Network Hubs Buffer Environmental Variation in Saccharomyces cerevisiae

Regulatory and developmental systems produce phenotypes that are robust to environmental and genetic variation. A gene product that normally contributes to this robustness is termed a phenotypic capacitor. When a phenotypic capacitor fails, for example when challenged by a harsh environment or mutation, the system becomes less robust and thus produces greater phenotypic variation. A functional phenotypic capacitor provides a mechanism by which hidden polymorphism can accumulate, whereas its failure provides a mechanism by which evolutionary change might be promoted. The primary example to date of a phenotypic capacitor is Hsp90, a molecular chaperone that targets a large set of signal transduction proteins. In both Drosophila and Arabidopsis, compromised Hsp90 function results in pleiotropic phenotypic effects dependent on the underlying genotype. For some traits, Hsp90 also appears to buffer stochastic variation, yet the relationship between environmental and genetic buffering remains an important unresolved question. We previously used simulations of knockout mutations in transcriptional networks to predict that many gene products would act as phenotypic capacitors. To test this prediction, we use high-throughput morphological phenotyping of individual yeast cells from single-gene deletion strains to identify gene products that buffer environmental variation in Saccharomyces cerevisiae. We find more than 300 gene products that, when absent, increase morphological variation. Overrepresented among these capacitors are gene products that control chromosome organization and DNA integrity, RNA elongation, protein modification, cell cycle, and response to stimuli such as stress. Capacitors have a high number of synthetic-lethal interactions but knockouts of these genes do not tend to cause severe decreases in growth rate. Each capacitor can be classified based on whether or not it is encoded by a gene with a paralog in the genome. Capacitors with a duplicate are highly connected in the protein–protein interaction network and show considerable divergence in expression from their paralogs. In contrast, capacitors encoded by singleton genes are part of highly interconnected protein clusters whose other members also tend to affect phenotypic variability or fitness. These results suggest that buffering and release of variation is a widespread phenomenon that is caused by incomplete functional redundancy at multiple levels in the genetic architecture

Bet Hedging in Yeast by Heterogeneous, Age-Correlated Expression of a Stress Protectant

A new experimental approach reveals a bet-hedging strategy in unstressed, clonal yeast cells, whereby they adopt a range of growth states that correlate with expression of a trehalose-synthesis regulator and predict resistance to future stress

Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations

We tested whether DNA-methylation profiles account for inter-individual variation in body mass index (BMI) and height and whether they predict these phenotypes over and above genetic factors. Genetic predictors were derived from published summary results from the largest genome-wide association studies on BMI (n ∼ 350,000) and height (n ∼ 250,000) to date. We derived methylation predictors by estimating probe-trait effects in discovery samples and tested them in external samples. Methylation profiles associated with BMI in older individuals from the Lothian Birth Cohorts (LBCs, n = 1,366) explained 4.9% of the variation in BMI in Dutch adults from the LifeLines DEEP study (n = 750) but did not account for any BMI variation in adolescents from the Brisbane Systems Genetic Study (BSGS, n = 403). Methylation profiles based on the Dutch sample explained 4.9% and 3.6% of the variation in BMI in the LBCs and BSGS, respectively. Methylation profiles predicted BMI independently of genetic profiles in an additive manner: 7%, 8%, and 14% of variance of BMI in the LBCs were explained by the methylation predictor, the genetic predictor, and a model containing both, respectively. The corresponding percentages for LifeLines DEEP were 5%, 9%, and 13%, respectively, suggesting that the methylation profiles represent environmental effects. The differential effects of the BMI methylation profiles by age support previous observations of age modulation of genetic contributions. In contrast, methylation profiles accounted for almost no variation in height, consistent with a mainly genetic contribution to inter-individual variation. The BMI results suggest that combining genetic and epigenetic information might have greater utility for complex-trait prediction

Cell sorting by Tsl1-GFP content alters growth rate distributions and heat shock survival.

