MiasDB: A Database of Molecular Interactions Associated with Alternative Splicing of Human Pre-mRNAs

Temporal Dynamic Analysis of Alternative Splicing During Embryonic Development in Zebrafish

Alternative splicing is pervasive in mammalian genomes and involved in embryo development, whereas research on crosstalk of alternative splicing and embryo development was largely restricted to mouse and human and the alternative splicing regulation during embryogenesis in zebrafish remained unclear. We constructed the alternative splicing atlas at 18 time-course stages covering maternal-to-zygotic transition, gastrulation, somitogenesis, pharyngula stages, and post-fertilization in zebrafish. The differential alternative splicing events between different developmental stages were detected. The results indicated that abundance alternative splicing and differential alternative splicing events are dynamically changed and remarkably abundant during the maternal-to-zygotic transition process. Based on gene expression profiles, we found splicing factors are expressed with specificity of developmental stage and largely expressed during the maternal-to-zygotic transition process. The better performance of cluster analysis was achieved based on the inclusion level of alternative splicing. The biological function analysis uncovered the important roles of alternative splicing during embryogenesis. The identification of isoform switches of alternative splicing provided a new insight into mining the regulated mechanism of transcript isoforms, which always is hidden by gene expression. In conclusion, we inferred that alternative splicing activation is synchronized with zygotic genome activation and discovered that alternative splicing is coupled with transcription during embryo development in zebrafish. We also unveiled that the temporal expression dynamics of splicing factors during embryo development, especially co-orthologous splicing factors. Furthermore, we proposed that the inclusion level of alternative splicing events can be employed for cluster analysis as a novel parameter. This work will provide a deeper insight into the regulation of alternative splicing during embryogenesis in zebrafish.

Systemic characterization of alternative splicing related to prognosis, immune infiltration, and drug sensitivity analysis in ovarian cancer

Background

Alternative splicing (AS) plays an essential role in tumorigenesis and progression. This study intended to construct an innovative prognostic model based on AS events to gain more precise survival prediction and search for potential therapeutic targets in ovarian cancer.

Methods

Seven types of AS events in ovarian serous cystadenocarcinoma (OV) patients with RNA-seq were obtained using The Cancer Genome Atlas (TCGA) SpliceSeq tool and database. Cox and Kaplan-Meier curve analyses were employed to establish the prognostic models. Relying on drug sensitivity data from the CellMiner database, Genomics of Drug Sensitivity (GDS) was adopted to estimate the platinum-sensitive analysis. Furthermore, a prognostic splicing factor (SF)-AS network was constructed using Cytoscape. Finally, in order to explore the influence of the tumor microenvironment on the prognosis of OV patients, we first combined a similar network fusion and consensus clustering (SNF-CC) algorithm to identify three OV subtypes based on survival-related AS events and then utilized single-sample Gene Set Enrichment Analysis (ssGSEA) method to perform immune cell infiltration analysis.

Results

A total of 48,049 AS events and 21,841 related genes were selected from 318 OV samples, and 2,206 AS events associated with disease-free survival (DFS) were identified. Multivariate Cox and Kaplan-Meier curve analyses were then employed to establish the prognostic models. Receiver operating characteristic (ROC) analysis from 0.59 to 0.75 showed that these models were highly efficient in distinguishing patient survival. GDS was adopted with the CellMiner database to provide some insights for platinum-sensitive analysis of OV. Furthermore, a prognostic SF-AS network, which discovered a significant connection between SFs and prognostic AS genes, was constructed using Cytoscape. The combined SNF-CC algorithm revealed three distinct OV subtypes based on the prognostic AS events, and the associations between this novel molecular classification and immune cell infiltration were further explored.

Conclusions

We developed a powerful prognostic AS signature for OV and provided a deeper understanding of SF-AS network regulatory mechanisms, as well as platinum-sensitive and cancer immune microenvironments. These results revealed various candidate biomarkers and potential targets for OV treatment strategies.

The long non-coding RNA PFI protects against pulmonary fibrosis by interacting with splicing regulator SRSF1

Pulmonary fibrosis (PF) is a type of interstitial pneumonia with complex etiology and high mortality, characterized by progressive scarring of the alveolar interstitium and myofibroblastic lesions. Recently, there has been growing appreciation of the importance of long non-coding RNAs (lncRNAs) in organ fibrosis. The aim of this study was to investigate the role of lncRNAs in lung fibrosis. We used a qRT-PCR assay to identify dysregulated lncRNAs in the lungs of mice with experimental, bleomycin (BLM)-induced pulmonary fibrosis, and a series of molecular assays to assess the role of the novel lncRNA NONMMUT060091, designated as pulmonary fibrosis inhibitor (PFI), which was significantly downregulated in lung fibrosis. Functionally, knockdown of endogenous PFI by smart silencer promoted proliferation, differentiation, and extracellular matrix (ECM) deposition in primary mouse lung fibroblasts (MLFs). In contrast, overexpression of PFI partially abrogated TGF-β1-induced fibrogenesis both in MLFs and in the human fetal lung fibroblast MRC-5 cells. Similarly, PFI overexpression attenuated BLM-induced pulmonary fibrosis compared with wild type (WT) mice. Mechanistically, using chromatin isolation by RNA purification-mass spectrometry (ChIRP-MS) and an RNA pull-down assay, PFI was found to directly bind Serine/arginine-rich splicing factor 1 (SRSF1), and to repress its expression and pro-fibrotic activity. Furthermore, silencing of SRSF1 inhibited TGF-β1-induced proliferation, differentiation, and ECM deposition in MRC-5 cells by limiting the formation of the EDA+Fn1 splicing isoform; whereas forced expression of SRSF1 by intratracheal injection of adeno-associated virus 5 (AAV5) ablated the anti-fibrotic effect of PFI in BLM-treated mice. Overall, these data reveal that PFI mitigated pulmonary fibrosis through negative regulation of the expression and activity of SRSF1 to decrease the formation of the EDA+Fn1 splicing isoform, and suggest that PFI and SRSF1 may serve as potential targets for the treatment of lung fibrosis.

