We study problems in biology at the interface of molecular biology and evolution. Our research is focused on three main areas.


Genetic interactions and adaptive evolution

Genetic-interactions One of the central challenges in genetics is to understand how genes interact to result in phenotypic variation. We are studying how interactions between alleles at different loci affect reproductive fitness and the role of environmental variation in determining the outcome of genetic interactions. We study the role of genetic interactions in adaptive evolution using experimental evolution to identify the contribution of individual mutations and their various combinations to fitness. We are determining whether loci interact in additive or non-additive ways and the effect of the order of mutation acquisition on fitness landscapes. We are also studying how 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 have developed next generation sequencing method to quantify mutant barcodes allowing us to assess the fitness of double deletion mutants in heterogeneous pools.
Representative publications:

Molecular specificity, convergence and constraint shape adaptive evolution in nutrient-poor environments. Hong J, Gresham D. PLoS Genet. 2014 Jan;10(1):e1004041. doi: 10.1371/journal.pgen.1004041. Epub 2014 Jan 9. [pdf] [Pubmed] [SRA] [GEO] [Supplement]

The functional basis of adaptive evolution in chemostats.  Gresham D, Hong J.  FEMS Microbiology. 2014 Aug 6. doi: 10.1111/1574-6976.12082. [pdf] [Pubmed]


Cell growth and quiescence

Most cells – including microbes and individual cells in multicellular organisms – are not actively dividing. However, we know very little about the molecular processes that are important for maintaining this quiescent cellular state. We are investigating the molecular networks important for survival during quiescence. When starved for different nutrients, cell growth is inhibited but the cells remain viable and are able to 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.   We are also investigating how cells sense environmental signals during quiescence including signals that do not promote the reinitiation of cellular growth. Growth-Pathways
Representative publications:

Genetic and nongenetic determinants of cell growth variation assessed by high-throughput microscopy. Ziv N, Siegal ML, Gresham D. Mol Biol Evol. 2013 Dec;30(12):2568-78. [pdf] [Pubmed]

System-level analysis of genes and functions affecting survival during nutrient starvation in Saccharomyces cerevisiae. Gresham D, Boer D, Caudy A, Ziv N, Brandt NJ, Storey JD and Botstein D.  Genetics (2011), 187:299-317. [pdf] [Pubmed]


mRNA degradation

mRNA-decay-pathways The fastest way to remodel global transcriptional states is by regulating the stability of existing populations of messenger RNAs. We are studying the role of post-transcriptional regulation in response to environmental signals and how signaling pathways communicate with the mRNA degradation machinery. 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. We are also studying 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 rate at which cells are growing. 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 associated with the growth of cells.
Representative publications:

Determination of in vivo RNA kinetics using RATE-seq.  Benjy Neymotin, Rodoniki Athanasiadou, David Gresham. RNA. 2014 Oct;20(10):1645-52. doi: 10.1261/rna.045104.114. Epub 2014     Aug 26. [pdf] [Pubmed][SRA][Data and Supplemental Info][rateSeqFit]