Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates

Countless studies monitor the growth rate of microbial populations as a measure of fitness. However, an enormous gap separates growth-rate differences measurable in the laboratory from those that natural selection can distinguish efficiently. Taking advantage of the recent discovery that transcript and protein levels in budding yeast closely track growth rate, we explore the possibility that growth rate can be more sensitively inferred by monitoring the proteomic response to growth, rather than growth itself. We find a set of proteins whose levels, in aggregate, enable prediction of growth rate to a higher precision than direct measurements. However, we find little overlap between these proteins and those that closely track growth rate in other studies. These results suggest that, in yeast, the pathways that set the pace of cell division can differ depending on the growth-altering stimulus. Still, with proper validation, protein measurements can provide high-precision growth estimates that allow extension of phenotypic growth-based assays closer to the limits of evolutionary selection

Quantifying Condition-Dependent Intracellular Protein Levels Enables High-Precision Fitness Estimates

Countless studies monitor the growth rate of microbial populations as a measure of fitness. However, an enormous gap separates growth-rate differences measurable in the laboratory from those that natural selection can distinguish efficiently. Taking advantage of the recent discovery that transcript and protein levels in budding yeast closely track growth rate, we explore the possibility that growth rate can be more sensitively inferred by monitoring the proteomic response to growth, rather than growth itself. We find a set of proteins whose levels, in aggregate, enable prediction of growth rate to a higher precision than direct measurements. However, we find little overlap between these proteins and those that closely track growth rate in other studies. These results suggest that, in yeast, the pathways that set the pace of cell division can differ depending on the growth-altering stimulus. Still, with proper validation, protein measurements can provide high-precision growth estimates that allow extension of phenotypic growth-based assays closer to the limits of evolutionary selection.</p

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping.

Pleiotropy-when a single mutation affects multiple traits-is a controversial topic with far-reaching implications. Pleiotropy plays a central role in debates about how complex traits evolve and whether biological systems are modular or are organized such that every gene has the potential to affect many traits. Pleiotropy is also critical to initiatives in evolutionary medicine that seek to trap infectious microbes or tumors by selecting for mutations that encourage growth in some conditions at the expense of others. Research in these fields, and others, would benefit from understanding the extent to which pleiotropy reflects inherent relationships among phenotypes that correlate no matter the perturbation (vertical pleiotropy). Alternatively, pleiotropy may result from genetic changes that impose correlations between otherwise independent traits (horizontal pleiotropy). We distinguish these possibilities by using clonal populations of yeast cells to quantify the inherent relationships between single-cell morphological features. Then, we demonstrate how often these relationships underlie vertical pleiotropy and how often these relationships are modified by genetic variants (quantitative trait loci [QTL]) acting via horizontal pleiotropy. Our comprehensive screen measures thousands of pairwise trait correlations across hundreds of thousands of yeast cells and reveals ample evidence of both vertical and horizontal pleiotropy. Additionally, we observe that the correlations between traits can change with the environment, genetic background, and cell-cycle position. These changing dependencies suggest a nuanced view of pleiotropy: biological systems demonstrate limited pleiotropy in any given context, but across contexts (e.g., across diverse environments and genetic backgrounds) each genetic change has the potential to influence a larger number of traits. Our method suggests that exploiting pleiotropy for applications in evolutionary medicine would benefit from focusing on traits with correlations that are less dependent on context

Selection Transforms the Landscape of Genetic Variation Interacting with Hsp90

<div><p>The protein-folding chaperone Hsp90 has been proposed to buffer the phenotypic effects of mutations. The potential for Hsp90 and other putative buffers to increase robustness to mutation has had major impact on disease models, quantitative genetics, and evolutionary theory. But Hsp90 sometimes contradicts expectations for a buffer by potentiating rapid phenotypic changes that would otherwise not occur. Here, we quantify Hsp90’s ability to buffer or potentiate (i.e., diminish or enhance) the effects of genetic variation on single-cell morphological features in budding yeast. We corroborate reports that Hsp90 tends to buffer the effects of standing genetic variation in natural populations. However, we demonstrate that Hsp90 tends to have the opposite effect on genetic variation that has experienced reduced selection pressure. Specifically, Hsp90 tends to enhance, rather than diminish, the effects of spontaneous mutations and recombinations. This result implies that Hsp90 does not make phenotypes more robust to the effects of genetic perturbation. Instead, natural selection preferentially allows buffered alleles to persist and thereby creates the false impression that Hsp90 confers greater robustness.</p></div

Epistatic interactions between Hsp90 and spontaneous mutations do not often involve buffering or potentiation.

<p>(A) Models of the different effects that Hsp90 might have on phenotypic variation between strains are shown. Each plot displays median phenotype per strain in GdA− (blue circles) and GdA+ (red circles); each line connecting two circles follows the change that Hsp90 inhibition has on a given strain. When lines have different slopes, GdA has a genotype-specific effect (rightmost 3 models). Line-crossing epistasis can be distinguished from buffering or potentiating, which are line-spreading subtypes of epistasis. (B) Line-crossing epistasis is far more prevalent than buffering or potentiation between Hsp90 and the spontaneous mutations present in the MA lines. The horizontal axis represents the fraction of the interaction between GdA and genotype that can be explained by line spreading (as opposed to line crossing); the vertical axis represents the number of PCs that fall into each bin, where bin width is 1%. PCs for which line spreading contributes >1% of this interaction are labeled; bold-labeled PCs are those plotted in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.g003" target="_blank">Fig 3A and 3B</a>. (C) An example phenotype for which line crossing contributes >99% (spreading contributes <1%) of the interaction between GdA and MA lines. These data are plotted as in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.g003" target="_blank">Fig 3A</a>. (D) The effects of GdA on MA line morphology are more similar to the effects of another Hsp90 inhibitor radicicol (Rad) than they are to the effects of abbreviated growth. Displayed plots are similar to those in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.g003" target="_blank">Fig 3A</a>, except here they show a random subset of 22 MA lines that were imaged after growth in GdA, Rad, and in a “less growth” condition in which we reduced the duration of exponential phase from 6 to 4 h (see <i>Experimental Procedures</i>). This PC was chosen because both correlation coefficients (<b><i>r</i></b>) are close to their median values across all 29 PCs (for all PCs, see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.s004" target="_blank">S4 Fig</a>).</p

GdA’s effect on morphological variation differs in yeast strains possessing spontaneous mutations or recombinations, as compared to natural yeast isolates.

