Minimization of Biosynthetic Costs in Adaptive Gene Expression Responses of Yeast to Environmental Changes

Yeast successfully adapts to an environmental stress by altering physiology and fine-tuning metabolism. This fine-tuning is achieved through regulation of both gene expression and protein activity, and it is shaped by various physiological requirements. Such requirements impose a sustained evolutionary pressure that ultimately selects a specific gene expression profile, generating a suitable adaptive response to each environmental change. Although some of the requirements are stress specific, it is likely that others are common to various situations. We hypothesize that an evolutionary pressure for minimizing biosynthetic costs might have left signatures in the physicochemical properties of proteins whose gene expression is fine-tuned during adaptive responses. To test this hypothesis we analyze existing yeast transcriptomic data for such responses and investigate how several properties of proteins correlate to changes in gene expression. Our results reveal signatures that are consistent with a selective pressure for economy in protein synthesis during adaptive response of yeast to various types of stress. These signatures differentiate two groups of adaptive responses with respect to how cells manage expenditure in protein biosynthesis. In one group, significant trends towards downregulation of large proteins and upregulation of small ones are observed. In the other group we find no such trends. These results are consistent with resource limitation being important in the evolution of the first group of stress responses

Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell population's growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a “sweet spot” of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings

Impact of high sugar content on metabolism and physiology of indigenous yeasts

This PhD project is part of an ARC Training Centre for Innovative Wine Production larger initiative to tackle the main challenges for the Australian wine industry. In particular, the aim is to address the implication of the increasing trend of sugar accumulation in ripe grapes that consequently results in high sugar musts and high ethanol wines. These increase the risk of sluggish and stuck fermentation, especially when only the indigenous microflora of yeast is exploited. At the beginning of fermentation, yeast cells must coordinate genome expression rapidly in response to external changes to maintain competitive fitness and cell survival. Understanding how cells modulate their adaptation strategies can be the key to predicting their capacity to survive in a harsh environment and consequently be able to influence wine composition. This project aims to give strategic advice to deal with fermentations by studying non-conventional yeast physiology in response to high sugar must and correlating it with growth and metabolism. Chapter 2 compares T. delbrueckii and S. cerevisiae oenological traits at a molecular level. The mechanisms behind the metabolic differences that exist between these two species were inspected using Next Generation Sequencing technology (ILLUMINA) and analysed by assembling RNA transcriptomes. In Chapter 3 two Australian indigenous yeast species genomes were sequenced with the newest Next Generation Sequencing (NGS) technology, Nanopore MinION. Chapter 4 further analyzed the global short-term stress adaptive response to grape must, implementing the technique previously used. The results, discussed in Chapter 5, summarize the improvements in high-throughput data analysis and reveal the genomic and physiological differences of these wine-related species.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 201

Investigation of the length distributions of coding and noncoding sequences in relation to gene architecture, function, and expression

The last 20 years has seen the birth of bioinformatics, and is defined as the combination of mathematics, biology, and computational approaches. This discipline has led to the era of ontology, extensive databases including sequences, structures, expression profiles, and genomes and database cross-referencing, (Ouzounis, 2012). Before this discipline, scientists referenced atlas books, such as Margret Dayhoff’s protein sequence collection (Strasser, 2010) which required long hours of letter counting. Through the development of sequencing technology over the past forty years, a tremendous amount of genomic sequencing data has already been collected. With a surge of such data increasing, so does the challenges of data organisation, accessibility and interpretation, with interpretation being the most challenging (Ouzounis, 2012)