<p>(A) The distribution of Tsl1 abundance does not appear bimodal. Histogram of single-cell Tsl1-GFP intensity as measured by fluorescence-activated cell sorting. Inset: the right-hand tail of the main figure. Cells with the top 0.1% (dark green) and next 0.1 to 1% (light green) Tsl1-GFP fluorescence were sorted for downstream analysis. (B) Survival of sorted populations following heat shock of a liquid suspension as measured by plating on agar plates. <i>p</i>-Values are a comparison to the unsorted population (purple), Student's <i>t</i> test of arcsin transformed data: *, <i>p</i><0.01; **, <i>p</i><1×10⁻⁵. Error bars indicate SEM. (C) Sorting for Tsl1-GFP abundance transiently alters growth rate distributions. Left: cumulative growth rate distributions of sorted cells (dark and light green), cells passed through the cell sorter but unsorted (purple), and cells not passed through the cell sorter (orange). Right: Samples of the same sorted or unsorted cell populations after ∼42 generations of growth.</p

Tsl1 protein content marks slow growth.

<p>(A) A bioinformatic screen for candidates marking slow-growing cells. The correlation of mRNA expression with the bulk population growth rate <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001325#pbio.1001325-Brauer1" target="_blank">[18]</a> is plotted against the protein-expression noise (the extent of cell-to-cell variation in expression, DM in synthetic dextrose media from <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001325#pbio.1001325-Newman1" target="_blank">[4]</a>) for each gene. 78 noisy genes that anti-correlate with the growth rate are in the upper left quadrant (dashed lines). Among these are three subunits of the trimeric trehalose synthase complex (green circles). (B) Time-lapse microscopy of cells expressing Tsl1-GFP under the endogenous <i>TSL1</i> promoter. Three time points of Tsl1-GFP fluorescence overlaid onto bright-field (left) and 1.67-fold magnified views of bright-field (top right for each time point) and GFP fluorescence (bottom right for each time point) of three colonies from the field. Arrows indicate the emergence of a morphologically distinct, slowly dividing, Tsl1-GFP fluorescent cell within a colony. (C) Tsl1 abundance correlates with growth rate. Top: A histogram of the specific growth rates of <i>TSL1</i>-GFP cells. Colors indicate bins used in bottom. Bottom: Tsl1-GFP fluorescence intensity per unit area of colonies binned by growth rate. Error bars indicate standard error of the mean (SEM); <i>p</i>-values are a comparison to all colonies; Wilcoxon-Mann-Whitney test: *, <i>p</i><0.01; ***, <i>p</i><1×10⁻¹⁰.</p

Growth heterogeneity is a stress survival mechanism.

<p>(A) Slowly growing, Tsl1 abundant cells survive heat shock. Time-lapse images of Tsl1-GFP fluorescence overlaid onto bright-field (left) and Tsl1-GFP fluorescence of the right-most colony (right) before and after heat shock. Colonies are grown for 5 h to monitor growth rate and Tsl1-GFP fluorescence, heat shocked for 70 min at 60°C, resulting in massive cell death, and monitored for growth for 13 h following heat shock to identify colonies that contain at least one surviving cell. Bright-field and fluorescent images of the entire field at each time point are shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001325#pbio.1001325.s017" target="_blank">Video S4</a>. (B) Tsl1-GFP fluorescent colonies are more likely to survive heat shock. Percentage of colonies that contain at least one cell that survives heat shock binned by Tsl1-GFP fluorescence. <i>p</i>-Values are a comparison to all colonies, Fisher's exact test: **, <i>p</i><1×10⁻⁵, ***, <i>p</i><1×10⁻¹⁰. (C) <i>TSL1</i> contributes to survival in slow-growing colonies. Percentage of colonies that contain at least one cell that survives heat shock binned by growth rate for chimeric <i>TSL1</i>-GFP (green) or <i>TSL1</i> replaced with mCherry (grey) at the endogenous <i>TSL1</i> locus. Both growth rate and <i>TSL1</i> genotype significantly affect survivorship (multiple logistic regression, <i>p</i><10⁻²⁸ and <i>p</i><0.01, respectively). (D) <i>TSL1</i> contributes to population resistance to acute heat shock. Survival of a strain containing a gene deletion of <i>TSL1</i> (green) or a control dubious open reading frame (<i>YFR054C</i>, grey) as measured by plating on agar following heat shock of cell suspensions. Student's <i>t</i> test of arcsin transformed data; **, <i>p</i><1×10⁻⁴. Error bars indicate SEM.</p

Old cells are Tsl1-GFP abundant.

