Suboptimal Global Transcriptional Response Increases the Harmful Effects of Loss-of-Function Mutations

Abstract

The fitness impact of loss-of-function mutations is generally assumed to reflect the loss of specific molecular functions associated with the perturbed gene. Here, we propose that rewiring of the transcriptome upon deleterious gene inactivation is frequently nonspecific and mimics stereotypic responses to external environmental change. Consequently, transcriptional response to gene deletion could be suboptimal and incur an extra fitness cost. Analysis of the transcriptomes of ∼1,500 single-gene deletion Saccharomyces cerevisiae strains supported this scenario. First, most transcriptomic changes are not specific to the deleted gene but are rather triggered by perturbations in functionally diverse genes. Second, gene deletions that alter the expression of dosage-sensitive genes are especially harmful. Third, by elevating the expression level of downregulated genes, we could experimentally mitigate the fitness defect of gene deletions. Our work shows that rewiring of genomic expression upon gene inactivation shapes the harmful effects of mutations.

Keywords: fitness effect of mutations; gene deletion; gene expression regulation; genotype–phenotype map.

Fig. 1.

Extent of transcriptome changes upon gene deletions. (A) Distribution of the number of transcript level changes across the ∼1,500 single-gene deletion strains. A transcript level change was defined by fold-change (FC > 1.7) and statistical significance thresholds (P < 0.05), following Kemmeren et al. (2014). The inset shows number of transcript level changes for the subset of gene deletions causing ≥5% growth defect. (B) Growth rate is highly correlated with the number of changing transcripts (Spearman’s ρ = −0.49, P < 10⁻¹⁵). Blue line indicates a loess curve fitted on log(number of transcript level changes + 1), with parameters span = 1, degree = 2. Relative growth rate of deletion strains was obtained from O’Duibhir et al. (2014).

Fig. 2.

A handful of TRMs explain most gene expression responses to knockouts. (A) Overview of the approach to estimate the number of significant independent TRMs. PCA is performed over the original expression data set, as well as over randomized instances of it (Materials and Methods). The threshold for estimating the number of TRMs is set as the mean + 2 SD in the variance explained by TRM #1 in 600 randomized data sets. (B) Results obtained on the transcriptomes of ∼1,500 gene deletion strains, in which we estimate that each one of the first 15 TRMs explains more variance than expected by chance. Results are shown for the first 100 TRMs, y-axis showing the explained percentage of variance for the randomized (POV_exp) and observed (POV_obs) data. (C) Functionally diverse gene deletions trigger the same TRMs. Functional relatedness between all gene pairs (N = 98,438) triggering the same TRM is calculated as the Pearson correlation of their genetic interaction profiles (Costanzo et al. 2016). Violin plots show the density distributions of these Pearson correlation coefficients for each TRM, and black dots indicate their mean. Red line shows the randomly expected correlation in our set of genes (0.007), whereas blue and green lines show correlation coefficients indicating functional relatedness (0.2) and shared complex/pathway memberships (0.4), respectively (Costanzo et al. 2016). Only TRMs with more than ten data points are shown.

Fig. 3.

TRMs mimic genomic expression response to environmental change. (A) Upper panel: number and relative proportion of environmental expression responses correlating with TRMs above our threshold (|Spearman ρ| > 0.2). Lower panel: TRMs mimicking environmental expression responses above the same correlation threshold. Boxplot shows the distribution of correlation coefficients between TRM #1 and the most similar environmental profiles for 600 randomized data sets (box indicates the interquartile range [IQR], hinges represent third quartile + 1.5 IQR and first quartile − 1.5 IQR). (B) Heatmap showing transcripts (column) with largest absolute fold-change in TRM #2 (top and bottom 8%) and two relevant environments: comparison between anaerobic and anaerobic conditions under nitrogen limitation (Knijnenburg et al. 2007) and citrinin treatment (Iwahashi et al. 2007). For visual clarity, rank-transformed values are shown.(C) GO Slim category enrichment in the genes significantly affected by TRM #2 (Materials and Methods). Sign of the enrichment indicates expression direction in the signature profile (i.e., expression profile of the deletion strain with the highest amount of expression change in TRM #2, see Materials and Methods). Only those categories are shown where the enrichment is significant (Fisher’s test, FDR-adjusted P < 0.001.

Fig. 4.

Nonadaptive gene expression changes in response to gene deletion. (A) Fraction of upregulated genes being in synthetic sick of lethal genetic interaction with the deleted gene. The same fractions are shown for high- and low-fitness deletion strains (based on a 0.95 relative growth rate threshold), and for all strains as well. Dashed lines indicate the randomly expected fractions estimated by randomization (Materials and Methods). Asterisks indicate significant enrichment (*/**/*** indicates P value < 0.05/0.01/0.001, respectively, NS indicates nonsignificant). (B, C) Altered expression of dosage-sensitive genes is associated with lower fitness. Residual fitness of deletion strains negatively correlates with the number of overexpression-sensitive genes upregulated (Spearman correlation P = 10⁻¹⁶, panel B) or haploinsufficient genes downregulated (Spearman correlation P = 7 × 10⁻¹¹, panel C). Residual fitness is the fitness of a deletion strain after controlling for the total number of up- or downregulations. Boxplots show data points being grouped together for multiple ranges of number of up- or downregulations, whereas insets show the same data across the whole range of up- or downregulations with a second order polynomial fit. For details on the statistical analyses, see Materials and Methods.

Fig. 5.

Experimental test of suboptimal downregulation in knockouts. The figure shows the growth curves of strains with and without SAM1 overexpression from a single-copy plasmid (MoBY) in the wild-type (Δhis3) and Δseh1 backgrounds, respectively. SAM1 overexpression significantly increases the growth rate of Δseh1 background but not that of the WT. Average optical density values with 95% confidence bands based on 15 biological replicates are shown.