Browse
Submit Data
Databases
API
Help

Dataset Information

5 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Principles and Practical Considerations for the Analysis of Disease-Associated Alternative Splicing Events Using the Gateway Cloning-Based Minigene Vectors pDESTsplice and pSpliceExpress.

ABSTRACT: Splicing is an important RNA processing step. Genetic variations can alter the splicing process and thereby contribute to the development of various diseases. Alterations of the splicing pattern can be examined by gene expression analyses, by computational tools for predicting the effects of genetic variants on splicing, and by splicing reporter minigene assays for studying alternative splicing events under defined conditions. The minigene assay is based on transient transfection of cells with a vector containing a genomic region of interest cloned between two constitutive exons. Cloning can be accomplished by the use of restriction enzymes or by site-specific recombination using Gateway cloning. The vectors pDESTsplice and pSpliceExpress represent two minigene systems based on Gateway cloning, which are available through the Addgene plasmid repository. In this review, we describe the features of these two splicing reporter minigene systems. Moreover, we provide an overview of studies in which determinants of alternative splicing were investigated by using pDESTsplice or pSpliceExpress. The studies were reviewed with regard to the investigated splicing regulatory events and the experimental strategy to construct and perform a splicing reporter minigene assay. We further elaborate on how analyses on the regulation of RNA splicing offer promising prospects for gaining important insights into disease mechanisms.

SUBMITTER: Putscher E

PROVIDER: S-EPMC8152502 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Radiation-induced alternative transcription and splicing events and their applicability to practical biodosimetry.

Project description:Accurate assessment of the individual exposure dose based on easily accessible samples (e.g. blood) immediately following a radiological accident is crucial. We aimed at developing a robust transcription-based signature for biodosimetry from human peripheral blood mononuclear cells irradiated with different doses of X-rays (0.1 and 1.0 Gy) at a dose rate of 0.26 Gy/min. Genome-wide radiation-induced changes in mRNA expression were evaluated at both gene and exon level. Using exon-specific qRT-PCR, we confirmed that several biomarker genes are alternatively spliced or transcribed after irradiation and that different exons of these genes exhibit significantly different levels of induction. Moreover, a significant number of radiation-responsive genes were found to be genomic neighbors. Using three different classification models we found that gene and exon signatures performed equally well on dose prediction, as long as more than 10 features are included. Together, our results highlight the necessity of evaluating gene expression at the level of single exons for radiation biodosimetry in particular and transcriptional biomarker research in general. This approach is especially advisable for practical gene expression-based biodosimetry, for which primer- or probe-based techniques would be the method of choice.

| S-EPMC4725928 | biostudies-literature

Gateway cloning vectors for the LexA-based binary expression system in Drosophila.

Project description:Studies on the formation of connections in the developing nervous system are greatly aided by methods that permit the differential visualisation and manipulation of pre- and postsynaptic partner neurons. This has been facilitated by the advent of the LexA-based, GAL4/UAS-independent, binary expression system. On the molecular side, the introduction of DNA sequences into expression vectors has been simplified by the Invitrogen Gateway cloning technology. We have developed cloning vectors that combine the Gateway cloning technology with the LexA-based genetic expression system. These vectors facilitate the creation of driver and reporter constructs for the generation of Drosophila transgenic lines for the new LexA-based binary transcriptional system. We further report a new LexA::GAD sensory neuron driver and a red fluorescent membrane targeted lexAop reporter designed to complement the existing GFP-based lexAop reporter. Using these transgenic lines we have been able to differentially label motor and sensory neuron projections in the ventral nerve cord of Drosophila larvae.

| S-EPMC2913122 | biostudies-literature

Stochastic principles governing alternative splicing of RNA.

Project description: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.

| S-EPMC5614656 | biostudies-literature

ExonImpact: Prioritizing Pathogenic Alternative Splicing Events.

