Mapping variation in growth in response to glucose concentration Mapping natural variation determining cell growth variation within and between environments Naomi Ziv, Mark Siegal and David Gresham Center for Genomics and Systems Biology, New York University In microbial populations, growth initiation and proliferation rates are major components of fitness and therefore likely targets of selection. A more complete understanding of cell proliferation rates requires quantifying growth across genotypes both within and between environments. We used a high-throughput microscopy assay to determine the response to variation in environmental glucose in the budding yeast (Saccharomyces cerevisiae). We identified natural isolates that differ in their response to glucose concentration and others that differ in the extent of growth rate variation within an environment. We are using a combination of mapping strategies, including classic interval mapping, bulk segregant analysis of complex mixtures of genotypes under selection and successive back-crossing to identify causative loci. By resolving the genetic determinants of phenotypic variation and variability in natural populations, we can begin to understand how evolution has shaped the genotype to phenotype map across environments. Top: Unprocessed and processed image of a single field. Bottom: Images and growth profiles for two representative micro-colonies. Mapping variation in growth in response to glucose concentration Experimental method Individual yeast cells are distributed in glass-bottom 96-well plates and imaged every hour (Levy, et al., 2012, Ziv,et al., 2013). As the cells grow and divide, they form micro-colonies, consisting of the original single cell and its progeny. Growth parameters are calculated by analyzing the change in micro-colony area over time. I have identified strains that differ in their growth rate response to low glucose concentrations. The F1 phenotype demonstrates that the genetic basis of growth differs between concentrations. Furthermore, the magnitude of the difference is larger at low concentrations, as shown by both a microcolony growth rate assay and competition experiments in chemostats (right). Mapping growth rate heterogeneity and post-zygotic reproductive isolation 374 F2 segregants (genotyped at 226 markers) of a Oak/Vineyard cross (Gerke, et al., 2006) were phenotyped. QTL were identified using R/qtl (Broman, et al., 2003) (below). While phenotyping strains covering a wide range of genetic backgrounds and ecological histories. I identified strains that differ in the extent of growth rate variability despite almost identical mean growth rates (right). I have created an advanced intercross population by multiple rounds of sporulation and mating. The population was subjected to selection (imposed by growth in chemostats) and samples were sequenced over time. Many of the same QTL were identified using this method, including environment specific QTL (below). Goes to 40  Surprisingly, the two strains are reproductively isolated resulting in only 1% F2 viability. The number of viable cells per tetrad follows a Poisson distribution (right), consistent with the viability not depending on the genotype of the spore. Viable F2s still segregate growth rate heterogeneity (left). However any cross between low and high variability strains results in low spore viability Low dilution rate High dilution rate I am currently using a back-crossing strategy (right) to isolate the loci determining the spore viability and potentially the growth rate heterogeneity. The use of an advanced intercross population and whole genome sequencing improved the resolution of QTL peaks, confirming the glucose transporters HXT6 and HXT7 as potential candidates for the chromosome 4 QTL (left). I am currently phenotyping strains containing allele replacements. References: Levy, S., Ziv, N., & Siegal, M. (2012). Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant. PLoS Biology, 10(5): e1001325. Ziv, N., M. L. Siegal, and D. Gresham. (2013). Genetic and Non-Genetic Determinants of Cell-Growth Variation Assessed by High-Throughput Microscopy. Molecular Biology and Evolution, 30(12):2568-78. Gerke, J., Chen, C., & Cohen, B. (2006). Natural isolates of Saccharomyces cerevisiae display complex genetic variation in sporulation efficiency. Genetics, 174(2): 985-997. Broman K.W., Wu H., Sen Ś., Churchill G.A. (2003). R/qtl: QTL mapping in experimental crosses. Bioinformatics, 19(7): 889-890.