Quantitative analysis of protein interaction network dynamics in yeast
2017; Springer Nature; Volume: 13; Issue: 7 Linguagem: Inglês
10.15252/msb.20177532
ISSN1744-4292
AutoresAlbi Celaj, Ulrich Schlecht, Justin Smith, Weihong Xu, Sundari Suresh, Molly Miranda, Ana M. Aparicio, Michael Proctor, Ronald W. Davis, Frederick P. Roth, Robert P. St.Onge,
Tópico(s)Microbial Metabolic Engineering and Bioproduction
ResumoArticle13 July 2017Open Access Transparent process Quantitative analysis of protein interaction network dynamics in yeast Albi Celaj Albi Celaj orcid.org/0000-0002-5888-772X Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada Donnelly Centre, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada Search for more papers by this author Ulrich Schlecht Ulrich Schlecht Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Justin D Smith Justin D Smith orcid.org/0000-0003-3079-3534 Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Weihong Xu Weihong Xu orcid.org/0000-0003-1552-3333 Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Search for more papers by this author Sundari Suresh Sundari Suresh Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Molly Miranda Molly Miranda Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Ana Maria Aparicio Ana Maria Aparicio Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Michael Proctor Michael Proctor Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Ronald W Davis Ronald W Davis Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Frederick P Roth Corresponding Author Frederick P Roth [email protected] orcid.org/0000-0002-6628-649X Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada Donnelly Centre, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada Canadian Institute for Advanced Research, Toronto, ON, Canada Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, USA Search for more papers by this author Robert P St.Onge Corresponding Author Robert P St.Onge [email protected] orcid.org/0000-0003-1626-3107 Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Albi Celaj Albi Celaj orcid.org/0000-0002-5888-772X Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada Donnelly Centre, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada Search for more papers by this author Ulrich Schlecht Ulrich Schlecht Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Justin D Smith Justin D Smith orcid.org/0000-0003-3079-3534 Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Weihong Xu Weihong Xu orcid.org/0000-0003-1552-3333 Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Search for more papers by this author Sundari Suresh Sundari Suresh Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Molly Miranda Molly Miranda Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Ana Maria Aparicio Ana Maria Aparicio Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Michael Proctor Michael Proctor Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Ronald W Davis Ronald W Davis Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Frederick P Roth Corresponding Author Frederick P Roth [email protected] orcid.org/0000-0002-6628-649X Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada Donnelly Centre, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada Canadian Institute for Advanced Research, Toronto, ON, Canada Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, USA Search for more papers by this author Robert P St.Onge Corresponding Author Robert P St.Onge [email protected] orcid.org/0000-0003-1626-3107 Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA Search for more papers by this author Author Information Albi Celaj1,2,3,‡, Ulrich Schlecht4,5,‡, Justin D Smith4,6, Weihong Xu4, Sundari Suresh4,5, Molly Miranda4,5, Ana Maria Aparicio4,5, Michael Proctor4,5, Ronald W Davis4,5,6, Frederick P Roth *,1,2,3,7,8 and Robert P St.Onge *,4,5 1Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada 2Donnelly Centre, University of Toronto, Toronto, ON, Canada 3Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada 4Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA 5Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA 6Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA 7Canadian Institute for Advanced Research, Toronto, ON, Canada 8Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, USA ‡These authors contributed equally to this work *Corresponding author. Tel: +1 416 946 5130; E-mail: [email protected] *Corresponding author. Tel: +1 650 721 2976; E-mail: [email protected] Molecular Systems Biology (2017)13:934https://doi.org/10.15252/msb.20177532 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment-dependent protein complex dynamics, we used a DNA-barcode-based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that "concerted" protein-centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass-action-based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics. Synopsis A multiplexed assay measures abundance of 1,379 binary protein complexes in 14 environments. Many environment-dependent changes were found, enabling exploration of the extent to which network dynamics can be explained