Audrey P. Gasch

Bio: Audrey P. Gasch is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Saccharomyces cerevisiae & Gene. The author has an hindex of 41, co-authored 98 publications receiving 11188 citations. Previous affiliations of Audrey P. Gasch include Lawrence Berkeley National Laboratory & Wisconsin Alumni Research Foundation.

Genomic expression programs in the response of yeast cells to environmental changes.

Abstract: We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (approximately 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.

Genomic expression responses to DNA-damaging agents and the regulatory role of the yeast ATR homolog Mec1p.

Abstract: Eukaryotic cells respond to DNA damage by arresting the cell cycle and modulating gene expression to ensure efficient DNA repair. The human ATR kinase and its homolog in yeast, MEC1, play central r...

Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering

Abstract: Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. Many proteins serve different roles depending on the demands of the organism, and therefore the corresponding genes are often coexpressed with different groups of genes under different situations. This poses a challenge in analyzing whole-genome expression data, because many genes will be similarly expressed to multiple, distinct groups of genes. Because most commonly used analytical methods cannot appropriately represent these relationships, the connections between conditionally coregulated genes are often missed. We used a heuristically modified version of fuzzy k-means clustering to identify overlapping clusters of yeast genes based on published gene-expression data following the response of yeast cells to environmental changes. We have validated the method by identifying groups of functionally related and coregulated genes, and in the process we have uncovered new correlations between yeast genes and between the experimental conditions based on similarities in gene-expression patterns. To investigate the regulation of gene expression, we correlated the clusters with known transcription factor binding sites present in the genes' promoters. These results give insights into the mechanism of the regulation of gene expression in yeast cells responding to environmental changes. Fuzzy k-means clustering is a useful analytical tool for extracting biological insights from gene-expression data. Our analysis presented here suggests that a prevalent theme in the regulation of yeast gene expression is the condition-specific coregulation of overlapping sets of genes.

The genomics of yeast responses to environmental stress and starvation.

Abstract: Unicellular organisms such as yeast have evolved to survive constant fluctuations in their external surroundings by rapidly adapting their internal systems to meet the challenges of each new environment. One aspect of this cellular adaptation is the reorganization of genomic expression to the program required for growth in each environment. The reprogramming of genomic expression can be unveiled using DNA microarrays, which measure the relative transcript abundance of essentially every gene in an organism's genome. Characterizing environmentally triggered gene expression changes provides insights into when, where, and how each gene is expressed and offers a glimpse at the physiological response of the cells to changes in their surroundings. This review will focus on the genomic expression responses of the budding yeast Saccharomyces cerevisiae to diverse environmental changes, highlighting some of the themes that have emerged from the collection of published yeast genomic expression studies. The results of these studies present insights as to how yeast cells sense and respond to each new environment, and suggest mechanisms that this organism uses to survive stressful environmental changes.

REACTIVE OXYGEN SPECIES: Metabolism, Oxidative Stress, and Signal Transduction

Abstract: Several reactive oxygen species (ROS) are continuously produced in plants as byproducts of aerobic metabolism. Depending on the nature of the ROS species, some are highly toxic and rapidly detoxified by various cellular enzymatic and nonenzymatic mechanisms. Whereas plants are surfeited with mechanisms to combat increased ROS levels during abiotic stress conditions, in other circumstances plants appear to purposefully generate ROS as signaling molecules to control various processes including pathogen defense, programmed cell death, and stomatal behavior. This review describes the mechanisms of ROS generation and removal in plants during development and under biotic and abiotic stress conditions. New insights into the complexity and roles that ROS play in plants have come from genetic analyses of ROS detoxifying and signaling mutants. Considering recent ROS-induced genome-wide expression analyses, the possible functions and mechanisms for ROS sensing and signaling in plants are compared with those in animals and yeast.

Missing value estimation methods for DNA microarrays.

Abstract: Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data. Results: We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1–20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions. Availability: The software is available at http://smi-web.

Insights into the regulation of protein abundance from proteomic and transcriptomic analyses

Abstract: Recent advances in next-generation DNA sequencing and proteomics provide an unprecedented ability to survey mRNA and protein abundances. Such proteome-wide surveys are illuminating the extent to which different aspects of gene expression help to regulate cellular protein abundances. Current data demonstrate a substantial role for regulatory processes occurring after mRNA is made - that is, post-transcriptional, translational and protein degradation regulation - in controlling steady-state protein abundances. Intriguing observations are also emerging in relation to cells following perturbation, single-cell studies and the apparent evolutionary conservation of protein and mRNA abundances. Here, we summarize current understanding of the major factors regulating protein expression.

Transcriptional Regulatory Networks in Saccharomyces cerevisiae

Abstract: We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiaeassociate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.

ROS as signalling molecules: mechanisms that generate specificity in ROS homeostasis

Abstract: Reactive oxygen species (ROS) have been shown to be toxic but also function as signalling molecules. This biological paradox underlies mechanisms that are important for the integrity and fitness of living organisms and their ageing. The pathways that regulate ROS homeostasis are crucial for mitigating the toxicity of ROS and provide strong evidence about specificity in ROS signalling. By taking advantage of the chemistry of ROS, highly specific mechanisms have evolved that form the basis of oxidant scavenging and ROS signalling systems.