Dataset Information

Developmental analysis of spliceosomal snRNA isoform expression.

ABSTRACT: Pre-mRNA splicing is a critical step in eukaryotic gene expression that contributes to proteomic, cellular, and developmental complexity. Small nuclear (sn)RNAs are core spliceosomal components; however, the extent to which differential expression of snRNA isoforms regulates splicing is completely unknown. This is partly due to difficulties in the accurate analysis of the spatial and temporal expression patterns of snRNAs. Here, we use high-throughput RNA-sequencing (RNA-seq) data to profile expression of four major snRNAs throughout Drosophila development. This analysis shows that individual isoforms of each snRNA have distinct expression patterns in the embryo, larva, and pharate adult stages. Expression of these isoforms is more heterogeneous during embryogenesis; as development progresses, a single isoform from each snRNA subtype gradually dominates expression. Despite the lack of stable snRNA orthologous groups during evolution, this developmental switching of snRNA isoforms also occurs in distantly related vertebrate species, such as Xenopus, mouse, and human. Our results indicate that expression of snRNA isoforms is regulated and lays the foundation for functional studies of individual snRNA isoforms.

SUBMITTER: Lu Z

PROVIDER: S-EPMC4291461 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Developmental analysis of spliceosomal snRNA isoform expression.

Lu Zhipeng Z Matera A Gregory AG

G3 (Bethesda, Md.) 20141121 1

Pre-mRNA splicing is a critical step in eukaryotic gene expression that contributes to proteomic, cellular, and developmental complexity. Small nuclear (sn)RNAs are core spliceosomal components; however, the extent to which differential expression of snRNA isoforms regulates splicing is completely unknown. This is partly due to difficulties in the accurate analysis of the spatial and temporal expression patterns of snRNAs. Here, we use high-throughput RNA-sequencing (RNA-seq) data to profile exp ...[more]

PMID: 25416704

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Project description:BackgroundGenetic variants in the voltage-gated sodium channels SCN1A, SCN2A, SCN3A, and SCN8A are leading causes of epilepsy, developmental delay, and autism spectrum disorder. The mRNA splicing patterns of all four genes vary across development in the rodent brain, including mutually exclusive copies of the fifth protein-coding exon detected in the neonate (5N) and adult (5A). A second pair of mutually exclusive exons is reported in SCN8A only (18N and 18A). We aimed to quantify the expression of individual exons in the developing human brain.MethodsRNA-seq data from 783 human brain samples across development were analyzed to estimate exon-level expression. Developmental changes in exon utilization were validated by assessing intron splicing. Exon expression was also estimated in RNA-seq data from 58 developing mouse neocortical samples.ResultsIn the mature human neocortex, exon 5A is consistently expressed at least 4-fold higher than exon 5N in all four genes. For SCN2A, SCN3A, and SCN8A, a brain-wide synchronized 5N to 5A transition occurs between 24 post-conceptual weeks (2nd trimester) and 6 years of age. In mice, the equivalent 5N to 5A transition begins at or before embryonic day 15.5. In SCN8A, over 90% of transcripts in the mature human cortex include exon 18A. Early in fetal development, most transcripts include 18N or skip both 18N and 18A, with a transition to 18A inclusion occurring from 13 post-conceptual weeks to 6 months of age. No other protein-coding exons showed comparably dynamic developmental trajectories.ConclusionsExon usage in SCN1A, SCN2A, SCN3A, and SCN8A changes dramatically during human brain development. These splice isoforms, which alter the biophysical properties of the encoded channels, may account for some of the observed phenotypic differences across development and between specific variants. Manipulation of the proportion of splicing isoforms at appropriate stages of development may act as a therapeutic strategy for specific mutations or even epilepsy in general.

Project description:BACKGROUND: Alternative splicing and isoform level expression profiling is an emerging field of interest within genomics. Splicing sensitive microarrays, with probes targeted to individual exons or exon-junctions, are becoming increasingly popular as a tool capable of both expression profiling and finer scale isoform detection. Despite their intuitive appeal, relatively little is known about the performance of such tools, particularly in comparison with more traditional 3' targeted microarrays. Here, we use the well studied Microarray Quality Control (MAQC) dataset to benchmark the Affymetrix Exon Array, and compare it to two other popular platforms: Illumina, and Affymetrix U133. RESULTS: We show that at the gene expression level, the Exon Array performs comparably with the two 3' targeted platforms. However, the interplatform correlation of the results is slightly lower than between the two 3' arrays. We show that some of the discrepancies stem from the RNA amplification protocols, e.g. the Exon Array is able to detect expression of non-polyadenylated transcripts. More importantly, we show that many other differences result from the ability of the Exon Array to monitor more detailed isoform-level changes; several examples illustrate that changes detected by the 3' platforms are actually isoform variations, and that the nature of these variations can be resolved using Exon Array data. Finally, we show how the Exon Array can be used to detect alternative isoform differences, such as alternative splicing, transcript termination, and alternative promoter usage. We discuss the possible pitfalls and false positives resulting from isoform-level analysis. CONCLUSION: The Exon Array is a valuable tool that can be used to profile gene expression while providing important additional information regarding the types of gene isoforms that are expressed and variable. However, analysis of alternative splicing requires much more hands on effort and visualization of results in order to correctly interpret the data, and generally results in considerably higher false positive rates than expression analysis. One of the main sources of error in the MAQC dataset is variation in amplification efficiency across transcripts, most likely caused by joint effects of elevated GC content in the 5' ends of genes and reduced likelihood of random-primed first strand synthesis in the 3' ends of genes. These effects are currently not adequately corrected using existing statistical methods. We outline approaches to reduce such errors by filtering out potentially problematic data.

Dataset Information

Developmental analysis of spliceosomal snRNA isoform expression.

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Developmental analysis of spliceosomal snRNA isoform expression.

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