Degradation of mRNA is a fundamental process and knowledge about its mechanisms serves to better understand protein synthesis and gene expression regulation. The mechanistic detail of how RNA to be degraded is recognized and which are the factors involved in the process, remains to be clarified. Preliminary and recently published results allow us to ask new fundamental questions about the mechanisms of NMD (non-sense mediated mRNA decay):
- What is the composition of NMD-related protein-RNA complexes?
- How is the degradation system recruited to mRNA substrates?
- How enzymes that degrade NMD substrates are activated?
Predict cellular processes sensitive to NMD defects?
To answer these questions we use affinity purification of RNA-protein and protein-protein complexes coupled with mass-spectrometry based quantitative estimation of protein components and RNASeq for RNA components, large scale genetic screens and classical phenotypic analysis of individual yeast mutant strains.
Our expertise in chemogenomic screens is useful in collaborative studies where the mechanism of action of a toxic compound is sought. Large-scale data analysis and visualization also pushed us to set up interactive graphical views for genetic interactions and protein-protein association results.
Our most recent results on NMD, initially submited to the pre-print server biorxiv (https://www.biorxiv.org/content/early/2018/02/16/266833), are now published. They show that NMD factors associate in two mutually exclusive complexes around the RNA helicase Upf1. Our results led to the characterization of new direct partners of Upf1 in yeast, the Nmd4 and Ebs1 proteins, that are potential equivalents of human Smg6 and Smg5/7. How these RNA helicase partners affect RNA decay during NMD, what exactly happens on RNA substrates and to what extent these mechanisms are conserved in human cells are questions we are currently addressing.
The complexity of RNA metabolism pathways require both biochemical and genetic tools for their investigation. We performed several hundred thousands measures for growth of strains in which two genes were affected. Since genes involved in the same pathway tend to respond similarly to the addition of a second mutation, these genetic interaction screens can be used to identify new factors involved in RNA metabolism. We developed a publication-based method to be able to infer function from genetic interaction profile similarity. An example of the large panel of cellular functions that were explored by our genetic screens in shown below. The pre-print is now (Oct 2020) available on biorxiv.