Gresham:Research
Current Research
The Gresham lab studies the molecular basis by which cells respond to their environments. We are interested in a variety of questions that focus on the interaction between cells and their environments including how genomes evolve to optimize the match between genotype and environment, how genetic interactions are affected by the environment, and the role of post-transcriptional gene regulation in mediating responses to environmental signals. We use the budding yeast (Saccharomyces cerevisiae) as a model system and a combination of genetic and genomic methods - many of which we develop in our lab.
Adaptive Evolution
In order to study adaptive evolution we perform long-term selection experiments in chemostats using a variety of nutrient-limiting conditions. We determine the fitness of adapted strains in competitive growth assays and characterize their genomes using next generation sequencing, microarray-based whole genome single nucleotide polymorphism (SNP) detection and copy number variation (CNV) analysis. We are interested in fundamental questions about adaptive evolution and in the application of directed evolution for the selection and engineering of strains with novel properties.
1) What is the effect of different parameters on adaptive evolution in novel environments? By varying parameters such as population size and the rate of different types of mutations we are studying the factors that determine the pace of adaptation, the variety of outcomes and the evolutionary tradeoffs. By identifying which genes are mutated to confer increased fitness in specific environments we aim to identify the functional basis for optimized matches between genotype and environment.
2) How do novel metabolic pathways evolve? We investigate the means by which organisms evolve to use novel substrates by evolving strains under nutrient-limited conditions in the presence of compounds that contain the limiting agent but are not normally metabolized by yeast. We use a combination of forward genetic selections and libraries of deletion and overexpression alleles to search for new metabolic pathways. We are interested in using this approach to produce desirable metabolites or consume pollutants for bioremediation.
1. Gresham D, Desai MM, Tucker CM, Jenq HT, Pai DA, Ward A, DeSevo CG, Botstein D, and Dunham MJ. The repertoire and dynamics of evolutionary adaptations to controlled nutrient-limited environments in yeast. PLoS Genet 2008 Dec; 4(12) e1000303. doi:10.1371/journal.pgen.1000303 pmid:19079573.
2. Gresham D, Ruderfer DM, Pratt SC, Schacherer J, Dunham MJ, Botstein D, and Kruglyak L. Genome-wide detection of polymorphisms at nucleotide resolution with a single DNA microarray. Science 2006 Mar 31; 311(5769) 1932-6. doi:10.1126/science.1123726 pmid:16527929.
Genetic Interactions
One of the central challenges in genetics is to understand how genes interact to result in phenotypic variation. We study how interactions between alleles at different loci affect reproductive fitness. These genetic interactions are investigated in different environments to study the role of environmental variation in determining the outcome of genetic interactions.
1) What is the role of genetic interactions in adaptive evolution? For experimentally evolved strains we decompose the contribution of each mutation and their various combinations to fitness. We are interested in determining whether loci interact in additive or non-additive ways and the effect of the order of mutation acquisition on fitness landscapes.
2) How do genetic interactions differ in different environments? We are employing synthetic genetic array (SGA) technology to study the fitness of double deletion mutants in different environments. We use quantitative sequencing of mutant barcodes to assess the fitness of comprehensive double deletion mutants in heterogeneous pools.
3) What is the high-resolution structure of genetic interactions? In order to build high-resolution maps of genetic interactions we employ suppressor screens using conditional lethal alleles. We use forward and reverse genetic approaches to explore a large fraction of sequence space allowing us to identify both those genes (and their products) that interact and the sequence specificity of those interactions. Our ultimate aim is to infer the rules that govern the interaction and co-evolution of genes whose products physically interact.
Post-transcriptional Regulation
The fastest way to remodel global transcriptional states is by regulating the stability of transcripts that have already been synthesized. We are studying the role of post-transcriptional regulation in response to environmental signals and how signaling pathways communicate with the mRNA degradation machinery.
