Genome-wide analysis of SRSF10-regulated alternative splicing by deep sequencing of chicken transcriptome

Splicing factor SRSF10 is known to function as a sequence-specific splicing activator that is capable of regulating alternative splicing both in vitro and in vivo. We recently used an RNA-seq approach coupled with bioinformatics analysis to identify the extensive splicing network regulated by SRSF10 in chicken cells. We found that SRSF10 promoted both exon inclusion and exclusion. Functionally, many of the SRSF10-verified alternative exons are linked to pathways of response to external stimulus. Here we describe in detail the experimental design, bioinformatics analysis and GO/pathway enrichment analysis of SRSF10-regulated genes to correspond with our data in the Gene Expression Omnibus with accession number GSE53354. Our data thus provide a resource for studying regulation of alternative splicing in vivo that underlines biological functions of splicing regulatory proteins in cells.


Sample preparation and RNA-seq analysis
It is known that phosphorylated SRSF10 functions as a sequencespecific splicing activator that is capable of regulating alternative splicing both in vitro and in vivo [1,2]. To understand the global profile of the alternative splicing network regulated by SRSF10 in vivo, we carried out an RNA-seq approach to analyze the effects of SRSF10 depletion on splicing regulation in chicken DT40 cells. The general strategy for sample preparation for RNA-seq was provided in Fig. 1A and the detailed experimental procedures were as follows. Wild type (WT) and SRSF10-knockout (KO) DT40 cells were cultured in a standard medium to 90% confluence. Total RNA was isolated from these cells, and cDNA libraries were prepared individually by performing a series of experiments, including poly(A) enrichment, RNA fragmentation, random hexamer-primed cDNA synthesis, linker ligation, size selection and PCR amplification. After selecting approximately 200-bp fragments by agarose gel electrophoresis, Illumina paired-end sequencing adapters were ligated to the DNA fragments for PCR amplification and sequencing with an Illumina HiSeq™ 2000 system.
The bioinformatics analysis pipeline for RNA-seq data was provided in Fig. 1B. Briefly, images generated by sequencers were converted by base calling into nucleotide sequences, which were called raw reads. Then raw reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence to get clean reads with high quality. The clean reads were then aligned to the reference chicken genome (release: galGal4) and the corresponding recently annotated genes (galGal4.0) using the TopHat program (v2.0.6) [3]. In the alignment, preliminary experiments were performed to optimize the alignment parameters to provide the most information on the alternative splicing events. Cufflinks [4] was used to construct KO and WT DT40 transcriptomes and Cuffmerge merges the transcriptomes with the reference gene annotations (galGal4.0) to generate a new comprehensive transcriptome. Finally, a Java program called ASD (alternative splicing detector, available at http://www.novelbio.com/asd/ASD.html) was developed to detect and assess alternative splicing events between the two samples [5].

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Genomics Data

SRSF10 both activates and represses exon inclusion
There are five major modes of alternative splicing (AS) events described in metazoan organisms, including cassette exon, alternative 3′ splice site, alternative 5′ splice site, mutually exclusive exon and retained intron ( Fig. 2A). With a p value cutoff of b 0.05, we were able to identify 167 events belonging to the five major modes of AS from a total number of 10,652 splicing events that changed significantly between the WT and the KO samples by ASD. Specifically, the majority of the affected splicing events (130) belonged to the cassette exon Cufflinks and Cuffmerge were then used to construct and merge DT40 transcriptomes. The merged transcriptome and the read counts in exonic reads and junction reads were then further analyzed using our Java program ASD to identify differential alternative splicing events between WT and KO samples. category. Strikingly, analytical results by ASD indicated that SRSF10 could both activate and repress exon inclusion (Fig. 2B). RT-PCR validation of SRSF10-affected splicing events further demonstrated this prediction. In addition, the selection of alternative 3′ and 5′ splice sites was similarly affected by SRSF10. Taken together, these results provide strong evidence that SRSF10 can promote both exon inclusion and exclusion in vivo.

Function enrichment analysis of SRSF10-affected genes
To understand how loss of SRSF10 affects cellular biological processes, we next performed gene ontology (GO) and pathway analysis of SRSF10-affected genes by using ClueGO [6]. Function enrichment analysis indicated that these affected spliced genes are involved in multiple processes, including histone methylation, response to external stimulus, glucose metabolic processes and mitochondrion distribution (Fig. 3). These results illustrated that SRSF10 might play an important role in a wide range of biological processes by controlling its downstream splicing targets.

Discussion
We present here a unique dataset of RNA-seq results in which we globally analyzed SRSF10-regulated AS events based on deep sequencing of the chicken transcriptome. We found that SRSF10 is involved in all of the common modes of AS, with cassette exons being the most frequent targets for SRSF10 regulation. We also found that SRSF10 could both activate and repress exon inclusion in vivo. Finally, SRSF10affected splicing genes were involved in multiple processes, indicating a role of SRSF10 in these processes. This dataset is of high quality and will facilitate future study of SRSF10 function in vivo and will also improve understanding of AS regulation in vivo.