<p>In (A–C), each point is plotted to represent between-strain morphological variation (standard deviation) in the GdA+ condition minus that in the GdA− condition; colored dots represent significant variance increases (green) or decreases (purple) in GdA+ versus GdA− (significance defined as when the 95% credible interval surrounding the difference does not overlap zero). Boxplots summarize the distribution across all 29 PCs for each strain collection, displaying the median (center line), interquartile range (IQR) (upper and lower hinges), highest value within 1.5 × IQR (whiskers), and roughly a 95% confidence interval around the median calculated as 1.58 × IQR / √n (notches). If this confidence interval does not overlap zero, boxplots are colored green when Hsp90-inhibition tends to reveal variation and purple when inhibition tends to hide variation. Although PCs differ in the amount of variance explained, each is scaled to have an overall variance of 1. Panels represent GdA’s effect on (A) variation between MA lines, (B) variation between strains in four collections of yeast isolated from natural environments, and (C) variation between recombinant progeny of a mating between two divergent yeast strains (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.s005" target="_blank">S5 Fig</a> for similar plots depicting only those PCs for which variance is not affected by growth perturbations). (D) For the PC indicated by grey arrows in panels A–C, these plots display the average morphologies for each strain in GdA+ and GdA− conditions as well as the between-strain variation (blue and red bars), which decreases in GdA+ for MA and Rec lines but increases in other strain collections. Plots are drawn as in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.g003" target="_blank">Fig 3A</a> (for all PCs, see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.s002" target="_blank">S2 Fig</a>). (E) Points represent only those PCs that have a significant GdA-by-genotype interaction in linear models for each strain collection (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.s007" target="_blank">S1 Table</a>). The horizontal axis represents the fraction of this interaction that can be explained by line spreading (as opposed to line crossing). The dashed line helps guide the eye to see that this fraction, although low across all strain collections, is lowest for those that experienced reduced selection pressure (MA and Rec) relative to collections of natural yeast isolates (Ale, Div, SPD, and SPH; for additional evidence that natural isolates experienced stabilizing selection on morphological traits, see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.s006" target="_blank">S6 Fig</a>). The vertical axis represents the same as in panels A–C: the between-strain morphological variation (standard deviation) in the GdA+ condition minus that in the GdA− condition. Points are colored as in panels A–C.</p

Responses of genetically diverse populations to inhibition of a buffer or potentiator.

<p>(A) Inhibiting a protein that buffers the phenotypic effects of genetic variation will reveal those effects, which increases the phenotypic diversity between genetically distinct individuals. (B) Inhibiting a protein that potentiates the phenotypic effects of genetic variation will diminish those effects, which decreases the phenotypic diversity between genetically distinct individuals.</p

Many MA lines possess spontaneous mutations with GdA-dependent effects on morphology.

<p>(A–B) Two example phenotypes for which some MA lines have unique responses to GdA (for other phenotypes, see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.s002" target="_blank">S2 Fig</a>). Each open circle and its associated vertical bar represents the average morphology of an individual MA line in either the GdA− or GdA+ condition, +/– 1 standard deviation. Lines connect open circles representing the same MA line; the darkness of each black line is proportional to how much a given MA line’s response to GdA differs from the ancestor’s. Colored lines: magenta = MA ancestor; yellow = MA line #2; orange = MA line #30; cyan = MA line #42; olive = MA line #137. Micrographs (with cell images adjusted for uniform size and orientation) reflect how morphological traits that contribute strongly to each PC respond to GdA in selected strains. The cells shown from each replicate (“rep 1” or “rep 2”) possess the value closest to the median for a given PC in the given strain and treatment. The trait that contributes most strongly to each PC is listed above the cell images (and in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.s007" target="_blank">S1 Table</a>); the >, <, or ≈ symbols indicate how this trait differs in GdA− versus GdA+ cells. Each solid black circle and its associated blue or red vertical bar reports the average phenotype +/– 1 standard deviation across all MA lines in GdA− or GdA+. (C) The MA lines with the most divergent responses to GdA, relative to the ancestral response, differ for different PCs (see also <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.s003" target="_blank">S3 Fig</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.s008" target="_blank">S2 Table</a>). This cumulative distribution describes the number of unique MA lines represented among the top 1 through 25 most-divergently responding MA lines for each PC (open circles). The dotted line shows the maximum possible value. (D) MA lines possessing coding mutations predicted to have severe effects on protein function tend to have greater responses to GdA relative to the ancestral response (see also <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2000465#pbio.2000465.s008" target="_blank">S2 Table</a>). Open circles represent 20 of the 94 MA lines that each possess only a single coding mutation. The mutated gene is listed next to the circle. Vertical axis represents the maximum absolute value of the difference in MA line versus ancestral response to GdA across all 29 PCs. Boxplots represent the distribution of these maximum absolute differences for MA lines with either a single synonymous (“Syn”), conservative (“Con”), radical (“Rad”), or damaging (“Dmg”) mutation, displaying the median (center line), interquartile range (IQR) (upper and lower hinges), and highest value within 1.5 × IQR (whiskers).</p