简介

The principal question being pursued in our laboratory is how the cellular splicing machinery recognizes the exons it must join during the maturation of mRNA from long primary transcripts. The 3 sequence motifs that are almost always associated with exons -- the branch site, the upstream acceptor splice site, and the downstream donor splice site -- provide insufficient information for molecular recognition. “Pseudo” exons bordered by these elements outnumber the real exons by at least an order of magnitude yet are ignored. We are trying to provide a global definition of the additional informational elements that play roles in defining exons for constitutive and alternative splicing and to uncover their mode of action. I many ways this informational problem is analogous to that of where and when transcription start sites are recognized. We have used computational methods to ferret out some of this information. Using machine learning techniques we have found that 50 nt intronic stretches on either side of exons, beyond the splices site consensus sequences themselves, contain information that is necessary for the efficient splicing of most human exons (14, 12). The information at this stage is in the form of 5‑mers that are overrepresented in these regions. We would now like to know the exact nature of these signaling elements (intronic splicing enhancers, ISEs), the step(s) in splicing at which they act, and the proteins that mediate their effects. Additional information lies within the exon bodies in the form of exonic splicing enhancers (ESEs) and exonic splicing silencers (ESSs). Using genomic statistical analysis, we compiled lists of 8-mers as putative ESEs (PESEs) and putative ESSs (PESSs) in each class and showed that the most of the predicted motifs can function as expected (13, 11, 8). You can visit our online PESX utility to find these 8-mers in your own sequence and see reference 5 for our computational approaches. This work along with similar successes of other laboratories now make it clear that exons and their flanks are filled with a dense population of regulatory elements. Our task now lies in figuring out how this rich sequence information is integrated to make what is usually a binary decision to splice. But the high density makes it difficult to make solitary genetic changes, and makes interpretations ambiguous. To circumvent this problem we have turned to synthetic exons that we design to contain isolated enhancer, silencer and neutral modules (6). We hope that the rules governing splicing will be more apparent by the pointed manipulation of these “designer exons.” Comparative genomics is another tool we are using to decipher splicing information and to view the evolutionary pressures exerted upon these sequences. In the course of these experiments we discovered that the most recently created mammalian exons stem largely from repeated sequences and are spliced inefficiently and are often non-protein coding (10).We also find that new ESEs are constantly being created and ESSs destroyed as the genomes strives to maintain splicing efficiency in the face of continual mutation (8). High throughput (deep) sequencing has provided means for high throughput mutational analysis. We have developed a method (quantifying extensive phenotypic arrays from sequence arrays, or “QUEPASA”) for the exhaustive testing of all possible k-mers for positive and negative splicing influences. The results for exonic 6-mers point to significant context effects in many cases. Our 2005 review dealing with the definition of splicing regulatory motifs can be found in reference 9.