Dissecting DNA damage response pathways by analysing protein localization and abundance changes during DNA replication stress

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Dissecting DNA damage response pathways byanalysing protein localization and abundancechanges during DNA replication stressJohnny M. Tkach1,2, Askar Yimit1,2, Anna Y. Lee2,3, Michael Riffle4, Michael Costanzo2,3, Daniel Jaschob4,Jason A. Hendry1,2, Jiongwen Ou1,2, Jason Moffat2,3, Charles Boone2,3, Trisha N. Davis4, Corey Nislow2,3

and Grant W. Brown1,2,5

Relocalization of proteins is a hallmark of the DNA damage response. We use high-throughput microscopic screening of the yeastGFP fusion collection to develop a systems-level view of protein reorganization following drug-induced DNA replication stress.Changes in protein localization and abundance reveal drug-specific patterns of functional enrichments. Classification of proteinsby subcellular destination enables the identification of pathways that respond to replication stress. We analysed pairwisecombinations of GFP fusions and gene deletion mutants to define and order two previously unknown DNA damage responses. Inthe first, Cmr1 forms subnuclear foci that are regulated by the histone deacetylase Hos2 and are distinct from the typical Rad52repair foci. In a second example, we find that the checkpoint kinases Mec1/Tel1 and the translation regulator Asc1 regulateP-body formation. This method identifies response pathways that were not detected in genetic and protein interaction screens,and can be readily applied to any form of chemical or genetic stress to reveal cellular response pathways.

Cells detect and respond to changes in their environment in a numberof ways. Perhaps the best studied of these are changes in genetranscription1, protein abundance2,3 and protein modification4,5, all ofwhich have been subjected to genome-scale analysis. Cells also regulatethe intracellular localization of proteins to accommodate differentenvironmental conditions, but this form of regulation has not beenanalysed systematically.The DNA damage response consists of transcriptional, translational

and post-translational facets, and several lines of evidence suggestthat post-translational regulation is particularly important. At thesingle-gene level, there is little if any correlation between transcriptionalregulation in response to DNA damage and requirement for drugresistance6–8. Likewise, blocking messenger RNA translation doesnot prevent cells from completing S phase when challenged withthe replication inhibitor hydroxyurea, nor does it affect cell viabilityafter hydroxyurea treatment9,10. Critical roles of phosphorylation-,ubiquitylation- and sumoylation-dependent signalling in the DNAdamage response have been well characterized11–13. Together, these

1Department of Biochemistry, University of Toronto, 1 King’s College Circle, Toronto, Ontario M5S 1A8, Canada. 2Donnelly Centre for Cellular and BiomolecularResearch, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada. 3Banting and Best Department of Medical Research and Department ofMolecular Genetics, University of Toronto, Toronto, Ontario M5G 1L6, Canada. 4Department of Biochemistry, University of Washington, 1705 NE Pacific Street, Seattle,Washington 98195, USA.5Correspondence should be addressed to G.W.B. (e-mail: [email protected])

Received 13 March 2012; accepted 29 June 2012; published online 29 July 2012; DOI: 10.1038/ncb2549

data suggest that post-translational regulation of existing proteins playsa paramount role in the DNA damage response.Regulated protein relocalization is a hallmark of the cellular response

to genotoxic drugs that cause DNA damage or DNA replication stress.In yeast, DNA damage response proteins, including the single-strandedDNA binding complex replication protein A, the double-strandDNA break processing complex MRX (Mre11–Rad50–Xrs2), theDNA damage sensor Ddc2 and proteins involved in homologousrecombination, relocalize from a diffuse nuclear distribution to formsubnuclear foci in cells treated with genotoxic drugs14,15. In thecase of the recombination protein Rad52, these foci co-localize withinduced double-stranded breaks, suggesting that they represent centresfor DNA repair15. Other localization changes occur including therelocalization of the small ribonucleotide reductase subunits to thecytoplasm16. Some aspects of the regulated localization of DNA repairproteins to subnuclear foci are conserved, as replication proteinA, the Ddc2 homologue ATRIP, and recombination proteins formfoci in response to DNA damage in both yeast and human cells15.

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Mutations that disrupt phosphorylation of H2AX, or delete theubiquitin interacting domains of Rad18 or Polη, specifically disruptthe accumulation of repair proteins at nuclear foci and render cellssensitive to DNA damaging agents17–20, highlighting the importance ofthis post-translational regulation.Despite the frequent occurrence, conservation, and importance

of protein localization changes in response to DNA damage, theyhave not been examined systematically in any organism. We usedhigh-throughput microscopic analysis of the green fluorescent protein(GFP)-tagged yeast open reading frame (ORF) collection to definethe total proteome localization and abundance changes that occur inresponse to drug-induced DNA replication stress, and to identify DNAdamage response modules. When combined with high-throughputgenetic interaction methods the approach identifies and orders DNAdamage response pathways. This method is readily applicable to anychemical or genetic stress in which the relocalization of proteins issuspected to play a role.

