Abstract: Metazoan genes are encrypted with at least two superimposed codes: the genetic code to specify the primary structure of proteins and the splicing code to expand their proteomic output via alternative splicing. Here, we define the specificity of a central regulator of pre-mRNA splicing, the conserved, essential splicing factor SFRS1. Cross-linking immunoprecipitation and high-throughput sequencing (CLIP-seq) identified 23,632 binding sites for SFRS1 in the transcriptome of cultured human embryonic kidney cells. SFRS1 was found to engage many different classes of functionally distinct transcripts including mRNA, miRNA, snoRNAs, ncRNAs, and conserved intergenic transcripts of unknown function. The majority of these diverse transcripts share a purine-rich consensus motif corresponding to the canonical SFRS1 binding site. The consensus site was not only enriched in exons cross-linked to SFRS1 in vivo, but was also enriched in close proximity to splice sites. mRNAs encoding RNA processing factors were significantly overrepresented, suggesting that SFRS1 may broadly influence the post-transcriptional control of gene expression in vivo. Finally, a search for the SFRS1 consensus motif within the Human Gene Mutation Database identified 181 mutations in 82 different genes that disrupt predicted SFRS1 binding sites. This comprehensive analysis substantially expands the known roles of human SR proteins in the regulation of a diverse array of RNA transcripts.
Abstract: BACKGROUND: Human genes undergo various patterns of pre-mRNA splicing across different tissues. Such variation is primarily regulated by trans-acting factors that bind on exonic and intronic cis-acting RNA elements (CAEs). Here we report a computational method to mechanistically identify cis-acting RNA elements that contribute to the tissue-specific alternative splicing pattern. This method is an extension of our previous model, SplicingModeler, which predicts the significant CAEs that contribute to the splicing differences between two tissues. In this study, we introduce tissue-specific functional levels estimation step, which allows evaluating regulatory functions of predicted CAEs that are involved in more than two tissues. RESULTS: Using a publicly available Affymetrix Genechip Human Exon Array dataset, our method identifies 652 cis-acting RNA elements (CAEs) across 11 human tissues. About one third of predicted CAEs can be mapped to the known RBP (RNA binding protein) binding sites or match with other predicted exonic splicing regulator databases. Interestingly, the vast majority of predicted CAEs are in intronic regulatory regions. A noticeable exception is that many exonic elements are found to regulate the alternative splicing between cerebellum and testes. Most identified elements are found to contribute to the alternative splicing between two tissues, while some are important in multiple tissues. This suggests that genome-wide alternative splicing patterns are regulated by a combination of tissue-specific cis-acting elements and "general elements" whose functional activities are important but differ across multiple tissues. CONCLUSION: In this study, we present a model-based computational approach to identify potential cis-acting RNA elements by considering the exon splicing variation as the combinatorial effects of multiple cis-acting regulators. This methodology provides a novel evaluation on the functional levels of cis-acting RNA elements by estimating their tissue-specific functions on various tissues.
Abstract: The serine and arginine-rich protein family (SR proteins) are highly conserved regulators of pre-mRNA splicing. SF2/ASF, a prototype member of the SR protein family, is a multifunctional RNA binding protein with roles in pre-mRNA splicing, mRNA export and mRNA translation. These observations suggest the intriguing hypothesis that SF2/ASF may couple splicing and translation of specific mRNA targets in vivo. Unfortunately the paucity of endogenous mRNA targets for SF2/ASF has hindered testing of this hypothesis. Here, we identify endogenous mRNAs directly cross-linked to SF2/ASF in different sub-cellular compartments. Cross-Linking Immunoprecipitation (CLIP) captures the in situ specificity of protein-RNA interaction and allows for the simultaneous identification of endogenous RNA targets as well as the locations of binding sites within the RNA transcript. Using the CLIP method we identified 326 binding sites for SF2/ASF in RNA transcripts from 180 protein coding genes. A purine-rich consensus motif was identified in binding sites located within exon sequences but not introns. Furthermore, 72 binding sites were occupied by SF2/ASF in different sub-cellular fractions suggesting that these binding sites may influence the splicing or translational control of endogenous mRNA targets. We demonstrate that ectopic expression of SF2/ASF regulates the splicing and polysome association of transcripts derived from the SFRS1, PABC1, NETO2 and ENSA genes. Taken together the data presented here indicate that SF2/ASF has the capacity to co-regulate the nuclear and cytoplasmic processing of specific mRNAs and provide further evidence that the nuclear history of an mRNA may influence its cytoplasmic fate.
Abstract: This is a first report, using our MotifModeler informatics program, to simultaneously identify transcription factor (TF) and microRNA (miRNA) binding sites from gene expression microarray data. Based on the assumption that gene expression is controlled by combinatorial effects of transcription factors binding in the 5'-upstream regulatory region and miRNAs binding in the 3'-untranslated region (3'-UTR), we developed a model for (1) predicting the most influential cis-acting elements under a given biological condition, and (2) estimating the effects of those elements on gene expression levels. The regulatory regions, TF and miRNA, which mediate the differential genes expression in fetal alcohol syndrome were unknown; microarray data from alcohol exposure paradigm was used. The model predicted strong inhibitory effects of 5' cis-acting elements and stimulatory effects of 3'-UTR under alcohol treatment. Current predictive model derived a key hypothesis for the first time a novel role of miRNAs in gene expression changes associated with abnormal mouse embryo development after alcohol exposure. This suggests that disturbance of miRNA functions may contribute to the alcohol-induced developmental deficiencies.
