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 (9,6). 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 (8,7). 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 makes 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 (1).  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 (5).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 (3).

Our review dealing with the definition of splicing regulatory motifs can be found in reference 4.

Our present efforts include:

A) 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) Defining the effect of sequence context on the action of splicing regulatory elements, using deep sequencing technology.  

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

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

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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.