RESULTSGlobal changes in protein abundance and localization followingDNA replication stressWe imaged each strain of the yeast GFP collection in the absenceof perturbation and in the presence of hydroxyurea or methylmethanesulphonate (MMS) to determine the spectrum of yeastproteins that undergo localization or abundance changes in responseto replication stress (Fig. 1a). Hydroxyurea slows DNA replication byinhibiting ribonucleotide reductase and limiting 2′-deoxynucleoside 5′-triphosphate pools21, whereasMMS is an alkylating agent that results ina lesion that cannot be bypassed by the replicative DNA polymerases22.Following drug treatment, we observed phosphorylation of histone 2ASer 129 and Rad53, upregulation of Rnr3 and accumulation of cellsin S phase, all of which indicate that the DNA damage response wasactivated23–25 (Supplementary Fig. S1). A total of 74,664 images werecollected, and raw image files are available from the Yeast ResourceCenter Public Image Repository (http://images.yeastrc.org/tkach_brown/replication_stress). To identify proteins that changed inabundance after drug treatment we used a CellProfiler26 analysispipeline to determine the fluorescence intensity in images of controland drug-treated cells (Supplementary Table S1). We compared thecontrol intensities to the single-cell-based fluorescence measurementsof the same GFP-fusion collection grown in minimal medium27 andfound a significant positive correlation (r = 0.890,p< 2.2× 10−16,Supplementary Fig. S2a) indicating the robustness of our abundancemeasurement method. Fluorescence intensities were converted toZ scores relative to the control based on the median of the intensitymeasurements (Fig. 1b), and cutoffs of −2 and 2 (corresponding totwo median absolute deviations from the control median value) wereapplied to identify strains that deviated significantly from the control.We scored localization changes by visual inspection of images,

reasoning that some changes might be unanticipated and thereforedifficult to score computationally. Ten major localization changeclasses and several minor ones, each representing two proteins or less,were identified (Fig. 1c and Supplementary Table S2). To assess theaccuracy of our subcellular localization designations, we comparedour localization calls in unperturbed cells for 323 strains with thosepreviously reported28. The primary localizations for 89%of the proteins

tested matched those from ref. 28 whereas only 8% differed, indicatingthat ourmanual inspection was of high quality. In addition, we assignedlocalizations to 3% of proteins that were previously characterized as‘ambiguous’ (Supplementary Table S3). To assess the reproducibilityof the localization analysis, we rescreened 252 of the 254 strains thatshowed a protein localization change in response to drug in the primaryscreen (Supplementary Table S4). Of these, 74% were positive in thehydroxyurea rescreen and 78%were positive in theMMS rescreen.A global view of the protein abundance and localization changes

induced by replication stress is shown in Fig. 1d. In total, 254 proteinsunderwent one ormore localization changes and 356 proteins increasedin abundance in response to drug treatment. Abundance changes weremore prevalent in MMS than in hydroxyurea (Figs 1d and 2a), andonly 35 proteins showed both localization and abundance changes(Fig. 2b). In total, 575 proteins changed localization or abundancefollowing hydroxyurea or MMS treatment, representing 14% ofthe proteins screened.

Analysis of protein dynamics reveals chemical-specificfunctional enrichmentsThe sets of proteins identified by localization and abundance changesare largely non-overlapping (Fig. 2b) and thus represent differentkinds of cellular response. Furthermore, the proteins identified inMMS differed from those in hydroxyurea, particularly in abundancechanges (Fig. 2a,c), and so might represent useful signatures todistinguish chemical agents. Enrichment analysis revealed thatbiological processes and protein complexes enriched in the abundancechange classes (Fig. 3a,b) were distinct from those in the localizationchange classes (Fig. 3c,d and Supplementary Fig. S3a,b). Abundancechanges identified functions reminiscent of a global stress response,including iron homeostasis for hydroxyurea and oxidative stressresponse for MMS. Interestingly, hydroxyurea causes loss of ironfrom the ribonucleotide reductase active site29 and hydroxyurea isknown to interfere with iron homeostasis in mammalian cells30,whereas MMS depletes mammalian cells of reduced glutathione31 andinduces genes involved in cellular redox homeostasis in yeast8. Bycontrast, localization changes in MMS were enriched for functionswith more obvious connections to the response to genotoxic stress,including cell cycle regulation, cell cycle checkpoint and DNA repair(Fig. 3d). Despite the large overlap between proteins that relocalizein hydroxyurea and those that relocalize in MMS (Fig. 2c), theenrichments remain specific for each agent (Fig. 3c,d). Finally, we findan unanticipated enrichment for mRNA decapping proteins in thehydroxyurea localization category (Fig. 3c). These data indicate thatprotein abundance is regulated differently from protein localization,and so each probably carries out distinct cellular roles in the responseto hydroxyurea and MMS. Furthermore, the enrichments we observeare specific to each chemical’s mechanism of action, and suggest thatcomprehensive chemical screening by this method could produceuseful agent-specific signatures.