Project description:Alternative splicing (AS) is a closely regulated process that allows a single gene to encode multiple protein isoforms, thereby contributing to the diversity of the proteome. Dysregulation of the splicing process has been found to be associated with many inherited diseases. However, among the pathogenic AS events, there are numerous "passenger" events whose inclusion or exclusion does not lead to significant changes with respect to protein function. In this study, we evaluate the secondary and tertiary structural features of proteins associated with disease-causing and neutral AS events, and show that several structural features are strongly associated with the pathological impact of exon inclusion. We further develop a machine-learning-based computational model, ExonImpact, for prioritizing and evaluating the functional consequences of hitherto uncharacterized AS events. We evaluated our model using several strategies including cross-validation, and data from the Gene-Tissue Expression (GTEx) and ClinVar databases. ExonImpact is freely available at http://watson.compbio.iupui.edu/ExonImpact.

| S-EPMC5390777 | biostudies-literature

Outcome measures for oncology alternative payment models: practical considerations and recommendations.

Project description:ObjectivesThis paper aims to synthesize existing scholarship on quality measures in oncology, with a specific focus on outcome-based quality measures, which are often underutilized. We also present a set of "core outcome measures" that may be considered in future oncology alternative payment models (APMs).Study designOur research consists of a focused literature review, content analysis, and quality measure synthesis and categorization.MethodsWe conducted a focused literature review to generate key evidence on quality measures in oncology. We studied 7 oncology quality assessment frameworks, encompassing 142 quality metrics, and synthesized recommendations using the Center for Medicare and Medicaid Innovation APM toolkit, focusing on outcome measures.ResultsWe present 34 outcome-based oncology quality measures for consideration, which are classified into 5 domains: clinical care (eg, hospital and emergency department visits, treatment effectiveness, mortality), safety (eg, infections, hospital adverse events), care coordination (for hospital and hospice care), patient and caregiver experience, and population health and prevention. Both general and indication-specific outcome measures should be considered in oncology APMs, as appropriate. Utilizing outcome-based measures will require addressing multiple challenges, ranging from risk adjustment to data quality assurance.ConclusionsOncology care will benefit from a more rigorous approach to quality assessment. The success of oncology APMs will require a robust set of quality measures that are relevant to patients, providers, and payers.

| S-EPMC6927528 | biostudies-literature

Mapping alternative splicing events in colorectal cancer.

Project description:Although aberrant splicing events of genes are closely related to the development and progression of colorectal cancer (CRC), the mapping of abnormal splicing events, especially alternative splicing (AS) event types and the underlying effects, remain investigational. In the present study, we analyzed a public RNA-seq database (GSE138202) and identified 14,314 significant AS events in CRC patients compared to healthy individuals. Most of the key genes such as oncogenes involved in the development of CRC have different AS event types. Moreover, the results demonstrate that certain AS events may play a significant role in the functioning of key genes involved in splicing factors and microRNAs. Furthermore, we observed that the oncogene CDK4 in CRC tends to undergo exon 2 skipping AS events, resulting in a stronger tendency for protein expression to form complexes with CCND1, thereby inhibiting the cell cycle and weakening cell proliferation, while enhancing cell migration capability. These findings not only provide new insights into the mechanism of AS in regulating CRC, but also offers a theoretical basis for targeted splicing therapy in CRC.

| S-EPMC11247070 | biostudies-literature

Alternative Splicing Events and Splicing Factors Are Prognostic in Adrenocortical Carcinoma.

Project description:Alternative splicing is involved in the pathogenesis of human diseases, including cancer. Here, we investigated the potential application of alternative splicing events (ASEs) and splicing factors (SFs) in the prognosis of adrenocortical carcinoma (ACC). Transcriptome data from 79 ACC cases were downloaded from The Cancer Genome Atlas database, and percent spliced-in values of seven splicing types were downloaded from The Cancer Genome Atlas SpliceSeq database. By the univariate Cox regression analysis, 1,839 survival-related ASEs were identified. Prognostic indices based on seven types of survival-related ASEs were calculated by multivariate Cox regression analysis. Survival curves and receiver operating characteristic curves were used to assess the diagnostic value of the prognostic model. Independent prognosis analysis identified several ASEs (e.g., THNSL2| 54469| ME) that could be used as biomarkers to predict the prognosis of patients with ACC accurately. By analyzing the co-expression correlation between SFs and ASEs, 188 highly correlated interactions were established. From the protein interaction network, we finally screened six hub SFs, including YBX1, SART1, PRCC, SNRPG, SNRPE, and SF3B4, whose expression levels were significantly related to the overall survival and prognosis of ACC. Our findings provide a reliable model for predicting the prognosis of ACC patients based on aberrant alternative splicing patterns.

| S-EPMC7494975 | biostudies-literature

Alternative splicing events are prognostic in hepatocellular carcinoma.

