Translating information from DNA to proteins is the most energetically expensive process a cell undertakes. The basic principles of translation are simple ? free-floating ribosomes bind to mRNAs and translate the transcript one codon at a time, each of which is recognized by a corresponding tRNA. In recent years, our understanding of the molecular basis of translation has improved significantly. Advances in structural biology have provided a detailed view of how an individual ribosome rests at a particular codon on an mRNA, recognizes a tRNA, makes peptide bonds, and then physically translocates to the next codon. Many factors, such as patterns of codon usage, mRNA structures, transcript abundances, protein domain-architectures, lengths of genes and untranslated regions (UTRs), and initiation and elongation rates have all been shown to modulate protein production. However, there exist two critical gaps in our understanding of dynamics and evolution of translation. First, we lack a coherent view of how all the various factors involved in translation interact with each other to regulate the global pace of protein synthesis in a cell. Second, we know little about how regulation of protein synthesis changes over time during organismal evolution and speciation. To address these critical gaps, we will develop a synthetic modeling framework for transcription and translation, and parameterize it by generating high- throughput genomic datasets. We will employ this combined modeling/experimental approach to study the dynamics and regulation of protein synthesis in a panel of model organisms and evolving populations. In the long-term, this hybrid approach will allow us to study how a cell modulates translation in different contexts, including viral infections and systemic diseases such as cancer. This framework will be particularly useful for elucidating the mechanisms of diseases that arise from synonymous mutations leading to opportunities for development of therapeutic interventions to modify protein synthesis in a targeted manner.
Synonymous mutations in genes are one of the least understood drivers of human diseases and they typically influence the dynamics of protein synthesis in cells. The proposed research will provide a deeper understanding of the mechanisms underlying protein synthesis, its regulation, and its evolution, thereby providing opportunities for development of therapeutic interventions to modify protein synthesis in a targeted manner.
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| Carja, Oana; Xing, Tongji; Wallace, Edward W J et al. (2017) riboviz: analysis and visualization of ribosome profiling datasets. BMC Bioinformatics 18:461 |
