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Lawrence Chasin
William R. Kenan Jr. Professor
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.

Our present efforts include:

A) Computational Biology
Algorithm development to predict splice sites based on information available to the cell, including secondary structure prediction, so as to identify all the elements that need to be considered for a mechanistic understanding of splicing. .

B) Designer Exons
Learning the rules of ESE, ESS, ISE and ISS interaction through the de novo design of synthetic exons.  These exons are made up of prescribed ESEs and ESSs that are designed to function singularly when combined (i.e., not give rise to confounding overlapping sequences)  

C) High throughput mutational analysis
We have recently developed this tool for carrying out saturation screens of sequences and sequence changes that affect splicing. We are using this methodology to defined combinatorial interactions governing exon definition and defining the effect of context in splicing decisions. 

D) The effect of DNA-level action on splicing
We are genetically manipulating the 25 kb dihydrofolate reductase (dhfr) locus in Chinese hamster cells to study the effect of chromatin structure and promoter/enhancer action on splicing in the natural chromosomal context of a gene.

E) A second area of interest lies 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.  




PESE score profile of human chuk exon 8 (black curve) and the effect of mutations on PESE score (red curves or blue curves) and splicing (rectangles).
Representative Recent Publications
  • 1. Shengdong Ke, Shulian Shang, Sergey M. Kalachikov, Irina Morozova3 Lin Yu, James J. Russo, Jingyue Ju, and Lawrence A. Chasin. (2011) Quantitative evaluation of all hexamers as exonic splicing elements. Genome Res 2011 21: 1360-74.
  • S. Ke and L.A. Chasin. (2011) Context-dependent splicing regulation: exon definition, co-occurring motif pairs and tissue specificity. RNA Biol. 8: 384-388.
  • J. Cacciatore, E. Leonard, and L. A. Chasin. (2010) Current Methods for achieving efficient high-level therapeutic protein productivity using Chinese hamster ovary (CHO) cell expression systems. Biotechnol Adv. 28: 673-81.
  • M.A. Arias, S. Ke, and L.A. Chasin. (2010) Splicing by cell type. Nat Biotechnol. 28: 686- 7.
  • S. Ke and L.A. Chasin. (2010) Intronic motif pairs cooperate across exons to promote pre-mRNA splicing. Genome Biol. 11(8): R84.
  • Zhang, X. H.-F., M. A. Arias, S. Ke, and L. A. Chasin. (2009) Splicing of designer exons reveals unexpected complexity in pre-mRNA splicing. RNA 15: 367-376.
  • Yu, Y., P. A. Maroney, J. A. Denker, X. H. Zhang, O. Dybkov, R. Luhrmann, E. Jankowsky, L. A. Chasin, and T. W. Nilsen (2008) Dynamic regulation of alternative splicing by silencers that modulate 5' splice site competition. Cell 135: 1224-36.
  • Ke, S.,X. H-F. Zhang, and L. A. Chasin 2008 (2008) Positive selection acting on splicing motifs reflects compensatory evolution. Genome Research 18: 533-43.
  • Chasin, L. A. (2007) Searching for Splicing Motifs, in “Alternative Splicing in the Postgenomic Era,” B. Blencowe and B. Graveley, eds., pp.. Landes Bioscience, Austin, TX 78701 85-106.
  • Zhang, X. H-F. and L. A. Chasin. (2006) Comparison of multiple vertebrate genomes reveals the birth and evolution of human exons. Proc Natl. Acad. Sci. USA 103: 13427-13432.
  • Zhang, X. H-F., Kangsamaksin, T. , Chao, M.S.P., Banerjee, J.K., and Chasin, L.A. (2005) Exon inclusion is dependent upon predictable exonic splcing enhancers Mol. Cell. Biol. 25: 7323-7332.
  • Xiang H-F. Zhang and Lawrence A. Chasin (2004) Computational definition of sequence motifs governing the splicing of constitutive exons Genes Dev 18: 1241-50.
  • X. H-F. Zhang, K. Heller, I. Helfer, C. Leslie, and L.A. Chasin (2003) Sequences Information for the splicing of human pre-mRNA identified by support vector machine classification Genome Research 13: 2637-2650.
  • Zhang X, Lee J, Chasin LA. (2003) The effect of nonsense codons on splicing: A genomic analysis RNA 9(6): 637-639; update: Latent splice sites and stop codons revisited. RNA 10: 5-6 RNA (2004).
  • Fairbrother, W. and Chasin, L.A. (2000) Human genomic sequences that inhibit splicing Mol. Cell. Biol. 20: 6816-6825. Article
  • Sun, H. and Chasin, L.A. (2000) Defective splice signals in an intronic false exon Mol. Cell. Biol. 20: 6414-6425. Article
  • Bai, Y. , D. Lee, T. Tu, and L.A. Chasin (1999) Control of 3' splice site selection in vivo by ASF/SF2 and hnRNP A1 Nucleic Acids Res. 27: 1126-1134. Article
  • Chen, Chao and Lawrence A. Chasin (1999) Cointegration of DNA molecules introduced into mammalian cells by electroporation Somat. Cell Molec. Genet. 24: 249-256. Article
  • Noe V, Alemany C, Chasin LA, Ciudad CJ. (1998) Retinoblastoma protein associates with SP1 and activates the hamster dihydrofolate reductase promoter Oncogene 16(15): 1931-8. Article
  • Noe, V., C. Chen, C. Alemany, M. Nicolas, I. Caragol, L.A. Chasin, and C. J. Ciudad (1997) Cell growth regulation of the hamster dihydrofolate reductase gene promoter by transcription factor Sp1 Eur. J. Biochem. 249: 13-20. Article
  • Kessler, O. & Chasin, L.A. (1996) The Effect of Nonsense Mutations on Nuclear and Cytoplasmic Adenine Phosphoribosyltransferase RNA Mol Cell Biol 16: 4426-4435. Article
Lawrence Chasin
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