1. Quantitative analysis of genetic and environmental factors determining variation in cell growth
Naomi Ziv, Mark Siegal and David Gresham
Center for Genomics and Systems Biology, New York University
Experimental method
Motivation
Complex networks of genetic and environmental factors regulate the initiation of cell growth and the subsequent rates of proliferation. A major determinant of growth rate in the budding yeast (Saccharomyces cerevisiae) is carbon availability. How a cell tunes its rate of growth in response to carbon availability is poorly understood. Moreover, the extent of inter-individual variation in growth rate and how this variation is affected by genetic or environmental factors has not been extensively studied. Using a novel growth phenotyping assay, I investigate within and between genotype variations in the cellular response to different nutrient concentrations. In particular, I aim to determine the molecular components that are important for this response and how have they been modified throughout evolution.
Individual yeast cells are distributed in glass-bottom 96-well plates and imaged every hour. As the cells grow and divide, they form micro-colonies, consisting of the original single cell and its progeny. Custom image analysis algorithms (Levy, et al., 2012) are used to process the images and compute the area of each micro-colony. Growth parameters for ~50,000, individual micro-colonies are calculated by analyzing the change in area over time.
Maximal growth rate is calculated as the highest slope of a linear regression that has a R2 of at least 0.9, Lag duration is defined by the intersection with a horizontal line defined by the initial cell size.
Growth rate
Lag duration
Initial
cell size
Growth rate, glucose and genetic variation
We phenotyped four prototrophic diploid strains under 8 glucose limitation conditions, resulting in 154,085 micro-colony growth rates. Data was analyzed using a mixed effect model. Subsequently, measurements were transformed by subtracting corresponding plate and well conditional mean estimates.
Monod (1949) proposed that growth rate is related to the concentration of an essential nutrient with saturating kinetics resembling the Michaelis-Menten equation. Using non-linear least-squares regression, data for each strain was fit to the Monod model for substrate-limited growth.  
A single micro-colony - Top
Representative image.
Unprocessed image - Left
Processed image – Right
Genetic basis – QTL mapping
374 F2 segregants (genotyped at 226 markers) of a Oak/Vineyard cross (Gerke, et al., 2006) were phenotyped at 4.44 mM glucose (proxy for μmax) and 0.2 mM glucose (proxy for KS). Growth rates were normalized according to parental phenotypes to remove plate effects.
We have identified QTL using R/qtl (Broman, et al., 2003).
Same mean, different deviation
Growth rate, lag duration and respiration
Despite identical means, two European strains isolated from soil show reproducible differences in the extent of growth rate variation.
We phenotyped additional strains covering a wide range of genetic backgrounds and ecological histories. Lag duration negatively correlates with low growth rates (<0.3). Strains differ in lag despite similar growth rates.
Cellular respiration, measured by CIT1 (Citrate synthase) expression, negatively correlates with growth rate at low glucose concentrations.  
References:
Monod, J. (1949). The Growth of Bacterial Cultures. Annual Review of Microbiology, 3:
Levy, S., Ziv, N., & Siegal, M. (2012). Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant. PLoS Biology, 10(5): e
Gerke, J., Chen, C., & Cohen, B. (2006). Natural isolates of Saccharomyces cerevisiae display complex genetic variation in sporulation efficiency. Genetics, 174(2):
Broman K.W., Wu H., Sen Ś., Churchill G.A. (2003). R/qtl: QTL mapping in experimental crosses. Bioinformatics, 19(7):