by mRNA levels. A DNA-barcode-based multiplexed protein interaction assay measured in vivo abundance of 1,379 binary protein complexes under 14 diverse environments in Saccharomyces cerevisiae. More than half of binary complexes were found to be environment-dependent, especially those among transmembrane transporters. Many binary complexes changed in a concerted, protein-centric manner, and under a "diauxic" shift in carbon source from glucose to ethanol, mRNA levels predicted many of the observed changes. Introduction The molecular function and cellular role of a protein often cannot be understood without knowledge of its interactions with other proteins. For this reason, multiple high-throughput methods have been developed to identify direct protein–protein interactions (PPIs) on a genomewide scale, for example, using yeast 2-hybrid (Y2H) or protein-fragment complementation assay (PCA) methods. These and complementary techniques for detecting co-complexation, for example, affinity purification coupled with tandem mass spectrometry (AP-MS), have yielded a wealth of PPI data in model organisms and humans (Uetz et al, 2000; Ito et al, 2001; Butland et al, 2005; Stelzl et al, 2005; Gavin et al, 2006; Krogan et al, 2006; Tarassov et al, 2008; Yu et al, 2008). A positive Y2H assay suggests that two expressed proteins are capable of a direct biophysical PPI in the context of the Saccharomyces cerevisiae nucleus, but whether, where, and when this interaction is physiologically relevant is left undetermined. In contrast, various PCA approaches have been developed which test for an interaction in the native cellular environment at native expression levels (Tarassov et al, 2008; Schlecht et al, 2012). These approaches, reportedly able to detect as few as 25 complexes per cell (Remy & Michnick, 1999), can directly measure the dynamic dependence of a PPI on growth environment. While the results of many high-throughput PPI assays are interpreted as static maps of physical connections, it is known that the variability of protein complexes and the coordinated dynamics of physically linked gene products underlie fundamental aspects of cellular function. In an attempt to capture this, static protein–protein interactomes have been used as a "scaffold" on which to overlay and interpret other genome-scale data such as gene expression and metabolic fluxes (Ideker et al, 2002; Luscombe et al, 2004; Sauer, 2004; de Lichtenberg et al, 2005). These approaches have shown the value of PPIs in contextually understanding gene function, but they cannot straightforwardly identify quantitative changes in PPI complex levels, nor directly determine protein complex levels in an environment-dependent cellular state. A simplified view of PPI dynamics as "binary switches" in which interactions are either present or absent is common and can offer biological insights (de Lichtenberg et al, 2005; Greene et al, 2015) but ignores potentially important quantitative changes in protein complex abundance. The proliferating cell nuclear antigen (PCNA) complex serves to illustrate the idea of quantitative environmentally responsive protein interactions. PCNA, a major factor in DNA replication and repair, forms a chromatin-bound complex with other proteins at sites of DNA damage in response to gamma irradiation in a dose-dependent manner (Balajee & Geard, 2001; Mailand et al, 2013). In another example, salt stress leads to in vivo activation of the HOG (high-osmolarity glycerol) signal transduction pathway, which is quantitatively dependent on the interaction between Sho1 and Pbs2 (Marles et al, 2004). In order to address the limitations of static interactome maps, multiple studies have begun to identify condition-specific PPI changes directly. We have previously implemented highly multiplexed murine dihydrofolate reductase (mDHFR)-based PCA, which can detect changes in the abundance of hundreds of binary protein complexes in parallel. In this approach, the mDHFR fragments are fused to genes at their genomic locus under control of the endogenous promoter, thus allowing an in vivo study of binary protein complex level changes. This approach was used to examine the effects of 80 small molecules on 238 yeast binary protein complexes, uncovering multiple positive and negative chemical modulators (Schlecht et al, 2012). A related study by Rochette et al (2014) used a plate-based mDHFR PCA to test the response of 1,338 yeast protein interactions to methyl methanesulfonate, an alkylating agent that induces DNA damage. This study identified PPI changes in diverse cellular processes and found that, in the DNA damage response, protein relocalization is a major driver of PPI changes. A strength of the mDHFR PCA in measuring dynamic interactions is that quantitative changes in relative strain abundance reflect quantitative changes in binary protein complex abundance (Schlecht et al, 2012; Freschi et al, 2013), allowing for a detailed view of PPI remodeling by simply measuring cellular growth rates. Here, we extend our previously developed multiplex barcoded mDHFR PCA (BC-PCA) to investigate the effects of 14 chemical and environmental