Protein localization or abundance changes correlate poorly withreplication stress resistanceGenes that are transcriptionally upregulated in response to DNAdamaging agents do not correspond to those that are required fordrug resistance6–8. Consistent with the lack of overlap between MMS

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a b

DNA replication/repair/segregation

Chromatin/transcription

Cell polarity/morphogenesis

Golgi/endosome/vacuole/sorting

RNA processing

Cell cycle

Drug/ion transport

Fatty acid/lipid/sterol biosynthesis

Metabolism/mitochondria

Nuclear-cytoplasmic transport

Protein degradation

Protein folding/glycosylation/cell wall biogenesis

Ribosome/translation

Signalling/stress response

Unknown

d

HU MMS

4148 MATa XXX–GFP

NUP49–mCherry

Image

Computational analysis for abundance

Visual analysis for localization

HU

Z-s

co

re

MMS

c

From budneck (Skg3–GFP):

Nuclear foci (Dpb11–GFP):

Cytoplasmic foci (Lsm1–GFP): Cytoplasm (Rnr4–GFP):

Nuclear periphery (Mtr10–GFP):

MMS

HU MMS

Nucleus (Msn2–GFP):

HU

HU

Nucleolus (Nop13–GFP):

HU

HU

MMS

HU

Vacuole (Fcy2–GFP): ER foci (Lag1–GFP):

Plasma membrane (Lag1–GFP):

83 312

Abundance change

Localization change

MMS

HU

MMS

Control

25

0

–5

5

10

15

20

Z-s

co

re

20

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–5

5

10

15

Proteome Proteome

Figure 1 High-throughput microscopic screening of yeast GFPcollection. (a) Schematic representation of screening methodology.HU, hydroxyurea. MMS, methyl methanesulphonate. (b) Rank-orderplots of Z score for each protein screened (collectively designated asproteome) for abundance change measurements in hydroxyurea (left)and MMS (right). Red lines indicate Z score cutoffs (−2 and 2). Proteinswith Z scores exceeding the cutoffs are coloured red. The number ofproteins with Z> 2.0 is indicated. (c) High-throughput images ofrepresentative proteins for ten relocalization classes. Left and right

panels in each pair show control and drug-treated samples respectively.Green, GFP fusion; red, Nup49–mCherry. Note that Nup49–mCherryis not shown for Mtr10–GFP to show its localization at the nuclearperiphery. ER, endoplasmic reticulum. Scale bar, 5 µm. (d) Networksummary of screen hits. Positives from the screen were organized basedon type (abundance or localization) and inducing drug. Nodes representproteins and are coloured by biological process. Red edges indicateabundance changes with edge width proportional to the magnitude ofchange. Blue edges indicate localization changes.

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a

b

c

Abundance: HUMMS

312

83

39273 44

Response to oxidative stress; response to stress

Iron ion transport; trehalose biosynthesis; carbohydrate catabolism

356Cellular homeostasis; response to oxidative stress; response to stress

P = 4.47 × 10–24

Trehalose biosynthesis

Iron ion transport; iron ion homeostasis

No significant terms

Relocalization Abundance

35

P = 0.00149

321219

254

356

575Response to stimulus; cellular response to stimulus; homeostatic process

Cellular homeostasis; response to oxidative stress; response to stress

Regulation of cell cycle; cell cycle; mRNA decapping

Actin filament depolymerization

Cell cycle; regulation of cell cycle, DSB repair

Cellular homeostasis; response to oxidative stress

HUMMS

108

P = 7.57 × 10–116

6581

189

173

Relocalization:

Cellular response to stress; DNA

metabolic process; response to stimulus

mRNA decapping

Cell cycle; regulation of hydrolase activity;

mitotic cell cycle

Cell cycle; DSB repair; regulation of cell cycle

mRNA decapping; regulation of cell cycle; cell cycle

254Regulation of cell cycle; cell cycle; mRNA decapping

Figure 2 Comparison of biological process enrichment for MMS andhydroxyurea (HU) abundance and localization positives. Venn diagramssummarizing overlap among abundance and localization positives. In allpanels the number of genes in each group, enriched Gene Ontology terms(Methods) and a P value for the significance of the overlap are indicated.(a) MMS versus hydroxyurea abundance positives. (b) All relocalizationpositives versus all abundance positives. (c) MMS versus hydroxyurearelocalization positives.

