Chimeras taking shape: potential functions of proteins encoded by chimeric RNA transcripts

Chimeric RNAs comprise exons from two or more different genes and have the potential to encode novel proteins that alter cellular phenotypes. To date, numerous putative chimeric transcripts have been identified among the ESTs isolated from several organisms and using high throughput RNA sequencing. The few corresponding protein products that have been characterized mostly result from chromosomal translocations and are associated with cancer. Here, we systematically establish that some of the putative chimeric transcripts are genuinely expressed in human cells. Using high throughput RNA sequencing, mass spectrometry experimental data, and functional annotation, we studied 7424 putative human chimeric RNAs. We confirmed the expression of 175 chimeric RNAs in 16 human tissues, with an abundance varying from 0.06 to 17 RPKM (Reads Per Kilobase per Million mapped reads). We show that these chimeric RNAs are significantly more tissue-specific than non-chimeric transcripts. Moreover, we present evidence that chimeras tend to incorporate highly expressed genes. Despite the low expression level of most chimeric RNAs, we show that 12 novel chimeras are translated into proteins detectable in multiple shotgun mass spectrometry experiments. Furthermore, we confirm the expression of three novel chimeric proteins using targeted mass spectrometry. Finally, based on our functional annotation of exon organization and preserved domains, we discuss the potential features of chimeric proteins with illustrative examples and suggest that chimeras significantly exploit signal peptides and transmembrane domains, which can alter the cellular localization of cognate proteins. Taken together, these findings establish that some chimeric RNAs are translated into potentially functional proteins in humans.The work of M.F-M. is supported by the CNIO (Caja Navarra International Postdoctoral Program) and the Miguel Servet (FIS) grant. The work of V.L. is supported by the French ANR MIRI BLAN08-1335497 Project and the ERC Advanced Grant Sisyphe. This study is supported by the Spanish National Bioinformatics Institute (INB-ISCIII) and by grants from the Spanish Government (CONSOLIDER CSD2007-00050, BIO2007-666855, and BIO2011-26205), European Commission FP7 project ASSET (HEALTH-F4-2010-259348), and the NHGRI-NIH ENCODE grants (U54 HG00455-04 and U54 HG004557