RNA landscape of evolution for optimal exon and intron discrimination

Abstract

Accurate pre-mRNA splicing requires primary splicing signals, including the splice sites, a polypyrimidine tract, and a branch site, other splicing-regulatory elements (SREs). The SREs include exonic splicing enhancers (ESEs), exonic splicing silencers (ESSs), intronic splicing enhancers (ISEs), and intronic splicing silencers (ISSs), which are typically located near the splice sites. However, it is unclear to what extent splicing-driven selective pressure constrains exonic and intronic sequences, especially those distant from the splice sites. Here, we studied the distribution of SREs in human genes in terms of DNA strand-asymmetry patterns. Under a neutral evolution model, each mononucleotide or oligonucleotide should have a symmetric (Chargaff's second parity rule), or weakly asymmetric yet uniform, distribution throughout a pre-mRNA transcript. However, we found that large sets of unbiased, experimentally determined SREs show a distinct strand-asymmetry pattern that is inconsistent with the neutral evolution model, and reflects their functional roles in splicing. ESEs are selected in exons and depleted in introns and vice versa for ESSs. Surprisingly, this trend extends into deep intronic sequences, accounting for one third of the genome. Selection is detectable even at the mononucleotide level, so that the asymmetric base compositions of exons and introns are predictive of ESEs and ESSs. We developed a method that effectively predicts SREs based on strand asymmetry, expanding the current catalog of SREs. Our results suggest that human genes have been optimized for exon and intron discrimination through an RNA landscape shaped during evolution.DNA strand asymmetry ͉ exon and intron recognition ͉ exon identity elements ͉ intron identity elements ͉ splicing-regulatory elements M ost mammalian genes are split, with exons (Ϸ150 nt) separated by much longer introns (Ϸ3,000 nt). To produce a mature transcript from a prem-RNA, introns are spliced out, and exons are ligated by a large protein/snRNA complex, the spliceosome. Extensive efforts have been made to elucidate the splicing code, i.e., the combinations of cis-regulatory elements and trans-acting factors responsible for splicing efficiency and fidelity. Besides the degenerate splice-site motifs, which are necessary but not sufficient for specific exon and intron recognition, other sequence elements are required for both constitutive and alternative splicing (1, 2). Many splicing-regulatory elements (SREs) have been identified by experimental or computational approaches (3-10). Among them, two classes of well studied SREs are exonic splicing enhancers (ESEs) recognized by SR proteins, and exonic splicing silencers (ESSs) recognized by certain hnRNP proteins (1, 2). Adding further complexity, the effect of an SRE on splicing is often context-dependent. For example, an SR-protein-dependent ESE element, when present in an intron, can act as an intronic splicing silencer (ISS) to repress splicing (11), whereas a number of ESSs, such as the GGG motif, are also pote...

Global regulation of alternative splicing by adenosine deaminase acting on RNA (ADAR)

“…S4b). These 150-base intronic regions were shown to contain regulatory sequences for splicing (Wang and Burge 2008;Zhang et al 2008a;Woolfe et al 2010).…”

Prediction of single‐nucleotide substitutions that result in exon skipping: identification of a splicing silencer inBRCA1exon 6

“…Each utility perform a local search of the exonic sequence for putative ESEs/PESSs (PESEs/PESSs) [Zhang and Chasin, 2004], FAS-ESSs [Wang et al, 2004], RESCUEESEs [Fairbrother et al, 2002;Fairbrother et al, 2004], neighbourhood inference [Stadler et al, 2006] and exon/intron identity elements (EIE/IIEs) [Zhang et al, 2008] The performance of HOT-SKIP was tested with a set of 37 previously reported mutations that resulted in exon skipping and genetic disease (Supplementary Table 4). …”

“…To determine which of the previously determined ESSs or ESEs correlate best with exon inclusion levels, the number of RESCUE-ESEs [Fairbrother et al, 2002;Fairbrother et al, 2004], FASS-ESSs [Wang et al, 2004], PESEs/PESSs [Zhang and Chasin, 2004], neighbourhood inference elements [Stadler et al, 2006] and EIE/IIEs [Zhang et al, 2008] was calculated for each segment. In addition, we employed the ESEfinder [Cartegni et al, 2003;Smith et al, 2006] to correlate exon inclusion levels with predicted scores of putative ESEs for SR proteins.…”

Genomic signatures of germline gene expression

“…For example, selection may act to reduce crossovers within genes if recombination is mutagenic (McVean et al 2004) and could result in strand-biased composition for efficient splicing (Zhang et al 2008). Selection could also explain the enrichment of Alus near genes of specific functional categories if selection against transposable elements is weaker for these genes (Grover et al 2003) or if selection promotes the fixation of Alus with regulatory potential (Tsirigos and Rigoutsos 2009).…”