Abstract: BACKGROUND: Prostate cancer is one of the leading causes of cancer death in men. Androgen ablation, the most commonly-used therapy for progressive prostate cancer, is ineffective once the cancer cells become androgen-independent. The regulatory mechanisms that cause this transition (from androgen-dependent to androgen-independent) remain unknown. In this study, based on the microarray data comparing global gene expression patterns in the prostate tissue between androgen-dependent and -independent prostate cancer patients, we identify a set of transcription factors and microRNAs that potentially cause such difference, using a model-based computational approach. RESULTS: From 335 position weight matrices in the TRANSFAC database and 564 microRNAs in the microRNA registry, our model identify 5 transcription factors and 7 microRNAs to be potentially responsible for the level of androgen dependency. Of these transcription factors and microRNAs, the estimated function of all the 5 transcription factors are predicted to be inhibiting transcription in androgen-independent samples comparing with the dependent ones. Six out of 7 microRNAs, however, demonstrated stimulatory effects. We also find that the expression levels of three predicted transcription factors, including AP-1, STAT3 (signal transducers and activators of transcription 3), and DBP (albumin D-box) are significantly different between androgen-dependent and -independent patients. In addition, microRNA microarray data from other studies confirm that several predicted microRNAs, including miR-21, miR-135a, and miR-135b, demonstrate differential expression in prostate cancer cells, comparing with normal tissues. CONCLUSION: We present a model-based computational approach to identify transcription factors and microRNAs influencing the progression of androgen-dependent prostate cancer to androgen-independent prostate cancer. This result suggests that the capability of transcription factors to initiate transcription and microRNAs to facilitate mRNA degradation are both decreased in androgen-independent prostate cancer. The proposed model-based approach indicates that considering combinatorial effects of transcription factors and microRNAs in a unified model provides additional transcriptional and post-transcriptional regulatory mechanisms on global gene expression in the prostate cancer with different hormone-dependency.
Abstract: Massively parallel pyrosequencing is a high-throughput technology that can sequence hundreds of thousands of DNA/RNA fragments in a single experiment. Combining it with immunoprecipitation-based biochemical assays, such as cross-linking immunoprecipitation (CLIP), provides a genome-wide method to detect the sites at which proteins bind DNA or RNA. In a CLIP-pyrosequencing experiment, the resolutions of the detected protein binding regions are partially determined by the length of the detected RNA fragments (CLIP amplicons) after trimming by RNase digestion. The lengths of these fragments usually range from 50-70 nucleotides. Many genomic regions are marked by multiple RNA fragments. In this paper, we report an empirical approach to refine the localization of protein binding regions by using the distribution pattern of the detected RNA fragments and the sequence specificity of RNase digestion. We present two regions to which multiple amplicons map as examples to demonstrate this approach.
Abstract: We report an integrative bioinformatic platform for next-generation sequencing data analysis, xIP-seq, which is built on GenePattern package. The purpose for this tool is to provide analysis pipelines for data derived from next-generation sequencing following a variety of experiments using immunoprecipitation, such as ChIP-seq (chromatin immunoprecipitation following sequencing) for analysis of DNA binding protein, CLIP-seq (cross-linking immunoprecipitation following sequencing) for analysis of RNA binding protein, and MeDIP-seq (methylation DNA immunoprecipitation following sequencing) for DNA methylation profiles. xIP-seq platform provides standardized data pre-processing workflows which prepare raw data from various sequencing platforms for further analysis, and several bioinformatic applications including sequence annotation and analysis of Pol II binding pattern and histone modification.. The tertiary module for sequence annotation is particularly discussed. xIP-seq also allows users to construct new analysis pipelines tailored for individual biological interests by employing currently available modules or creating new ones. As an example, a “CLIP-seq Mapper” pipeline is created to map CLIP-seq-derived data to human genome to identify the genome-wide annotation of SFRS1 binding pattern. This implementation demonstrates the significance of xIP-seq platform to analyze massive amounts of next-generation sequencing data and provide automatic, flexible, and intelligent enterprise-level bioinformatic solutions.
Abstract: Here we describe a model-based approach to predict cis-acting RNA elements which regulate tissue-specific alternative splicing. The model facilitates the identification of cis-acting elements (or CAE) and the estimation of their activities, considering the splicing variants between two different tissues as the combinatorial functions of multiple elements. We implement this model on a set of differentially expressed exons, between heart and liver, derived from Affymetrix GeneChip Human Exon 1.0 ST Array sample data. Focusing on hexamers, we select top 15 motifs with greatest cumulative exon inclusion (EIC) scores as the potential cis-acting elements. Eight of the total 15 hexamers are validated based on known exonic splicing regulators (ESRs) and predicted ESRs (PESRs). Permutation test demonstrates that the predicted EIC scores are statistically significant. Based on the prediction, we propose that PTB, hnRNP-B, SRp40, as well as other unknown factors are involved in the tissue-specific alternative splicing between heart and liver.
