Translational control of gene expression plays an essential role in diverse areas of biology, ranging from cellular stress responses to learning and memory. Despite the prevalence and importance of translational regulation, we have a limited view of the genes whose expression is affected and even less understanding of the ways in which their translation is controlled. In part, the study of translation has been limited by the relative difficulty of measuring it. I recently developed ribosome profiling as a technique to address this need for genome-wide, quantitative analysis of translation. Comprehensive and precise translational profiling has already proven its value by revealing novel expression regulation occurring in well-studied biological processes. Here, I propose to extend this exploration of translational control as an underappreciated component of cellular stress responses with direct relevance to human disease. While identifying regulated genes yields key insights into cellular physiology, it does not address directly the molecular basis of translationa control. The ultimate goal of my proposed research is to better explain the regulation of translation. Insights gained from such an understanding will impact many areas of biology, as translation is a fundamental process. They will also represent keys to enhancing or suppressing stress-induced gene expression programs in order to treat disease. I propose that translation is greatly affected by diverse mRNA-binding proteins that recognize sequence or structural elements encoded in the transcript. We now know that there are many hundreds of these mRNA-binding proteins, but their functional impact is not well understood. I will intersect global experimental maps of protein occupancy with translation profiling in order to link gene expression programs with regulatory factors and gain a better understanding of how translational regulation is specified. Finally, I propose that the mechanistic basis of translationl control can be understood through the identification and study of general coregulatory factors that are recruited to mRNAs by pathway-specific regulatory proteins. I will discover these coregulators based on their functional impact on expression and learn how they act, thereby revealing the specific molecular events that enhance or suppress translation. By intersecting expression, occupancy, and functional data sets, I will broaden our view of translational control single mRNAs to answer more generally one of the fundamental questions in how cells regulates protein abundance to control their physiology, allowing us to better understand the behaviors of healthy cells and intervene in disease.
Understanding how cells switch genes on and off is central to understanding the function of healthy cells and the dysfunction that arises in disease. Genes can be controlled by varying the amount of protein that is translated from an mRNA copy of a gene, but this is not as well understood as the regulation of transcribing those mRNA copies from the genome. We propose to find the regulatory information that controls the translation of mRNAs as well as the cellular machinery that carries out this regulation.
| McGlincy, Nicholas J; Ingolia, Nicholas T (2017) Transcriptome-wide measurement of translation by ribosome profiling. Methods 126:112-129 |
| Barry, Kevin C; Ingolia, Nicholas T; Vance, Russell E (2017) Global analysis of gene expression reveals mRNA superinduction is required for the inducible immune response to a bacterial pathogen. Elife 6: |
| Ingolia, Nicholas T (2016) Ribosome Footprint Profiling of Translation throughout the Genome. Cell 165:22-33 |
| McGeachy, Anna M; Ingolia, Nicholas T (2016) Starting too soon: upstream reading frames repress downstream translation. EMBO J 35:699-700 |
| Newman, Zachary R; Young, Janet M; Ingolia, Nicholas T et al. (2016) Differences in codon bias and GC content contribute to the balanced expression of TLR7 and TLR9. Proc Natl Acad Sci U S A 113:E1362-71 |
| Stern-Ginossar, Noam; Ingolia, Nicholas T (2015) Ribosome Profiling as a Tool to Decipher Viral Complexity. Annu Rev Virol 2:335-49 |
