Transcriptome and proteome quantification of a tumor model provides novel insights into post-transcriptional gene regulation

Genome-wide transcriptome analyses have given systems-level insights into gene regulatory networks. Due to the limited depth of quantitative proteomics, however, our understanding of post-transcriptional gene regulation and its effects on protein-complex stoichiometry are lagging behind.

Our comprehensive transcriptome and proteome data provide a valuable resource for quantitative biology and offer novel insights into understanding post-transcriptional gene regulation in a tumor model.

Here, we employ deep sequencing and the isobaric tag for relative and absolute quantification (iTRAQ) technology to determine transcript and protein expression changes of a Drosophila brain tumor model at near genome-wide resolution. In total, we quantify more than 6,200 tissue-specific proteins, corresponding to about 70% of all transcribed protein-coding genes. Using our integrated data set, we demonstrate that post-transcriptional gene regulation varies considerably with biological function and is surprisingly high for genes regulating transcription. We combine our quantitative data with protein-protein interaction data and show that post-transcriptional mechanisms significantly enhance co-regulation of protein-complex subunits beyond transcriptional co-regulation. Interestingly, our results suggest that only about 11% of the annotated Drosophila protein complexes are co-regulated in the brain. Finally, we refine the composition of some of these core protein complexes by analyzing the co-regulation of potential subunits. Conclusions: Our comprehensive transcriptome and proteome data provide a valuable resource for quantitative biology and offer novel insights into understanding post-transcriptional gene regulation in a tumor model.