perturbations on 1,379 binary protein complexes. We observed widespread PPI changes in these conditions, many of which were informative for the specific stimulus applied. Altered PPIs tended to concentrate in large subnetworks and were often centered around highly connected hub proteins. More closely examining the shift from fermentative to respiratory growth, we found a highly significant relationship between binary protein complex abundance and mRNA levels that explains a large portion of the observed network dynamics. This correlation was predictive, suggesting that reasonably accurate first-order estimates of PPI network dynamics can be made using only mRNA data. Results Construction of a genome-scale barcoded protein-fragment complementation assay In the murine dihydrofolate reductase protein complementation assay (mDHFR PCA), two proteins of interest are fused to two respective fragments of mDHFR. Upon successful physical interaction of the two target proteins, the mDHFR fragments fold together into the native conformation and give rise to a functional enzyme that is resistant to methotrexate (MTX). Interaction-dependent reconstitution of MTX-resistant mDHFR allows for growth-based selection, where the extent of MTX resistance is dependent on the intracellular concentration of the PPI complex. The majority of all possible binary PPI combinations in the yeast genome have been previously subjected to mDHFR testing, leading to the identification of 2,770 PPIs under a set of standard laboratory conditions (i.e., growth at 30°C on solid media containing glucose, a rich nitrogen source, and all essential supplements; Tarassov et al, 2008). Having established the multiplex BC-PCA assay at a smaller scale (Schlecht et al, 2012), here we attempted to scale it to as many known PCA interactions as possible in a pooled and barcoded format. We first reconstructed 2,394 (see Dataset EV1) of the 2,770 Tarassov et al (2008) strains, 1,701 of which were verified to grow in liquid minimal media containing MTX (Fig EV1A and B). We observed a broad range of growth rates among these strains. This likely reflects strain-specific differences in the abundance of reconstituted mDHFR complexes that arise from differences in abundance of the binary protein complex of interest, but could also reflect differences in DHFR reconstitution efficiency arising from steric effects of different proteins fused to mDHFR fragments. Consistent with a previous study (Freschi et al, 2013), we found growth rate to be significantly correlated with the protein expression level of the least abundant protein (using abundance data from Wang et al, 2012) in the interacting pair (Pearson's r = 0.31, P < 2.2e-16, Fig EV1C). Indeed, this correspondence is theoretically expected in cases where PPI affinity is high and each pair of interacting proteins is independent of others (see Materials and Methods). Also consistent with this idea, we found that the subset of confirmed PPI pairs from Tarassov et al (2008) tended to have least abundant proteins that were more abundant than the least abundant proteins of unconfirmed PPI pairs (Fig EV1D). Taken together, these results support the idea that quantitatively measuring MTX resistance of these PCA strains can capture protein complex abundance information. Click here to expand this figure. Figure EV1. Construction of a genome-scale pool of barcoded PCA strains Haploid strains expressing mDHFR-fragment (F[1,2] or F[3])-tagged proteins of interest were mated and diploids were selected. Interaction between the two proteins was then verified by individually growing each diploid strain in liquid minimal media supplemented with methotrexate (MTX) for 3 days. The F[1,2]-containing haploids (from verified interactions) were then transformed with unique TagModule cassettes, mated with their matching F[3]-containing partner strains, and combined to create a pool (depicted by differently colored yeast cells). Growth rate of all 2,394 PCA strains initially constructed and measured as the area under the OD curve (see Materials and Methods) after 75 h of growth in selective media. A cutoff of 8 was applied to identify strains that grew under MTX selection (n = 1,701). Relating strain growth [log10(AUC)] to the cellular concentration of the least abundant protein [log10(PPM) – values taken from PaxDB] in each protein–protein interaction pair. Each dot represents one strain, and the line of best fit is shown in red. Abundance of proteins involved in previously published PCA complexes (Tarassov et al, 2008) that were confirmed (YES) or not confirmed (NO) to grow under the conditions in this study. Protein abundance values were retrieved from PaxDB (Wang et al, 2012) and log10-transformed. In the plot, for each strain representing a protein complex, the value of the less abundant protein in that interaction was used. Box boundaries indicate 1st and 3rd quartiles, with the central line indicating median. Bottom and top whiskers extend for either 1.5 times the interquartile range or to the most distal data point, whichever yields the shortest whisker. Paired scatterplots comparing raw