Discovering candidate intron motifs regulating tissue-specific alternative splicing patterns in exons as identified by exon junction microarrays

Alternative splicing is an important mechanism by which eukaryotic species create abundant proteomic variation from a given set of protein coding genes. More than 74% of human multi-exon genes are estimated to be subject to alternative splicing, in most cases in characteristic tissue-specific patterns (Johnson et al., 2003). While the cellular machinery that splices introns out of pre-mRNA has been thoroughly studied, the regulatory mechanisms that govern its action in specific tissues are not yet well understood. To apply these methods for the identification of intronic splicing regulatory motifs by identifying sets of “co-regulated" exons, i.e. those with similar splicing patterns in different tissues. We hypothesize that different alternatively spliced transcripts exhibiting correlated expression levels in specific exons may share similar regulatory mechanisms that control their pattern of alternative splicing. These regulatory mechanisms recognize their exons through sites located in adjacent, upstream introns. From analysis of the Johnson et al. (2003) dataset of tissue-specific alternative splicing events, we clustered these exons from over 10,000 genes in 54 different tissues to organize them into clusters of similar expression patterns using complete link clustering. By applying Gibbs sampling techniques by using in the form of BioProspector (Liu et al., 2001), we identified statistically significant motif candidates within the intron sequences preceding the excised exon pattern. These intronic motifs exhibit strand bias by occurring with a higher frequency on the forward strand of the DNA that encodes the mRNA transcript. Introduction Alternative RNA splicing is a mechanism that adds diversity to the proteome for a given set of genes. Genes at the pre-RNA level contain protein coding and non-protein coding regions called exons and introns, respectively. In order to generate a functional mRNA transcript, constitutive RNA splicing must occur to remove the interspersed introns nestled between the exons. However, it is possible to also remove targeted exon sequences through methods of alternative splicing, which may occur in one of four ways: deletion of an optional exon, inclusion of an optional intron, sorting of mutually exclusive exons, and splicing at interior intronic splice sites. Based on the total number of exons in the pre-RNA transcript, the possible combinatorial patterns expand the diversity of the proteins isoforms to be translated from the posttranscriptional processing of the pre-RNA (Figure 1). a. b. Figure 1. (a) Constitutive splicing splices out introns from the pre-RNA while (b) alternative splicing also removes targeted exons. Images courtesy of Alberts et al. (2002). Alternative splicing is time dependent, as seen by its selective action during organismal development. For example the sex of the fruit fly is determined by the alternative splicing pattern of the tra gene, which encodes the Tra regulatory protein. Tra is involved in alternatively splicing the doublesex gene, and depending on the pattern of alternative splicing the fruit fly may be male or femaile. Alternative splicing is also a tissue specific process to facilitate cell differentiation (Alberts et al., 2002). The alpha-tropomyosin gene in rats regulates muscle contraction, and we see that the gene is alternatively spliced into various RNA transcripts (Figure 2) to give protein isoforms that are different across the different tissues types such as striated Figure 2. The alpha-tropomyosin gene in the rat regulates muscle contraction and undergoes different patterns of alternative splicing in a tissue-specific manner. Images courtesy of Alberts et al. (2002). versus smooth muscle tissue (Alberts et al, 2002). It is estimated that almost three-quarters of human genes are involved in alternative splicing (Johnson et al., 2003). Within the human genome, splicing motifs at the pre-RNA level are used to recruit the spliceosome machinery to bind and perform the two transesterification reactions required to excise the intron sequence in the form of a lariat to be targeted for degradation in the nucleus. Three types of sequences are required for the recognition of the targeted intron by the spliceosome machinery: the 5’ splice site (AG|GURAGU), branch site (GTRAYY), and polypyrimidine tract (YYYYYYYYNCAG|G) (Figure 3) (the vertical line indicates the intronFigure 3. The motifs involved in constitutive splicing include the 5’ and 3’ splice sites. exon junction). Together, the branch site and the polypyrimidine tract are known as the 3’ splice site. These consensus motifs are highly variable, which makes their discovery difficult on the computational level, but there a few consistent characteristics. Exon1 usually ends in AG, the branch point is an A (which is where the lariat forms), and Exon2 begins with a G to give the final spliced product an intersection