Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response

<div><p>Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in <i>Saccharomyces cerevisiae</i>, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.</p></div

Genome-wide association across <i>Saccharomyces cerevisiae</i> strains reveals substantial variation in underlying gene requirements for toxin tolerance

<div><p>Cellulosic plant biomass is a promising sustainable resource for generating alternative biofuels and biochemicals with microbial factories. But a remaining bottleneck is engineering microbes that are tolerant of toxins generated during biomass processing, because mechanisms of toxin defense are only beginning to emerge. Here, we exploited natural diversity in 165 <i>Saccharomyces cerevisiae</i> strains isolated from diverse geographical and ecological niches, to identify mechanisms of hydrolysate-toxin tolerance. We performed genome-wide association (GWA) analysis to identify genetic variants underlying toxin tolerance, and gene knockouts and allele-swap experiments to validate the involvement of implicated genes. In the process of this work, we uncovered a surprising difference in genetic architecture depending on strain background: in all but one case, knockout of implicated genes had a significant effect on toxin tolerance in one strain, but no significant effect in another strain. In fact, whether or not the gene was involved in tolerance in each strain background had a bigger contribution to strain-specific variation than allelic differences. Our results suggest a major difference in the underlying network of causal genes in different strains, suggesting that mechanisms of hydrolysate tolerance are very dependent on the genetic background. These results could have significant implications for interpreting GWA results and raise important considerations for engineering strategies for industrial strain improvement.</p></div

<i>LEU3</i> is important for SynH tolerance.

<p>(A) Wild-type YPS128 and a YPS128 <i>leu3Δ</i> mutant were grown in Synthetic Complete medium (SC), SynH without toxins (SynH -HT), or SynH with toxins, and final OD₆₀₀ was measured after 24 hours. (B) Final OD₆₀₀ was also measured in strains grown in media with 10X SC concentration of branched amino acids (leucine, isoleucine, and valine) in SynH -HT and SynH. Data represent average of 3 replicates with standard deviation. Significance was determined by paired t-test.</p

Inferred NaCl-activated phosphorylation signaling network.

<p><b>(A)</b> Consensus network at 75% confidence where node size represents degree. Pde2, Hog1, and Cdc14 sources are denoted with green, purple, and orange circles, respectively. Rectangular submodules are colored yellow or blue if their phospho-peptides showed increasing or decreasing phosphorylation upon NaCl treatment. (<b>B)</b> Precision-recall curves were calculated using a true positive list, excluding submodules and sources (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#sec011" target="_blank">Methods</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.s001" target="_blank">S1 Supporting Information</a> Section 3 for evaluation details). <i>Precision</i> is the percentage of network proteins that are true positives, while <i>Recall</i> is the percentage of true positives retrieved.</p

Subnetwork related to cell cycle control.

<p>A manually chosen region of the network capturing known regulation between Hog1 and Hsl1 (see text), other regulators connected to Hog1 or Hsl1, and all submodules connected to those regulators. Submodules are annotated by the phospho-motif and mutant phenotype if peptide changes were defective (-) or amplified (+) in the <i>hog1</i>Δ (‘h’), <i>pde2</i>Δ (‘p’), or <i>cdc14-3</i> (‘c’) mutants, and colored according to the key. Solid arrows represent directed SI-submodule edges or known directional interactions, dashed arrows represent directionality inferred by the ILP, and ball-and-stick edges indicate protein constituents of the submodule from which the line emits. Red arrows indicate a motif match between the known SI kinase specificity and the target submodule (FDR < 0.2). Asterisks denote submodules containing known Cdc28 target proteins, as curated in Chasman <i>et al</i> [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref052" target="_blank">52</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref053" target="_blank">53</a>], or phospho-peptides with defective phosphorylation in a strain in which Cdc28 was chemically inhibited [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref005" target="_blank">5</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref061" target="_blank">61</a>].</p

SNPs associated with SynH tolerance.

<p>SNPs associated with SynH tolerance.</p

Knockout effects of genes containing SNPs found in GWA.

<p>Genes linked to SNPs implicated by GWA were deleted in one or two genetic backgrounds, tolerant strain YPS128 (A) and sensitive strain YJM1444 (B). Significance was determined by paired T-test (where experiments were paired by replicate date, see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007217#sec010" target="_blank">Methods</a>) with FDR correction compared to respective wild type strain. Asterisks indicate FDR < 0.05 or p< 0.05 (which corresponds to FDR of ~13%), according to the key. Deletion strains in (B) are ordered as in (A); NA indicates missing data due our inability to make the gene deletion in that background. <i>zrt1-adh4Δ</i> indicates the deletion of an intergenic sequence between these genes.</p

Strain-specific difference for SynH and HT tolerance.

<p>Tolerance to lignocellulosic hydrolysate across strains (left) and across each population (right) measured as glucose consumption in SynH (A) and HT tolerance based on glucose consumption (B), calculated as described in Methods for 165 strains. Individual strains in B were ordered based on the quantitative scores in A. Population distributions shown in the boxplots are indicated for named populations from <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007217#pgen.1007217.g001" target="_blank">Fig 1</a>.</p

Increased SynH performance in the <i>mne1Δ</i> mutant is independent of oxygen availability.

<p>Wild type YPS128 and the <i>mne1Δ</i> mutant were grown anaerobically as described in Methods, and media was sampled over time to determine (A) cell density, (B) glucose consumption, and (C) ethanol production over time. Plots represent the average and standard deviation of 3 replicates.</p

Pde2 interacts with stress-regulated transcription factors.

<p>Shown are 10 phospho-repressed submodules (blue rectangles) downstream of at least one PKA subunit and containing at least one transcription factor, as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.g004" target="_blank">Fig 4</a>. Dashed lines without arrows denote Pde2 protein interactions identified by co-IP. Bolded red text denotes factors that are either known or predicted PKA targets [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref052" target="_blank">52</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref053" target="_blank">53</a>] or reside in pathways directly regulated by PKA [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref083" target="_blank">83</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref085" target="_blank">85</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref087" target="_blank">87</a>].</p