fluorescence intensities of the six control samples (DMSO) in selective media. Numbers in the lower half indicate the Pearson's r for each pairwise comparison. Scatterplot comparing the growth rate of each strain as measured by barcode abundance following competitive growth (x-axis), to that measured in isogenic culture (y-axis). The x-axis represents log2-transformed ratio of normalized fluorescence following selective and non-selective growth (log2(+MTX/−MTX)). Download figure Download PowerPoint To adapt the successfully recreated PCA strains to the BC-PCA assay, barcode cassettes were transformed into corresponding F[1,2]-containing haploid strain for each pair, which was then mated with the corresponding F[3]-containing partner (Materials and Methods, Fig EV1A) to create a barcoded diploid strain. We successfully incorporated 1,432 barcodes, representing 1,428 of 1,701 unique interactions (Dataset EV1). These strains were then pooled and competitively grown in selective media. Genomic DNA was isolated from an aliquot, and barcodes were PCR-amplified and hybridized to a high-density oligonucleotide array in six replicates. Most barcodes (1,383 of 1,432; representing 1,379 unique interactions) were detected across all six replicates, and the rest were excluded from further analysis. As was previously found, microarray signal intensity values showed very high correlation among six replicates (r ≥ 0.97, Fig EV1E), and correspondence to non-competitive growth rates measured in isogenic culture (r = 0.69, Fig EV1F). This confirms that our pool-based assay provides a reproducible quantitative growth measure for each individual strain. Identifying protein complex dynamics under diverse conditions Many previous studies have mapped and analyzed "static" PPI networks, in which the interaction assay reveals protein pairs that are capable of interacting if expressed at sufficient concentrations at the same time and place. However, the question of how and to what extent PPI complex levels vary across different conditions has not been studied at a large scale. Given that the BC-PCA assay yields quantitative strain abundance measurements (across a large dynamic range) which correspond to growth, and because growth directly relates to reconstituted mDHFR abundance (Remy & Michnick, 1999), BC-PCA represents a means of addressing this question. Using BC-PCA, we can detect a condition-specific deviation of growth rate in response to a perturbation (compared to a "reference" state) and infer a change in binary protein complex levels. To represent broad classes of environmental change, we chose a set of 14 different perturbations, including: addition of small molecules (e.g., FK506, atorvastatin, doxorubicin); altered nutrient composition of the growth medium (e.g., ethanol instead of dextrose as the sole carbon source, nitrogen starvation, addition of specific amino acids); and abiotic stress conditions (e.g., growth at high temperature, in a high salt concentration, or under oxidative stress). Dataset EV1 lists details of all growth environments. All experiments were performed in both selective and non-selective (i.e., without MTX) media, with the latter acting as a control to identify and exclude cases where the incorporation of the mDHFR tag had an impact on growth in the condition tested (Rochette et al, 2014; Dataset EV2). In most conditions, significant growth changes in non-selective media were observed in < 2% of all strains in the pool (see Materials and Methods). Such strains were specifically excluded from the conditions in which they were identified, as were additional strains containing PCA fragments appearing multiple times in excluded strains (Dataset EV2). For each PPI in each condition tested, we calculated R, the ratio of barcode abundance in that condition (as measured by microarray signal intensity) to barcode abundance in the reference condition (selective dextrose-containing media plus 1% DMSO). Requiring both the UP- and DOWN-tag of each strain to meet our significance and effect size thresholds (q-value < 0.05 and log2(R) > 0.25 for accumulated, and q-value < 0.05 and log2(R) < −0.25 for depleted interactions), we found 757 binary complexes that varied in at least one condition (Dataset EV2). Hierarchical clustering of these data showed that all experiment replicates were grouped as closest neighbors (Fig 1A), indicating a reproducible assay. We examined the reproducibility of a subset of candidate dynamic binary complexes using isogenic growth experiments and found a strong correlation between growth assays and pooled barcode fluorescence intensity values in most cases (r > 0.6, Fig EV2A and Dataset EV1). We also observed binary complexes where there was a clear dose-dependent relationship between the addition of a small molecule and the relative growth rate under MTX selection (Fig EV2B). Together, these results suggest that the BC-PCA assay can measure quantitative changes in abundance of many binary complexes, supporting its utility for studying condition-dependent global PPI remodeling. Figure 1. Environmental cues elicit changes in the yeast protein