Chemical-Genetic Interactions with the Proline Analog L-Azetidine-2-Carboxylic Acid in Saccharomyces cerevisiae

Abstract

Non-proteinogenic amino acids, such as the proline analog L-azetidine-2-carboxylic acid (AZC), are detrimental to cells because they are mis-incorporated into proteins and lead to proteotoxic stress. Our goal was to identify genes that show chemical-genetic interactions with AZC in Saccharomyces cerevisiae and thus also potentially define the pathways cells use to cope with amino acid mis-incorporation. Screening the yeast deletion and temperature sensitive collections, we found 72 alleles with negative chemical-genetic interactions with AZC treatment and 12 alleles that suppress AZC toxicity. Many of the genes with negative chemical-genetic interactions are involved in protein quality control pathways through the proteasome. Genes involved in actin cytoskeleton organization and endocytosis also had negative chemical-genetic interactions with AZC. Related to this, the number of actin patches per cell increases upon AZC treatment. Many of the same cellular processes were identified to have interactions with proteotoxic stress caused by two other amino acid analogs, canavanine and thialysine, or a mistranslating tRNA variant that mis-incorporates serine at proline codons. Alleles that suppressed AZC-induced toxicity functioned through the amino acid sensing TOR pathway or controlled amino acid permeases required for AZC uptake. Further suggesting the potential of genetic changes to influence the cellular response to proteotoxic stress, overexpressing many of the genes that had a negative chemical-genetic interaction with AZC suppressed AZC toxicity.

Keywords: L-azetidine-2-carboxylic acid; Saccharomyces cerevisiae; actin cytoskeleton; mistranslation; protein quality control; proteotoxic stress.

Figure 1

A screen identifying genes that have chemical-genetic interactions with AZC. (A) Overview of the screening approach. (B) Distribution of Z-score normalized fitness values for 5307 strains representing 4970 genes from the deletion and temperature sensitive yeast collections grown on medium containing 30 µg/mL AZC.

Figure 2

Negative chemical-genetic interactions with AZC. (A) Alleles that have negative chemical-genetic interactions with AZC are arranged according to their predicted function. (B) Significantly enriched GO biological processes were determined from the set of genes with negative chemical-genetic interactions with AZC. The number of genes identified corresponding to each GO term and their P-values are labeled on each bar. (C) SAFE analysis of genes that have negative chemical-genetic interactions with AZC were mapped onto the yeast genetic interaction profile map (Costanzo et al. 2016). Yellow dots represent genes within the local neighborhood of genes validated to have negative chemical-genetic interactions with AZC. Bold terms represent network regions that are enriched.

Figure 3

Overexpressing genes with negative chemical-genetic interactions with AZC can suppress AZC induced toxicity. Fifty-three of the genes having negative chemical-genetic interactions with AZC were obtained from the FLEX collection (Hu et al. 2007; Douglas et al. 2012). Overexpression strains and a control strain containing an empty plasmid were grown in medium lacking uracil and containing raffinose, diluted to an OD₆₀₀ of 0.1 in medium lacking uracil containing galactose with or without 10 µg/mL AZC. Cells were grown for 24 hr at 30° and OD₆₀₀ measured every 15 min. (A) Representative growth curves of the control strain, six strains that suppressed AZC toxicity (MCD4, MAK3, PUP2, RVS167, HSF1 and LAS17) and two strains that did not suppress (XRN1 and PRE4). (B) Area under the curve was calculated with the R package growthcurver (Sprouffske and Wagner 2016). Genes colored blue were considered to be suppressors of AZC toxicity when overexpressed and have a ratio of AUC plus/minus AZC twofold or greater than that of the control strain (P ≤ 0.05).

Figure 4

Negative chemical-genetic interactions with canavanine and thialysine for genes identified to have negative chemical-genetic interactions with AZC. (A) Venn diagram showing the overlap of genes with negative chemical-genetic interactions between AZC, canavanine and thialysine. (B) Alleles that were identified as having negative chemical-genetic interactions with AZC were tested for their chemical-genetic interactions with thialysine (blue) and canavanine (red) in liquid growth assays.

Figure 5

Negative genetic interactions between genes with negative chemical-genetic interactions with AZC and a tRNA that mistranslates serine at proline codons. Alleles that were identified as having negative chemical-genetic interactions with AZC were mated with a strain containing either a wild type tRNA^Ser or tRNA^Ser_{UGG, G26A} which mistranslates serine at proline codons. Double mutants were selected using the SGA method described in Tong et al. (2001). Negative genetic interactions (orange) were determined by comparing double mutant fitness of the strains with tRNA^Ser to the strains with tRNA^Ser_{UGG, G26A} in biological triplicate using SGAtools (Wagih et al. 2013) and a custom R script (supplemental file 1).

Figure 6

AZC treatment results in increased actin patches. (A) Cells were grown in various concentrations of AZC to an OD₆₀₀ of 0.1, fixed, stained with fluorescein isothiocyanate-ConA, rhodamine-phalloidin, and 4’,6-diamidino-2-phenylindole as described in Ohya et al. (2005) and imaged with 100x magnification on a Zeiss Axio Imager Z1 Fluorescent microscope. Number of actin patches per cell was quantified using CalMorph (Ohya et al. 2005). In each condition, at least 270 cells were quantified. All conditions were statistically different from each other (P ≤ 0.05; Welch’s t-test) with the exception of the comparison between 2.5 µg/mL and 6 µg/mL AZC. (B) Representative images of cells quantified in (A).

Figure 7

Genes that have positive chemical-genetic interactions with AZC. (A) Network of genes with positive chemical-genetic interactions with AZC. Genetic and physical interaction networks were generated using GeneMANIA. Nodes represent gene/proteins, green edges represent genes that have similar genetic interaction profiles (Costanzo et al. 2010, 2016) and blue edges represent proteins that physically interact. Dark purple indicates genes that are part of the EGO complex. (B) Significantly enriched GO biological processes were determined from the set of genes that have positive chemical-genetic interactions. The number of genes corresponding to each GO term and their P-values are labeled on each bar. (C) Genes with a chemical-genetic interaction with canavanine are shown on the network of genes having positive chemical-genetic interactions with AZC. Chemical-genetic interactions with canavanine were determined by growth in liquid media. Positive chemical-genetic interactions with canavanine are colored purple. (D) Genes with chemical-genetic interactions with thialysine are shown on the network of genes having positive chemical-genetic interactions with AZC. Chemical-genetic interactions with thialysine were determined by growth in liquid media. Strains with a positive chemical-genetic interaction with thialysine toxicity are colored purple; strains with a negative chemical-genetic interaction are colored yellow.