Splicing Code Research Articles

Towards an RNA Splicing Code

Towards an RNA Splicing Code

Functional coordination of alternative splicing in the mammalian central nervous system

A microarray analysis provides new evidence suggesting that specific cellular processes in the mammalian CNS are coordinated at the level of alternative splicing, and that a complex splicing code underlies CNS-specific alternative splicing regulation.

Alternative RNA splicing regulation in the testis

Alternative splicing regulation has been shown to be critically important for several developmental pathways. It is particularly prevalent in the testis, which is the site of an extensive adult developmental programme. Alternative splicing is controlled by a splicing code, in which transcripts respond to subtle cell type-specific variations in positive and negative trans-acting RNA-binding proteins according to their unique set of binding sites for these proteins. Because of their unique combinations of cis-acting sequence elements, specific transcripts are able to respond individually to this code. In this review, we discuss how this code may be deciphered in germ cells to mediate a splicing response.

Splicing Regulation in Neurologic Disease

The importance of alternative splicing in the regulation of diverse biological processes is reflected in the growing list of human diseases associated with known or suspected splicing defects. It is becoming evident that alternative splicing plays a particularly important role in neurologic disease, which is perhaps not surprising given the important role splicing plays in generating complexity and function in the brain. This review considers the evidence that defects in regulation of splicing may underlie many types of human neurologic diseases.

Computational searches for splicing signals

The removal of introns from pre-mRNA requires as an initial event the accurate molecular recognition of the proper exon–intron borders. It is now evident that RNA sequence elements in addition to the consensus splice site sequences themselves are required for this recognition. Genomic analyses have contributed to the definition of these elements as exonic and intronic splicing enhancers and silencers, comprising what has been called the “splicing code.” Many computational methods have been brought to bear in such studies. We describe here some of the methods we have used to discover functional splicing signals. What these methods have in common is a comparison of sequences in and around exons to sequences found elsewhere in the genome. We have especially made use of comparisons to “pseudo exons,” intronic sequences resembling exons by virtue of being bounded by sequences indistinguishable from splice sites. Two computational strategies are emphasized: (1) the use of a machine learning technique in which a computational algorithm, a support vector machine, is first trained on known examples and then used to predict sequences associated with splicing; and (2) straight statistical analysis of differences between regions associated with exons and other regions in the genome. In most cases, the predictions made using these methods have been validated by subsequent empirical tests. An attempt has been made to make this description understandable by researchers unfamiliar with computational practice and to include practical references to specific databases and programs.

A Splice Code for trans-Synaptic Cell Adhesion Mediated by Binding of Neuroligin 1 to α- and β-Neurexins

Previous studies suggested that postsynaptic neuroligins form a trans-synaptic complex with presynaptic beta-neurexins, but not with presynaptic alpha-neurexins. Unexpectedly, we now find that neuroligins also bind alpha-neurexins and that alpha- and beta-neurexin binding by neuroligin 1 is regulated by alternative splicing of neuroligin 1 (at splice site B) and of neurexins (at splice site 4). In neuroligin 1, splice site B is a master switch that determines alpha-neurexin binding but leaves beta-neurexin binding largely unaffected, whereas alternative splicing of neurexins modulates neuroligin binding. Moreover, neuroligin 1 splice variants with distinct neurexin binding properties differentially regulate synaptogenesis: neuroligin 1 that binds only beta-neurexins potently stimulates synapse formation, whereas neuroligin 1 that binds to both alpha- and beta-neurexins more effectively promotes synapse expansion. These findings suggest that neuroligin binding to alpha- and beta-neurexins mediates trans-synaptic cell adhesion but has distinct effects on synapse formation, indicating that expression of different neuroligin and neurexin isoforms specifies a trans-synaptic signaling code.

Organic codes: Metaphors or realities?