1) What are the genetic factors that regulate the fate of mRNAs? We have identified conditions in which the half-life of specific transcripts varies in response to specific environmental signals. Using genetic screens we are identifying the pathways that regulate the decay rates of transcripts.
2) What is the role of post-transcriptional regulation in growth-rate regulated gene expression programs? A large fraction of gene expression is differentially regulated depending on the growth-rate of the organism. We have identified trans-acting factors that regulate this expression – many of which are mRNA binding proteins. We are studying their contribution to the regulation of gene expression regulation associated with the growth of cells.
1. Airoldi EM, Huttenhower C, Gresham D, Lu C, Caudy AA, Dunham MJ, Broach JR, Botstein D, and Troyanskaya OG. Predicting cellular growth from gene expression signatures. PLoS Comput Biol 2009 Jan; 5(1) e1000257. doi:10.1371/journal.pcbi.1000257 pmid:19119411.
2. Brauer MJ, Huttenhower C, Airoldi EM, Rosenstein R, Matese JC, Gresham D, Boer VM, Troyanskaya OG, and Botstein D. Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. Mol Biol Cell 2008 Jan; 19(1) 352-67. doi:10.1091/mbc.E07-08-0779 pmid:17959824.
Cellular Quiescence
Most cells in the world – including microbes and individual cells in multicellular organims – are not actively dividing. We know very little about the molecular processes that are important for maintaining these quiescent cellular states.
1) What are the molecular pathways that mediate survival to nutrient starvation? When starved for different nutrients, cells growth is inhibited but the cells remain viable and able to rapidly resume growth when favorable conditions are restored. We are investigating the functions that are important for surviving long-term starvation states using high-throughput reverse genetics.
2) What is the physiology of environmental sensing in non-dividing cells? Typically, studies of environmental sensing and signaling pathways are performed in rapidly growing cells. We are investigating how yeast sense environmental changes that do not promote, or alter, cellular growth.
Technology Development
Technological advances frequently enable new biological insights. Previously, we have developed microarray methods for genome-wide detection of SNPs and transposons. We continue to pursue new applications for DNA microarrays and next-generation sequencing. In addition, we make extensive use of the chemostat – a method of continuous culturing – and are developing new variations of this device.
1) Microarray methods We are currently developing a DNA microarray method for simultaneously measuring the polyA tail length and abundance of transcripts.
2) Next generation sequencing methods We have developed a new method for quantitative sequencing of barcoded mutant collections enabling multiplexed genetic screens in which we can determine the fitness of all strains simultaneously. We are also developing methods for estimating SNP frequencies in heterogeneous populations from whole genome resequencing data.
1. Gresham D, Curry B, Ward A, Gordon DB, Brizuela L, Kruglyak L, and Botstein D. Optimized detection of sequence variation in heterozygous genomes using DNA microarrays with isothermal-melting probes. Proc Natl Acad Sci U S A 2010 Jan 8. doi:10.1073/pnas.0913883107 pmid:20080586.
2. Gresham D and Kruglyak L. Rise of the machines. PLoS Genet 2008 Aug 1; 4(8) e1000134. doi:10.1371/journal.pgen.1000134 pmid:18670625.
3. Gresham D, Dunham MJ, and Botstein D. Comparing whole genomes using DNA microarrays. Nat Rev Genet 2008 Apr; 9(4) 291-302. doi:10.1038/nrg2335 pmid:18347592.
4. Gabriel A, Dapprich J, Kunkel M, Gresham D, Pratt SC, and Dunham MJ. Global mapping of transposon location. PLoS Genet 2006 Dec 15; 2(12) e212. doi:10.1371/journal.pgen.0020212 pmid:17173485.
5. Gresham D, Ruderfer DM, Pratt SC, Schacherer J, Dunham MJ, Botstein D, and Kruglyak L. Genome-wide detection of polymorphisms at nucleotide resolution with a single DNA microarray. Science 2006 Mar 31; 311(5769) 1932-6. doi:10.1126/science.1123726 pmid:16527929.