Systematics for types and effects of RNA variations

Systematics is described for annotation of variations in RNA molecules. The conceptual framework is part of Variation Ontology (VariO) and facilitates depiction of types of variations, their functional and structural effects and other consequences in any RNA molecule in any organism. There are more than 150 RNA related VariO terms in seven levels, which can be further combined to generate even more complicated and detailed annotations. The terms are described together with examples, usually for variations and effects in human and in diseases. RNA variation type has two subcategories: variation classification and origin with subterms. Altogether six terms are available for function description. Several terms are available for affected RNA properties. The ontology contains also terms for structural description for affected RNA type, post-transcriptional RNA modifications, secondary and tertiary structure effects and RNA sugar variations. Together with the DNA and protein concepts and annotations, RNA terms allow comprehensive description of variations of genetic and non-genetic origin at all possible levels. The VariO annotations are readable both for humans and computer programs for advanced data integration and mining.

Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources

In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.

The Emerging Role of the RBM20 and PTBP1 Ribonucleoproteins in Heart Development and Cardiovascular Diseases

Alternative splicing is a regulatory mechanism essential for cell differentiation and tissue organization. More than 90% of human genes are regulated by alternative splicing events, which participate in cell fate determination. The general mechanisms of splicing events are well known, whereas only recently have deep-sequencing, high throughput analyses and animal models provided novel information on the network of functionally coordinated, tissue-specific, alternatively spliced exons. Heart development and cardiac tissue differentiation require thoroughly regulated splicing events. The ribonucleoprotein RBM20 is a key regulator of the alternative splicing events required for functional and structural heart properties, such as the expression of TTN isoforms. Recently, the polypyrimidine tract-binding protein PTBP1 has been demonstrated to participate with RBM20 in regulating splicing events. In this review, we summarize the updated knowledge relative to RBM20 and PTBP1 structure and molecular function; their role in alternative splicing mechanisms involved in the heart development and function; RBM20 mutations associated with idiopathic dilated cardiovascular disease (DCM); and the consequences of RBM20-altered expression or dysfunction. Furthermore, we discuss the possible application of targeting RBM20 in new approaches in heart therapies.

NPInter v4.0: an integrated database of ncRNA interactions

Noncoding RNAs (ncRNAs) play crucial regulatory roles in a variety of biological circuits. To document regulatory interactions between ncRNAs and biomolecules, we previously created the NPInter database (http://bigdata.ibp.ac.cn/npinter). Since the last version of NPInter was issued, a rapidly growing number of studies have reported novel interactions and accumulated numerous high-throughput interactome data. We have therefore updated NPInter to its fourth edition in which are integrated 600 000 new experimentally identified ncRNA interactions. ncRNA-DNA interactions derived from ChIRP-seq data and circular RNA interactions have been included in the database. Additionally, disease associations were annotated to the interacting molecules. The database website has also been redesigned with a more user-friendly interface and several additional functional modules. Overall, NPInter v4.0 now provides more comprehensive data and services for researchers working on ncRNAs and their interactions with other biomolecules.

Predicting Functional Effects of Synonymous Variants: A Systematic Review and Perspectives

Recent advances in high-throughput experimentation have put the exploration of genome sequences at the forefront of precision medicine. In an effort to interpret the sequencing data, numerous computational methods have been developed for evaluating the effects of genome variants. Interestingly, despite the fact that every person has as many synonymous (sSNV) as non-synonymous single nucleotide variants, our ability to predict their effects is limited. The paucity of experimentally tested sSNV effects appears to be the limiting factor in development of such methods. Here, we summarize the details and evaluate the performance of nine existing computational methods capable of predicting sSNV effects. We used a set of observed and artificially generated variants to approximate large scale performance expectations of these tools. We note that the distribution of these variants across amino acid and codon types suggests purifying evolutionary selection retaining generated variants out of the observed set; i.e., we expect the generated set to be enriched for deleterious variants. Closer inspection of the relationship between the observed variant frequencies and the associated prediction scores identifies predictor-specific scoring thresholds of reliable effect predictions. Notably, across all predictors, the variants scoring above these thresholds were significantly more often generated than observed. which confirms our assumption that the generated set is enriched for deleterious variants. Finally, we find that while the methods differ in their ability to identify severe sSNV effects, no predictor appears capable of definitively recognizing subtle effects of such variants on a large scale.

Bioinformatics challenges and perspectives when studying the effect of epigenetic modifications on alternative splicing

It is widely known that epigenetic modifications are important in regulating transcription, but several have also been reported in alternative splicing. The regulation of pre-mRNA splicing is important to explain proteomic diversity and the misregulation of splicing has been implicated in many diseases. Here, we give a brief overview of the role of epigenetics in alternative splicing and disease. We then discuss the bioinformatics methods that can be used to model interactions between epigenetic marks and regulators of splicing. These models can be used to identify alternative splicing and epigenetic changes across different phenotypes.

This article is part of a discussion meeting issue ‘Frontiers in epigenetic chemical biology’.