of AGG. Based on the existence of motifs for constitutive splicing, we are interested in looking for motifs that are specific to alternative splicing that generates similar splicing patterns across different human tissues. We want to analyze intron sequences from genes with similar alternative splicing patterns across different human tissues based on the RNA exon junction microarray data available from previous studies by Johnson et al. (2003). The justification for looking for intronic motifs that may govern patterns of alternative splicing is based on the existence of positive and negative regulatory factors that can bind to intronic sequences that help to stabilize or repel the spliceosome from a nearby exon. Alternative splicing regulatory motifs may be analogous to promoter motifs that influence transcription initiation by recruiting transcription factors. Transcriptional regulatory motifs have traditionally been identified by applying methods such as Gibbs sampling to the promoters of coregulated genes, and we look to perform a similar analysis on intron sequences to look for alternative splicing regulatory motifs. Here we operate under the assumption that the motif lies only within the intron sequences that precedes the first missing exon in a given splicing pattern, so we will only examine one intron sequence per gene. A database of known transcript isoforms, such as the Alternative Splicing Database (http://www.ebi.ac.uk/asd/altsplice/index.html), is not sufficient for this purpose of intronic motif discovery, since detailed information about the tissue-specificity of each alternative transcript is required. Exon junction microarrays for identification of tissue-specific alternative splicing events 5’ Splice Site 3’ Splice Site Branch Site (18-38 nt upstream) Polypyrimidine tract Recently, gene expression microarray technology has been extended to probe the presence or absence of individual exons in whole transcripts. For example, exon junction arrays, described in Johnson et al., place a probe targeting the junction between each pair of adjacent exons in a transcript. Adjacent exon junction probes indicate that the flanked exon is absent in the sample when their intensity is low in relation to other probes targeting the same processed RNA transcript (Figure 4). Figure 4. Conceptual schematic of an exon junction microarray experiment. The low intensity of probes targeting the junction of exons 2 and 3 and exons 3 and 4, in relation to the other probes targeting this transcript, indicate that exon 3 is removed through splicing. From exon junction arrays targeting some 100,000 exon junctions in more than 10,000 human genes, the Johnson et al. (2003) study identified alternative splicing events in mRNA samples from 54 different human tissues. Thus, a consistent, near-genome-wide database of tissuespecific alternative splicing events is now available. Methods Our strategy to systematically identify candidate intronic alternative splicing regulatory motifs is as follows. First, we will process the Johnson et al. (2003) microarray dataset to produce an expression vector across different tissues for each exon measured. We will then identify sets of “coregulated” exons by performing hierarchical clustering on these expression vectors. For each set of coregulated exons, we will extract the intronic sequence upstream of the first exon in the set and use existing motif finding tools such as BioProspector to identify candidate motifs. Finally, we will assess the significance of the motifs we identify through several different statisticallyand biologicallymotivated tests. Microarray data processing The full details of the microarray data processing strategy are reported in Johnson et al (2003). Briefly, linear models are fit to each probe based on its intensity across all tissue samples and of all probes targeting the same transcript, to predict the probe intensity in each tissue, under the assumption that the targeted exons are included in the transcript in that tissue. Each exon junction probe in each tissue is then assigned an integer value between 0 and 3 indicating how far below the predicted intensity the actual observed intensity was; for example, a value of 0 means that the observed intensity was mostly explained by the model, suggesting that both exons targeted by the probe are present in the sample. A value of 3 means that the observed intensity is far below expectation, suggesting that one or both exons targeted by the probe are not present. There may also be missing values; for example, if an entire transcript did not seem to be expressed in a certain tissue, then the probes targeting it will be assigned missing values. Generation of exon expression vectors We chose to represent the alternative splicing pattern of each exon as a binary vector with an entry for each of the 54 tissues containing 1 if the exon is expressed in that tissue, 0 if it is spliced out, or the missing value (“NA”) if there is not sufficient evidence to decide. For each exon targeted by the microa