interaction network Heatmap depicting log2-ratios of 757 binary protein complexes that displayed a significant change in at least one of the conditions tested here. Complexes are arranged on the y-axis, and conditions are arranged on the x-axis. Accumulated and depleted signals are colored in blue and red, respectively. Dendrograms on the left and on top show clustering of complexes and samples, respectively. Three clusters of binary protein complexes. Complexes are labeled on the right. Barplot depicting the number of protein complexes whose abundance significantly changed (log2(R)) > 0.25 or log2(R) < −0.25 for both UP- and DOWN-tags in the same direction, q < 0.05 for both UP- and DOWN-tags) in response to 14 perturbations (indicated on the x-axis). Number of complexes that were accumulated or depleted in response to each condition are shown in blue and orange bars, respectively. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Confirmation of pool data with isogenic culture Multiple strains that displayed enhanced or diminished growth (green and red, respectively) in the pool screen in doxorubicin, FK506, NaCl, or atorvastatin, as well as several control strains (black), were grown in isogenic cultures. Relative growth values were calculated for either 300 OD measurements (doxorubicin) or 150 OD measurements (FK506, atorvastatin, and NaCl) and compared to ratios derived from the pooled experiment. Several PCA strains (indicated in the legend) with enhanced or diminished growth rates identified in the pool screen in NaCl, atorvastatin, hydrogen peroxide, and doxorubicin were grown in selective media in the presence of multiple concentrations of the four agents (indicated on the x-axis of each plot). The strains that were identified in the pool assay in each condition are marked with an asterisk. Growth relative to that in the standard environment is indicated on the y-axis. Download figure Download PowerPoint We clustered dynamic interactions based on their pattern of change across environments. We observed several subnetworks that shared a common protein (Fig 1B), suggesting that control of the common protein may have led to concerted changes in binary complexes containing that protein. For example, Fmp45 was a member of several binary complexes that accumulated during respiratory growth (using ethanol as a carbon source) and under heat and high-salt stress (Fig 1B). Similarly, the addition of hydrogen peroxide led to the accumulation of binary complexes containing the Ftr1 protein, a high-affinity iron permease (Stearman et al, 1996). Finally, growth in methionine-supplemented media led to the depletion of binary complexes containing the methionine permease Mup1. This cluster also contained other proteins important for methionine metabolism, underscoring that the function of responsive complex components was logically connected to environmental change. We observed many binary complexes that changed between conditions, but interestingly, within each condition there was a comparable number of accumulated and depleted binary complexes (Fig 1C and Dataset EV2). Conditions that induced the most widespread changes were the shift from fermentation to respiration (i.e., dextrose to ethanol), the addition of amino acids to the media, and growth at high temperature, while the addition of bioactive small molecules such as FK506 and atorvastatin resulted in a much smaller number of altered binary complexes (Fig 1C). These results may be explained by the fact that pharmaceuticals are often selected for specificity (i.e., to minimize off-target and side effects). In contrast, metabolic shifts or exposures to abiotic stress factors can induce widespread cell-physiological effects (Gasch et al, 2000) and regulatory responses that may have evolved given frequent exposure to similar environments in the evolutionary history of yeast (Gasch & Werner-Washburne, 2002; Gasch, 2007). Contrary to this pattern, the pharmaceutical agent doxorubicin resulted in many protein interaction changes (Fig 1C), possibly due to widespread non-specific effects of DNA damage and corresponding induction of a DNA damage response (Westmoreland et al, 2009). Dynamic binary complexes exhibit condition-dependent functional trends About half (757) of the binary complexes tested were dynamic in at least one condition. Most (672) of these dynamic binary complexes were found to be specific to a few (from one to three) conditions, while only 86 binary complexes were frequently dynamic (exhibiting change in four or more conditions; Fig 2A). To assess functional trends among gene products participating in frequently dynamic binary complexes, we used the FuncAssociate web server (Berriz et al, 2003; Berriz et al, 2009, see Materials and Methods) to determine over-represented Gene Ontology (GO) terms. This revealed that gene products involved in frequently dynamic binary complexes were enriched for plasma membrane localization (q < 1e-04), and for active transmembrane transporter activity (q = 0.012; Dataset EV3). Among the proteins involved in frequently dynamic bin
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