Mass spectrometry and ribosome profiling, a perfect combination towards a more comprehensive identification strategy of true in vivo protein forms

An increasing number of studies involve integrative analysis of gene and protein expression data, taking advantage of new technologies such as next-generation transcriptome sequencing (RNA-Seq) and highly sensitive mass spectrometry (MS). Recently, a strategy, termed ribosome profiling, based on dee...

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Hauptverfasser: Crappé, Jeroen, Koch, Alexander, Steyaert, Sandra, Van Criekinge, Wim, Van Damme, Petra, Menschaert, Gerben
Format: Tagungsbericht
Sprache:eng
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Zusammenfassung:An increasing number of studies involve integrative analysis of gene and protein expression data, taking advantage of new technologies such as next-generation transcriptome sequencing (RNA-Seq) and highly sensitive mass spectrometry (MS). Recently, a strategy, termed ribosome profiling, based on deep sequencing of ribosome-protected mRNA fragments, indirectly monitoring protein synthesis, has been described. In contrast to routinely employed protein databases in proteomics searches, RIBO-seq derived data gives a more representative expression state and accounts for sequence variation information and alternative translation initiation. To verify the potential of ribosome profiling in providing us with a true snapshot of the translational landscape, we devised a proteogenomic approach generating a database of translation products based on ribosome profiling experiments. The raw and untreated RIBO-seq data is analyzed for both splice isoforms and single nucleotide polymorphisms, as such taking into account transcriptional variation. Next to that, RIBO-seq data for translation start site discovery (treated with harringtonine, lactomidomycin or puromycin) is used to obtain a genome wide blueprint of all possible translation initiation sites and as such taking into account translation variation. By adding protein-DB annotation to the genomic RIBO-seq derived data and after in silico translation a protein database is constructed reflecting the full complexity of the proteome. Using a first version of our proteogenomic approach on an undifferentiated mouse embryonic stem cell line (E14) we could demonstrate an increase of the overall protein identification rate with 2.5% as compared to only searching UniProtKB-SwissProt. Furthermore, identification of N-terminal COFRADIC data resulted in detection of 16 alternative start sites giving rise to N-terminally extended protein variants besides the identification of four translated uORFs.