Coding characteristics have been discovered not only in protein synthesis, but also in various other natural processes, thus showing that the genetic code is not an isolated case in the organic world. Other examples are the sequence codes, the adhesion code, the signal transduction codes, the splicing codes, the sugar code, the histone code, and probably more. These discoveries however have not had a significant impact because of the widespread belief that organic codes are not real but metaphorical entities. They are supposed to lack arbitrariness and codemakers, the two qualifying features of real codes. Here it is shown that the arbitrariness issue can be solved on an experimental basis, while the codemaker issue is dependent on our theoretical description of the cell and can only be solved by a new concept. In order to appreciate the reality of the organic codes, in short, it is necessary to have not only a more critical evaluation of the experimental data but also a new theory of the living system.

Interfering contexts of regulatory sequence elements.

Although one would normally expect a given regulatory element to perform best when it fully matches its consensus sequence, this is generally far from being the case. Usually, almost none of the actual sites fits the consensus exactly, and some of those that do fit do not perform well. The main reason for that is the very nature of the sequences and the messages (codes) they contain. Normally, any given stretch of the sequence with one or another regulatory site not only carries this regulatory message, but several more messages of various types as well. These messages overlap with the regulatory element in such a way that the letter (base) which actually appears in any given sequence position simultaneously belongs to one or more additional codes. Apart from numerous individual codes (sequence patterns) specific for a given species or gene, there are many different general (universal) sequence codes all interacting with one another. These are the classical triplet code, DNA shape code, chromatin code, gene splicing code, modulation code and many more, including those that have not yet been discovered. Examples of overlapping of different codes and their interaction are discussed, as well as the role of degeneracy of the codes and the sequence complexity as a function of code density.

Code domains in tandem repetitive DNA sequence structures.

Traditionally, many people doing research in molecular biology attribute coding properties to a given DNA sequence if this sequence contains an open reading frame for translation into a sequence of amino acids. This protein coding capability of DNA was detected about 30 years ago. The underlying genetic code is highly conserved and present in every biological species studied so far. Today, it is obvious that DNA has a much larger coding potential for other important tasks. Apart from coding for specific RNA molecules such as rRNA, snRNA and tRNA molecules, specific structural and sequence patterns of the DNA chain itself express distinct codes for the regulation and expression of its genetic activity. A chromatin code has been defined for phasing of the histone-octamer protein complex in the nucleosome. A translation frame code has been shown to exist that determines correct triplet counting at the ribosome during protein synthesis. A loop code seems to organize the single stranded interaction of the nascent RNA chain with proteins during the splicing process, and a splicing code phases successive 5' and 3' splicing sites. Most of these DNA codes are not exclusively based on the primary DNA sequence itself, but also seem to include specific features of the corresponding higher order structures. Based on the view that these various DNA codes are genetically instructive for specific molecular interactions or processes, important in the nucleus during interphase and during cell division, the coding capability of tandem repetitive DNA sequences has recently been reconsidered.

Epstein-Barr virus bicistronic mRNAs generated by facultative splicing code for two transcriptional trans-activators.

The Epstein-Barr virus (EBV) genome codes for several transcriptional trans-activators. One of them, the BZLF1 open reading frame (ORF)-encoded product EB1, is able to induce the productive cycle in infected B cells. From the cloning and characterization of full-length cDNAs, we found that EB1 could be made from three overlapping messenger RNAs expressed under the control of two different promoters that we call P1 and P2. The first mRNA, 1 kb long, is made from the P1 promoter and codes for EB1 alone. The two other mRNAs, respectively 3 and 4 kb long and made by facultative splicing, are bicistronic mRNAs. They code not only for the trans-activator EB1 but also for a second EBV transcriptional trans-activator R, encoded by the BRLF1 ORF. In effect, authentic EB1 and R proteins are expressed from the 3 and 4 kb long cDNAs as demonstrated by identification of the proteins with specific antisera. In addition, EB1 and R expressed from the 3 and 4 kb cDNAs activate transcription from their specific targets in the EBV early promoter DR.