DOI: 10.5061/dryad.39h5m
title: Data from: Natural variation in preparation for nutrient depletion reveals a cost-benefit tradeoff

description: Maximizing growth and survival in the face of a complex, time-varying
 environment is a common problem for single-celled organisms in the wild.
 When offered two different sugars as carbon sources, microorganisms first
 consume the preferred sugar, then undergo a transient growth delay, the
 “diauxic lag,” while inducing genes to metabolize the less preferred
 sugar. This delay is commonly assumed to be an inevitable consequence of
 selection to maximize use of the preferred sugar. Contrary to this view,
 we found that many natural isolates of Saccharomyces cerevisiae display
 short or nonexistent diauxic lags when grown in mixtures of glucose
 (preferred) and galactose. These strains induce galactose utilization
 (GAL) genes hours before glucose exhaustion, thereby “preparing” for the
 transition from glucose to galactose metabolism. The extent of preparation
 varies across strains, and seems to be determined by the steady-state
 response of GAL genes to mixtures of glucose and galactose rather than by
 induction kinetics. Although early GAL gene induction gives strains a
 competitive advantage once glucose runs out, it comes at a cost while
 glucose is still present. Costs and benefits correlate with the degree of
 preparation: strains with higher expression of GAL genes prior to glucose
 exhaustion experience a larger upfront growth cost but also a shorter
 diauxic lag. Our results show that classical diauxic growth is only one
 extreme on a continuum of growth strategies constrained by a cost–benefit
 tradeoff. This type of continuum is likely to be common in nature, as
 similar tradeoffs can arise whenever cells evolve to use mixtures of
 nutrients.
description: Dataset
 1GROWTH CURVES (Figure 1,
 S1-3, S11A-B; Also used in figure 3, 4, 7C). Contains .csv files of raw
 OD600 readings from plate reader.Dataset
 2DIAUXIC GROWTH
 TIMECOURSE ON BC187 AND YJM978 (Figure 2, 5A-B, S4B-D, S5, S7C, S11C; Also
 used in figure 5C, S7B). Contains .fcs files of raw flow cytometry data
 and .csv files of sugar concentrations.Dataset
 3DIAUXIC GROWTH
 TIMECOURSE ON MULTIPLE STRAINS (Figure 3, S7A-B,D-F,I, S9A). Contains .fcs
 files of raw flow cytometry data.Dataset_3.zipDataset 4MEDIUM SHIFT AND STEADY-STATE EXPRESSION (Figure 4, S6; Also used in figure S9A, S12). Contains .fcs files of raw flow cytometry data.Dataset 5STEADY-STATE FITNESS OF BC187 AND YJM978 (Figure 5C, S12). Contains .fcs files of raw flow cytometry data.Dataset 6SYNTHETIC INDUCTION (Figure 6). Contains .fcs files of raw flow cytometry data.Dataset 7STEADY-STATE FITNESS OF MULTIPLE STRAINS (Figure 7, S14). Contains .fcs files of raw flow cytometry data.Datasets 8-10Dataset 8. ABSOLUTE COUNTING CONTROL (Figure S4A). Contains .fcs files of raw flow cytometry data. Dataset 9. LONG TIMECOURSE INDUCTION (Figure S8, S9B). Contains .fcs files of raw flow cytometry data. Dataset 10. PRE-CONDITION EFFECT ON INDUCTION KINETICS (Figure S10). Contains .fcs files of raw flow cytometry data.Dataset 8-10.zipDataset 11TIMELAPSE MICROSCOPY (Figure S13). Contains .tiff files of raw microscopy data.Dataset_11.zipMATLAB analysis codeMATLAB scripts for reproducing the analysis and generating all the figures in the paper. Scripts are organized into subfolders according to which dataset they are meant to analyze. See README.txt for entire repository for details and usage instructions.