Friday, November 22, 2024
11/22/2024
PROJECT SUMMARY Codons and tRNAs are key components of the central dogma, and changes in the abundance of either can lead to significant cellular changes. While perturbations of the balance between codon usage and tRNA have been explored, little is known about how natural systems harness this potential regulatory system. This is a gap in our understanding of basic cellular biology, and there is the potential for erroneous evolutionary conclusions based on methods that assume neutrality in synonymous changes. The work proposed here will advance our understanding of the extrinsic factors shaping this phenomenon and the dynamics occurring within the cell. The budding yeast subphylum (Saccharomycotina) is an ideal model for studying tRNA and codon dynamics. Our recent collaboration has produced draft genomes, annotations, and a phylogeny for 1,154 budding yeasts representing >400 million years of evolutionary history. We also have phenotypic growth curves for over 800 species. Additionally, the budding yeasts have long been a model system for cellular biology and there are many genomic tools available for experimentation. This biological treasure trove has plentiful data for modern machine-learning methods and is genetically tractable for extensive experimentation. The first aim will unravel the association between evolutionary rate, growth rate, and expression level through the lens of codon usage. The current paradigm is that an increased growth rate is accompanied by increased amino acid and potentially synonymous mutation rates. These traits, however, both impact gene expression levels. The budding yeast subphylum provides a unique opportunity to simultaneously examine the evolution of these three traits as they vary widely across the group. The computational models will be accompanied by experimental work that modulates growth rate by altering transcriptional codon usage and the tRNA pool. The second aim will explore how the codon bias of transcribed genes is tuned to or by the tRNA pool. First, we will test the hypothesis that co-regulated genes have coordinated codon usage profiles using machine learning methods. Novel co-regulatory hypotheses generated will be experimentally verified. This work will be complemented by a large sequencing endeavor to produce tRNA, mRNA, and ribosomal sequencing data to capture transcriptional codon usage bias fully. This unique dataset will allow us to address the hypothesis that the transcriptional codon usage landscape is dynamic during regulatory changes. The LaBella lab group is ideally poised to address these critical hypotheses about the dynamics and impact of tRNA and codon biases. Our expertise in the fields of codon usage analysis and tRNA biology complements our skills in machine learning and fungal experimentation. These proficiencies will allow us to test diverse hypotheses on codon usage and tRNA dynamics. This research will impact many other fields that leverage evolutionary rates, require heterologous protein expression, or try to identify regulatory schemes.
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