse) contains about 95% of the points. The narrowness of the ellipse shows the correlation of the variables. If the ellipse is narrow and diagonally oriented, the variables are related. Pearson correlation coefficient = 0.93 and p-value < 0.0001.