Coordination of gene expression programs

• Most cellular processes depend on the activity and interactions of proteins. The proteome, i.e. the entire set of proteins in a specific condition, is shaped by regulation of transcription, mRNA-degradation, -processing, -storage, -translation and protein degradation. Cancer cells are known to highjack gene expression processes, including the translation machinery, for their growth and survival. This occurs as a result of converging oncogenic signaling pathways which impinge on translation factors to selectively modulate synthesis of cancer-related proteins. Our understanding of mechanisms by which oncogenic pathways dynamically control their targets' translational activity is limited and could be extended by transcriptome-wide studies of changes in translation efficiency. In Paper I, we developed anota2seq which allows for statistical analysis of such data. Using a simulation approach, we showed that anota2seq constitutes an improvement compared to other methods for identification of genes under translational regulation. The relative contribution of transcriptional and translational regulation to proteome modulation has been extensively debated. This raises the interest in studies integrating data on multiple levels of gene expression regulation. In Paper II, we study the role of estrogen receptor alpha (ERα), a transcription factor that is commonly targeted in hormone-dependent cancers, in coordinating transcriptional alterations with control at the level of translation. Upon ERα depletion in a prostate cancer model, we observed massive translational offsetting whereby the translational output remains unchanged despite changes in mRNA levels. To characterize mechanisms underlying translational offsetting, we extended the scope of the anota2seq method (Paper I) to also identify genes regulated by this underappreciated mode of gene expression regulation. Next, our detailed mechanistic study revealed that upon ERα depletion, mRNAs whose levels are reduced but translationally offset have less structured 5'UTRs and are devoid of miRNA target sites and thus cannot be influenced by such translational repressors. In contrast, transcripts which were upregulated but offset at the level of translation are enriched in codons requiring U34-modified tRNAs for their translation. We finally demonstrated that ERα impacts the levels of such modified tRNAs. Cancer is a highly heterogeneous disease. In our studies of translational control, we are reaching the limits of reasonable inference when extending conclusions from experiments in cell lines into clinical settings. However, experimental methods to quantify translatomes such as polysome-profiling, are not suitable for samples with low RNA input such as tissue samples from cancer patients. Paper III presents an optimization of the polysome-profiling method, compares it with the classical approach and validates that this new approach is suitable to study novel mechanisms regulating mRNA translation in large collections of tissue samples.

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