During protein biosynthesis, RNA transcripts provide the necessary instructions to build functional proteins. RNA editing and alternate splicing lead to the formation of protein isoforms and contribute to protein diversity. As a unique strategy for investigating gene expression, Wu et al. (2014) have integrated transcriptomics and proteomics techniques along with a novel bioinformatics workflow to identify 150 new peptides, 72 novel splicing isoforms, 43 new genetic regions and 15 RNA editing sites in the mouse liver.1
The research team based their experimental design on a “multi-omics” approach they had developed previously.2 For their experiments, they used a combination of mass spectrometry and RNA-Seq to analyze liver tissue from three C57BL/6 male mice (8−10 weeks old). The team sacrificed the mice and excised liver tissue, subsequently pooling and dividing tissue samples into two parts.
Wu et al. used RNA extraction kits to isolate the total RNA and then performed sequencing using a genome analyzer. Next, they mapped RNA data to the Ensembl mouse genome database and obtained approximately 38 million paired-end clean reads. Dividing the transcripts into four categories, the team identified “known transcripts”, “novel splice variants”, “unknown transcripts” (corresponding to new coding regions) and “ambiguous transcripts” containing potential artificial assemblies.
For the proteomic analysis, the team extracted and fractionated proteins prior to high-performance liquid chromatography and tandem mass spectrometry (HPLC−MS/MS) using an EASY-nLC 1000 liquid chromatograph coupled to an LTQ Orbitrap Velos hybrid ion trap-Orbitrap mass spectrometer (both from Thermo Scientific).
Using the RNA-Seq data as a template for database searches, the team developed a bioinformatics workflow that was able to incorporate newly identified splicing-derived peptides and peptide variants caused by RNA editing.
The research group then randomly selected 11 novel events to analyze using PCR and Sanger sequencing. The results of this verification proved that this combination “-omics” approach yields high-confidence data describing alternative splicing isoforms, novel genetic regions, and RNA editing sites.
Wu and colleagues suggest this approach using an integration of “-omics” is ideal for understanding tissue- or cell-specific functions. They also consider this method optimal for large-scale studies such as the Chromosome-centric Human Proteome Project. For future research interests, the authors have added the RNA-Seq data from this publication to the Short Read Archive, under study accession number SRP033468. Likewise, the MS data are also retrievable in the iProX, with the identifier IPX00003601.
1. Wu, P., et al. (2014) “Discovery of novel genes and gene isoforms by integrating transcriptomic and proteomic profiling from mouse liver,” Journal of Proteome Research, 13(5) (pp. 2409–19), doi: 10.1021/pr4012206.
2. Chang, C., et al. (2014) “Systematic analyses of the transcriptome, translatome, and proteome provide a global view and potential strategy for the C-HPP,” Journal of Proteome Research, 13(1) (pp. 38−49).
Post Author: Emily Humphreys. Emily has previous research experience in eye development, infectious diseases, and aging. While she enjoyed the thrill of research, She has since traded bench work for science journalism. Emily has been a regular contributor to Accelerating Science since 2012.