Project description:Alternative splicing events (ASEs) play a role in cancer development and progression. We investigated whether ASEs are prognostic for overall survival (OS) in hepatocellular carcinoma (HCC). RNA sequencing data was obtained for 343 patients included in The Cancer Genome Atlas. Matched splicing event data for these patients was then obtained from the TCGASpliceSeq database, which includes data for seven types of ASEs. Univariate and multivariate Cox regression analysis demonstrated that 3,814 OS-associated splicing events (OS-SEs) were correlated with OS. Prognostic indices were developed based on the most significant OS-SEs. The prognostic index based on all seven types of ASEs (PI-ALL) demonstrated superior efficacy in predicting OS of HCC patients at 2,000 days compared to those based on single ASE types. Patients were stratified into two risk groups (high and low) based on the median prognostic index. Kaplan-Meier survival analysis demonstrated that PI-ALL had the greatest capacity to distinguish between patients with favorable vs. poor outcomes. Finally, univariate Cox regression analysis demonstrated that the expression of 23 splicing factors was correlated with OS-SEs in the HCC cohort. Our data indicate that a prognostic index based on ASEs is prognostic for OS in HCC.

| S-EPMC6660027 | biostudies-literature

ASpediaFI: Functional Interaction Analysis of Alternative Splicing Events.

Project description:Alternative splicing (AS) regulates biological processes governing phenotypes and diseases. Differential AS (DAS) gene test methods have been developed to investigate important exonic expression from high-throughput datasets. However, the DAS events extracted using statistical tests are insufficient to delineate relevant biological processes. In this study, we developed a novel application, Alternative Splicing Encyclopedia: Functional Interaction (ASpediaFI), to systemically identify DAS events and co-regulated genes and pathways. ASpediaFI establishes a heterogeneous interaction network of genes and their feature nodes (i.e., AS events and pathways) connected by co-expression or pathway gene set knowledge. Next, ASpediaFI explores the interaction network using the random walk with restart algorithm and interrogates the proximity from a query gene set. Finally, ASpediaFI extracts significant AS events, genes, and pathways. To evaluate the performance of our method, we simulated RNA sequencing (RNA-seq) datasets to consider various conditions of sequencing depth and sample size. The performance was compared with that of other methods. Additionally, we analyzed three public datasets of cancer patients or cell lines to evaluate how well ASpediaFI detects biologically relevant candidates. ASpediaFI exhibits strong performance in both simulated and public datasets. Our integrative approach reveals that DAS events that recognize a global co-expression network and relevant pathways determine the functional importance of spliced genes in the subnetwork. ASpediaFI is publicly available at https://bioconductor.org/packages/ASpediaFI.

| S-EPMC9801047 | biostudies-literature

Position-dependent alternative splicing activity revealed by global profiling of alternative splicing events regulated by PTB.

Project description:To gain global insights into the role of the well-known repressive splicing regulator PTB, we analyzed the consequences of PTB knockdown in HeLa cells using high-density oligonucleotide splice-sensitive microarrays. The major class of identified PTB-regulated splicing event was PTB-repressed cassette exons, but there was also a substantial number of PTB-activated splicing events. PTB-repressed and PTB-activated exons showed a distinct arrangement of motifs with pyrimidine-rich motif enrichment within and upstream of repressed exons but downstream of activated exons. The N-terminal half of PTB was sufficient to activate splicing when recruited downstream of a PTB-activated exon. Moreover, insertion of an upstream pyrimidine tract was sufficient to convert a PTB-activated exon to a PTB-repressed exon. Our results show that PTB, an archetypal splicing repressor, has variable splicing activity that predictably depends upon its binding location with respect to target exons.

| S-EPMC2933513 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data