A comprehensive strategy enabling high-resolution functional analysis of the yeast genome

Abstract

Functional genomic studies in Saccharomyces cerevisiae have contributed enormously to our understanding of cellular processes. Their full potential, however, has been hampered by the limited availability of reagents to systematically study essential genes and the inability to quantify the small effects of most gene deletions on growth. Here we describe the construction of a library of hypomorphic alleles of essential genes and a high-throughput growth competition assay to measure fitness with unprecedented sensitivity. These tools dramatically increase the breadth and precision with which quantitative genetic analysis can be performed in yeast. We illustrate the value of these approaches by using genetic interactions to reveal new relationships between chromatin-modifying factors and to create a functional map of the proteasome. Finally, by measuring the fitness of strains in the yeast deletion library, we addressed an enigma regarding the apparent prevalence of gene dispensability and found that most genes do contribute to growth.

Figure 1

A library of hypomorphic alleles of essential yeast genes constructed using the DAmP approach. (a) Schematic of the strategy used to construct DAmP alleles. The kanamycin-resistance (Kan^R) cassette was inserted immediately after the open reading frame of each gene by transformation with a PCR product encoding the Kan^R cassette flanked at each end with homology to the targeted locus. (b) Number of DAmP diploid and haploid strains obtained. Of the 1,033 essential yeast genes, ~970 were obtained in diploid form and 842 as MATa haploids. (c) Schematic of the generation of DAmP strains and of degron-DAmP strains from existing strains with TAP tags by high-efficiency transformation with either of two universal cassettes. Deg, degron. (d) TAP-degron-DAmP alleles typically reduce protein abundance more than the DAmP allele alone as determined by western blots using an antibody to the TAP tag. To normalize for variation in loading, 3-phosphoglycerate kinase (Pgk1) was probed.

Figure 2

A flow cytometry–based technique for high-precision growth rate measurements. (a) Schematic of the assay. GFP-labeled mutant strains were competed with an RFP-labeled wild-type strain in 384-well format with cultures maintained by serial dilution over a 72-h time course. At each time point, samples were removed for analysis by flow cytometry to determine the ratio of GFP-positive to RFP-positive cells. (b) Wild-type and mutant cell populations can be resolved by flow cytometry. Rare events that appear to be GFP-positive and RFP-positive represent instances in which a mutant cell and a wild-type cell are misidentified by the cytometer as a single cell, and we took this into account during analysis. (c) Representative growth rate data obtained by flow cytometry. Relative growth rates spanning ~0.6 (sick) to 1.0 (wild-type growth) were observed with a high degree of linearity in the rate of mutant strain depletion over time. Dashed gray line indicates the approximate limit of sensitivity.

Figure 3

Growth rate measurements of deletion and DAmP yeast strains by the flow cytometry–based technique. (a) Correspondence between replicate measurements of relative growth rates for 344 deletion and DAmP strains. (b) Error as a function of the relative growth rate observed. For groups of 30 strains with similar growth rates, median fitness and median s.d. of triplicate measurements are shown. Inset, distribution of s.d. of triplicate measurements for all strains measured; the median s.d. was 0.0038. (c) Distribution predicted from experimental error and observed distribution of relative growth rates for 835 DAmP strains, with the inset showing the cumulative distribution of growth rates. (d) Distribution predicted from experimental error and observed distribution of relative growth rates for 4,204 deletion strains, with the inset showing the cumulative distribution of growth rates.

Figure 4

Utility of DAmP strains for identification of drug targets. (a) Sensitivity of DAmP alleles of known targets to the indicated drugs. Zones of growth inhibition surrounding drug-soaked discs were measured to determine the relative drug sensitivity of the indicated strains. (b) Distribution of growth defects for DAmP library strains grown in medium containing 0.25 μg/ml tunicamycin. Data for the strain bearing a DAmP allele of ALG7 is in red. Additional sensitive strains were also detected (dashed box; see Supplementary Data 2).

Figure 5

Highly sensitive growth rate measurements enable resolution of previously inaccessible classes of genetic interactions. (a) Schematic illustrating normalization of genetic interaction scores for an example case involving two genes, A and B. The bar graph indicates relative growth rates for the indicated strains, with the double-gene deletion growth rate shown to be equal to that expected for the case of no genetic interaction (left). For possible observed double-gene deletion growth rates, raw and normalized interaction scores are shown, along with the corresponding classes of alleviating interactions and a colorbar used for graphical representation of normalized interaction scores (right). (b) Comparison of genetic interactions observed between members of the COG complex when using fitnesses measured by flow cytometry (lower left) versus colony size (upper right; data from reference 2). Values shown for interactions calculated from flow cytometry measurements are the raw genetic interaction score (ε; black) and the normalized interaction score (ε_NORM; red). Relative growth rates for the single-gene deletion strains are indicated in blue. (c) Growth rates measured by flow cytometry for strains with deletions of HTZ1 and/or genes encoding members of the Swr-C. Error bars indicate s.d., n ≥ 3. (d) Genetic interactions between genes encoding members of Rpd3C(S) and Rpd3C(L). The organization of these complexes is shown schematically at right (note that both complexes also contain additional members). Question marks indicate additional functions for Set2 and Rpd3C(S) suggested by genetic interaction data. Raw interaction scores (upper right) and normalized interaction scores (lower left) were calculated as in a. Note that synthetic (ε < 0) interactions are normalized using a different scale (see Supplementary Methods).

Figure 6

Functional dissection of the 26S proteasome. (a) Schematic representation of the 26S proteasome. (b) Map of raw genetic interaction scores for the 26S proteasome. Interactions between pairs of genes encoding components of the same physical subcomplex are indicated by red boxes. (c) Map of normalized interaction scores for the 26S proteasome. In the lower panel, double-mutants were grouped according to the subcomplex(es) with mutations, with the median score for each group shown (*, data for PRE9 and PRE7 were excluded when calculating the medians). Individual scores for interactions with the α ring subunits are shown at the top. Note that synthetic interactions (ε < 0) are normalized using a different procedure (see Supplementary Methods).