Stochastic principles governing alternative splicing of RNA

<div><p>The dominance of the major transcript isoform relative to other isoforms from the same gene generated by alternative splicing (AS) is essential to the maintenance of normal cellular physiology. However, the underlying principles that determine such dominance remain unknown. Here, we analyzed the physical AS process and found that it can be modeled by a stochastic minimization process, which causes the scaled expression levels of all transcript isoforms to follow the same Weibull extreme value distribution. Surprisingly, we also found a simple equation to describe the median frequency of transcript isoforms of different dominance. This two-parameter Weibull model provides the statistical distribution of all isoforms of all transcribed genes, and reveals that previously unexplained observations concerning relative isoform expression derive from these principles.</p></div

The frequency distribution of the <i>k</i>th dominant transcript isoform.

<p>(A) <i>k</i> = 1. (B) <i>k</i> = 2. <i>k</i> is the rank of a transcript isoform. <i>M</i> is the number of transcript isoforms for a gene. Black curves represent frequency distribution of the experimental RNA-seq data. Red curves represent the frequency distribution of the simulated data from Weibull distribution <i>W(0</i>.<i>39)</i>. KLd is the Kullback-Leibler divergence between the two distributions.</p

Transcript isoform expression pattern of two genes in different conditions.

<p>(A) BRD4. (B) SRSF7. Among 11 transcript isoforms of BRD4 and 12 transcript isoforms of SRSF7, ENST00000371835 and ENST00000409276 are the most dominant isoforms in all four activated conditions, ENST00000263377 and ENST00000477635 are the most dominant isoforms in all four resting conditions, respectively. This result indicates the major transcript isoform can be regulated by single external signal.</p

A model of alternative splicing.

<p>(A) Splicing factor U1 and U2AF search the 5’ GU and 3’ AG splicing sites by 3D and 1D Brownian motion. Multiple candidate splice sites compete for the binding of U1 and U2AF. The binding is ATP-independent and reversible. (B) The binding of U1 and U2AF to the splice sites becomes stable only after the ATP-dependent binding of U2 snRNP. The identification of each intron is equivalent to a minimization process that U1 and U2AF dynamically search their global or local minimal energy sites on the pre-mRNA segment presented for AS. (C) The scaled expression level of transcript isoform follows type III extreme value distribution—a Weibull distribution. The approximate values of parameters <i>a (0</i>.<i>44)</i> and <i>b (0</i>.<i>6)</i> are estimated by curve fitting. Black curve represents the distribution of scaled expression level from experimental data. Red curve represent the Weibull distribution produced by curve fitting.</p

Scaffold proteins are widespread in signaling networks.

<p>(<i>A</i>) PPI distance of KSR pairs and all human protein pairs. The PPI distance of a protein pair is defined as the shortest distance of the two proteins in PPI network. KSR pairs are significantly enriched in PPI distance = 2. In fact, 24.9% of KSR pairs have PPI distance of 2, while only 2.7% of all human protein pairs have the same PPI distance. (<i>B</i>) Network motifs in which one protein interacts with a series of proteins and these proteins form a cascade via KSRs. These network motifs are enriched, suggesting that scaffold proteins are widespread in signaling pathways.</p

Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction

<div><p>Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components. In this study, we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks. We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways. The computational prediction was validated using a protein microarray-based approach. The predicted scaffold proteins showed several interesting characteristics, as we expected from the functionality of scaffold proteins. We found that the scaffold proteins are likely to interact with each other, which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers. Interestingly, a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners. Furthermore, we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process.</p></div

Experimental validations for <i>CSNK2A1</i> and <i>MAPK9</i>.

<p>A human proteome microarray, comprised of 17,000 individually purified human proteins in full-length, was used to perform phosphorylation reactions with CKII (CSNK2A1) and JNK2 (MAPK9) in the presence or absence of their predicted scaffold proteins, ATF2 and PIN1. Phosphorylation signals were detected by exposure of the human proteome microarrays to X-ray film. Positive hits in red boxes were identified by visual inspection.</p

Strategy to predict scaffold proteins.

<p>For each potential scaffold protein, we corrected the effect of interaction degree of the protein and the length of associated pathways. We utilized the randomized PPI to assess the significance of a predicted scaffold protein. The random PPI keep the same PPI degree for each protein by randomly selecting two PPI pairs and changing their partners.</p

Characterization of scaffold proteins.

<p>(<i>A</i>) Enriched GO terms for scaffold proteins. (<i>B</i>) Enriched protein domains defined by Pfam in scaffold proteins. The GO and Pfam terms are sorted increasingly from left to right by p-value. (<i>C</i>) Distribution of protein lengths. (<i>D</i>) Distribution of evolutionary conservation.</p

Specificity of scaffold proteins and pathways.

<p>(<i>A</i>) Number of pathways related to scaffold proteins. 408 pathways (67.4%) are found to be associated with only one scaffold protein. (<i>B</i>) Number of scaffold proteins related to pathways. Specifically, 83 scaffold proteins are associated with only one pathways, while 28 scaffold proteins are related to >10 pathways.</p