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. 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.

Over the last several years 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 (1,2). 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 (3, 4). You can visit our online PESX utility to find these 8-mers in your own sequence and see reference 5 for our computational approaches.

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 (6).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.

Our review dealing with the definition of splicing regulatory motifs can be found in reference 7.

Our present efforts include:

A) Continuing algorithm development to predict splice sites based on information available to the cell, including secondary structure prediction.

B) Genetic definition of flank signals that function as ISEs, including parameters that define a branch point.

C) Learning the rules of ESE, ESS, ISE and ISS interaction through the de novo design of synthetic exons.

D) Testing the hypothesis that ESSs play a general role in the repression of false splice sites.

E) Genetic experiments to provide complete definitions of 8-mer sequences that can serve as signaling elements, using massively parallel DNA sequencing.

F) Defining the splicing factor interactions necessary for exon definition.

G) Computational anlaysis of the effect of secondary structure on splice site selection.

References

(1) Zhang, X. H., K. A. Heller, I. Hefter, C. S. Leslie, and L. A. Chasin. 2003. Sequence information for the splicing of human pre-mRNA identified by support vector machine classification. Genome Res 13:2637-50.

(2) Xiang H-F. Zhang, Christina S. Leslie, and Lawrence A. Chasin. 2005. Dichotomous Splicing Signals in Exon Flanks.Genome Res. 15: 768-779.

^{(3) Zhang, X. H., and L. A. Chasin. 2004. Computational definition of sequence motifs governing constitutive exon splicing. Genes Dev 18:1241-50.}

^{(4) Exon inclusion is dependent on predictable exonic splicing enhancers. 2005. Xiang H-F. Zhang, Thaned Kangsamaksin, Mann S. P. Chao, Joydeep K. Banerjee, and Lawrence A. Chasin. Mol. Cell. Biol. 25: 7323-7332.}

^{(5) Xiang H-F. Zhang, Christina S. Leslie, and Lawrence A. Chasin. 2005. Computational searches for splicing signals. Methods 37: 292-305}

^{(7) L. A. Chasin. 2007. Searching for Splicing Motifs, In press, in "Alternative Splicing," B. Blencowe and B. Graveley, eds., Eureka Press. http://eurekah.com/chapter/3361}

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^{A second project in the area of biotechnology: We are isolating engineered derivatives of Chinese hamster ovary (CHO) cells that are capable of rapid gene amplification to speed up the development of recombinant protein based therapeutics, and developing vectors that increase recombinant protein production through more efficient posttranscriptional processing of recombinant transcripts.}

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