Diversification of stem cell molecular repertoire by alternative splicing

Abstract

Complete information regarding transcriptional and posttranscriptional gene regulation in stem cells is necessary to understand the regulation of self-renewal and differentiation. Alternative splicing is a prevalent mode of posttranscriptional regulation, and occurs in approximately one half of all mammalian genes. The frequency and functional impact of alternative splicing in stem cells are yet to be determined. In this study we combine computational and experimental methods to identify splice variants in embryonic and hematopoietic stem cells on a genome-wide scale. Using EST collections derived from stem cells, we detect alternative splicing in >1,000 genes. Systematic RT-PCR and sequencing studies show confirmation of computational predictions at a level of 80%. We find that alternative splicing can modify multiple components of signaling pathways important for stem cell function. We also analyze the distribution of splice variants across different classes of genes. We find that tissue-specific genes have a higher tendency to undergo alternative splicing than ubiquitously expressed genes. Furthermore, the patterns of alternative splicing are only weakly conserved between orthologous genes in human and mouse. Our studies reveal extensive modification of the stem cell molecular repertoire by alternative splicing and provide insights into its overall role as a mechanism of generating genomic diversity.

Fig. 1.

Computational and experimental approach. A reference set of full-length gene transcripts derived from Ensembl was aligned with ESTs from the NCBI and Stem Cell Databases. Potential sites of alternative splicing were identified as gaps in the alignments that correspond to the exon boundaries. For these studies, we focused only on inclusion or exclusion of entire exons within coding sequences. Computationally detected alternative splicing events were confirmed experimentally with RT-PCR, cloning, and sequencing. PECAM, platelet/endothelial cell adhesion molecule.

Fig. 2.

Experimental confirmation of computationally identified splice variants in stem cells. A representative set of 15 murine transcription factors and transmembrane receptors was selected for experimental confirmation; 80% (12 of 15) were confirmed. Prior knowledge of alternative splicing was not used in the selection of the gene set. RT-PCR profiles are shown for seven of the confirmed genes. The structure of the splice variants was confirmed by cloning and sequencing as described in Materials and Methods. Additional PCR profiles and sequences are provided in Fig. 7.

Fig. 3.

Defining the exon exclusion fraction. To quantitatively characterize frequency of alternative splicing, the exon exclusion fraction is defined as the number of ESTs with an excluded exon divided by the number of ESTs spanning a site of alternative splicing, f_ex = N_ex/(N_ex + N_in). This fraction is also determined experimentally as a ratio of DNA band intensities, f_ex = L[I_ex/(I_ex + I_in)]. Note the correlation between the computational and experimental values.

Fig. 4.

Correlation between transcription and frequency of alternative splicing. (A) For each alternative splice site, the exon exclusion fraction f_ex is plotted versus a number of ESTs covering this site (N_ex + N_in). The points corresponding to experimentally tested genes are shown with arrows. Note that experimental data confirm the computational results. (B and C) Representative examples of the ubiquitously expressed (B) and tissue-specific (C) genes. Note that genes with high numbers of ESTs (ubiquitously expressed genes overrepresented in EST libraries) show a low level of exon exclusion and that genes with low numbers of ESTs (tissue-specific genes) show more equal frequencies of exon exclusion and inclusion. Additional PCR profiles and sequences are provided in Figs. 8 and 9.

Fig. 5.

Low frequency of exon exclusion. (A) Distribution of alternative splice sites as a function of the exon exclusion fraction f_ex (solid red line). For comparison, a theoretical random distribution profile is shown with the dashed black line. (B) RT-PCR profiles of alternative splicing in representative genes. The gene names follow SwissProt nomenclature, and the exon exclusion values are indicated in parentheses. Here and in Figs. 8-10, note that for most genes, exon inclusion (upper bands) is prevalent in comparison to exon exclusion (lower bands). (C) Correspondence between experimental and computational data. The exon exclusion fractions f_ex calculated with the EST data N_ex/(N_ex + N_in) and determined experimentally with RT-PCR L[I_ex/(I_ex + I_in)] are plotted versus each other. In addition, a linear trend line is added (m = 0.92, r² = 0.72).

Fig. 6.

Comparison of alternative splicing in human and mouse orthologues. Corresponding regions of the mouse and human orthologues were identified using the Ensembl database. All splicing patterns were confirmed by sequencing except those from mouse genes with no detectable alternative splicing. Note the lack of alternative splicing conservation in most of the analyzed genes. Additional PCR profiles and sequences are provided in Fig. 10.