<p>(A) <i>TSL1</i>-GFP yeast stained with the bud scar stain WGA-TRITC are passed though a cell sorter to monitor co-fluorescence. Shown is the WGA-TRITC fluorescence of cells binned by Tsl1-GFP fluorescence. <i>p</i>-Values are a comparison to all cells, Wilcoxon-Mann-Whitney test: ***, <i>p</i><1×10⁻¹⁰. (B) Sorted WGA-TRITC stained <i>TSL1</i>-GFP cells are counted for bud scars. Shown is the cumulative percentage of all cells (grey) and cells in the top 1% Tsl1-GFP fluorescence bin (green). The 1% Tsl1-GFP cells have significantly more bud scars, Wilcoxon-Mann-Whitney test, <i>p</i><1×10⁻⁷. (C) Population demography accounts for some expression “noise.” Shown is the protein-expression noise (DM in yeast permissive dextrose media from <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001325#pbio.1001325-Newman1" target="_blank">[4]</a>) of genes binned by logarithm of their age expression ratio, the average expression in young cells over the average expression in old cells <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001325#pbio.1001325-Lesur1" target="_blank">[73]</a>. Error bars indicate SEM; <i>p</i>-values are a comparison to all colonies; Wilcoxon-Mann-Whitney test: *, <i>p</i><0.05; **, <i>p</i><0.001.</p

The genotype‐phenotype landscape of an allosteric protein

Abstract Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype‐phenotype relationships remains elusive. Here, we report the large‐scale measurement of the genotype‐phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli, LacI. Using a method that combines long‐read and short‐read DNA sequencing, we quantitatively measure the dose‐response curves for nearly 105 variants of the LacI genetic sensor. The resulting data provide a quantitative map of the effect of amino acid substitutions on LacI allostery and reveal systematic sequence‐structure‐function relationships. We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural‐network models, but unpredictable changes also occur. For example, we were surprised to discover a new band‐stop phenotype that challenges conventional models of allostery and that emerges from combinations of nearly silent amino acid substitutions

Histone Variant HTZ1 Shows Extensive Epistasis with, but Does Not Increase Robustness to, New Mutations

<div><p>Biological systems produce phenotypes that appear to be robust to perturbation by mutations and environmental variation. Prior studies identified genes that, when impaired, reveal previously cryptic genetic variation. This result is typically interpreted as evidence that the disrupted gene normally increases robustness to mutations, as such robustness would allow cryptic variants to accumulate. However, revelation of cryptic genetic variation is not necessarily evidence that a mutationally robust state has been made less robust. Demonstrating a difference in robustness requires comparing the ability of each state (with the gene perturbed or intact) to suppress the effects of new mutations. Previous studies used strains in which the existing genetic variation had been filtered by selection. Here, we use mutation accumulation (MA) lines that have experienced minimal selection, to test the ability of histone H2A.Z (HTZ1) to increase robustness to mutations in the yeast <i>Saccharomyces cerevisiae</i>. HTZ1, a regulator of chromatin structure and gene expression, represents a class of genes implicated in mutational robustness. It had previously been shown to increase robustness of yeast cell morphology to fluctuations in the external or internal microenvironment. We measured morphological variation within and among 79 MA lines with and without HTZ1. Analysis of within-line variation confirms that HTZ1 increases microenvironmental robustness. Analysis of between-line variation shows the morphological effects of eliminating HTZ1 to be highly dependent on the line, which implies that HTZ1 interacts with mutations that have accumulated in the lines. However, lines without HTZ1 are, as a group, not more phenotypically diverse than lines with HTZ1 present. The presence of HTZ1, therefore, does not confer greater robustness to mutations than its absence. Our results provide experimental evidence that revelation of cryptic genetic variation cannot be assumed to be caused by loss of robustness, and therefore force reevaluation of prior claims based on that assumption.</p></div

Estimates of percentage of interaction variance explained by crossing of line means or spreading of line means, along with 95% credible intervals (CrI), for each principal component (PC), derived from MCMC.

<p>Estimates of percentage of interaction variance explained by crossing of line means or spreading of line means, along with 95% credible intervals (CrI), for each principal component (PC), derived from MCMC.</p