sensitivity and mRNA abundance changes6,7, the overlap betweenMMS sensitivity (Supplementary Table S5) and protein abundancechange was insignificant (Supplementary Fig. S4a). Similarly, therewas little overlap between hydroxyurea sensitivity and proteinabundance or localization changes (Supplementary Fig. S4b,d). Last, acomparison of protein abundance and localization changes and genesidentified in screens for chromosome instability32 and increased Rad52focus formation33 did not reveal large overlaps among the datasets(Supplementary Table S6). We anticipate that drug-induced proteinlocalization changes and genetic requirements for drug resistance and

genome instability phenotypes are not strongly predictive of eachother owing to considerable redundancy in replication stress resistance.This notion is supported by DNA-damage-induced epistasis studies inwhich 379 double mutants exhibited greater MMS sensitivity than thecorresponding single mutants34.

Protein destination identifies DNA replication stress responsemodulesWe identified ten major classes of protein localization changes (Fig. 1c).Nine reflect a protein destination and one reflectsmovement away fromthe bud neck or bud tip. There was significant overlap between thelocalization changes in hydroxyurea and those in MMS (108 proteinsrelocalize in both drugs, Fig. 2c), and those that relocalized in bothdrugs moved to the same destination 98% of the time. To arrive ata dynamic view of protein localization changes, we compared thelocalizations of all proteins that move in response to hydroxyureaor MMS, before and after drug treatment (Fig. 4a). We found similarpatterns of relocalization in hydroxyurea and MMS, with the mostpopulated changes being a reduction in diffuse nuclear localization,increases in localization to the cytoplasm, to cytoplasmic foci andto nuclear foci, and a decrease in localization to the bud neck andbud tip. Closer examination of proteins that had reduced diffusenuclear distribution revealed that the reduction was due in part tothe recruitment of 24 nuclear proteins into subnuclear foci (Fig. 4b), awell-known response to DNA damage and replication stress. However,there was also an export of 33 proteins from the nucleus to thecytoplasm and to cytoplasmic foci that contributed to the reductionin nuclear localization (Fig. 4b). Import of proteins to the nucleustypically involved further nuclear enrichment of proteins that werelocated in both the cytoplasm and nucleus, indicating a change in netnuclear import (Fig. 4b). Recruitment of proteins to foci, in either thecytoplasm or the nucleus, most commonly reflected movement from adiffuse localizationwithin the same compartment (Fig. 4b).There was significant enrichment of biological processes within six of

the ten localization change classes (Supplementary Table S7), indicatingthat the classes might represent biological pathways important forthe replication stress response. In particular, we focused on thenuclear focus and cytoplasmic focus localization classes. Localizationto nuclear foci is a classic DNA damage response15 and so this classmight contain uncharacterized response proteins. The nuclear focusclass was highly enriched for the Gene Ontology term ‘DNA repair’(p = 2× 10−14; Fig. 4c and Supplementary Fig. S5), with 16 of 28proteins in the class annotated with this term. We mined existingdatabases to determine the extent of genetic interactions among the 28genes encoding nuclear focus proteins. This analysis revealed a strongenrichment for interactions (p= 1.9×10−14; Fig. 5a,e), indicating thatproteins that share the same localization following replication stressare more likely to share functional biological connections. This furthersuggests that biological function can be assigned based on relocalizationbehaviour. For example, of three poorly characterized genes in thenuclear focus class, one of them, CMR1, has extensive genetic andphysical interactions with other DNA repair genes and proteins inthe class (Fig. 5a,b).Localization to cytoplasmic foci following replication stress was an

unanticipated localization change. This class had a striking enrichmentfor mRNA catabolism processes, particularly mRNA decapping

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a b

c

Site of polarized growth

mRNA decapping

DNA repair

Cell cycle checkpoint

Regulation of molecular function

Regulation of catalytic activity

Site of polarized growth

Small nuclear ribonucleoprotein particle U6

Mitotic spindle

Polysaccharide biosynthetic

process

Regulation of ubiquitin–protein ligase activity

Cellular iron ion homeostasis

NADPH regeneration

Iron ion transport

Metal ion transmembrane

transporter activity

Monosaccharide metabolic process

Response to oxidative stress

Cellular response to oxidative stress

Cytosolic proteasome

complex

Monosaccharide catabolic process

Hydroxyurea abundance

Hydroxyurea localization

MMS abundance

MMS localization

Cell redox homeostasis

Antioxidant activity

Regulation of cell cycle

Regulation of cell cycle process

d

Figure 3 Abundance and relocalization positives show drug-specificbiological process enrichment. (a,b) Gene set enrichment analysiswas carried out on protein groups showing abundance changes inhydroxyurea (a) or MMS (b). NADPH, nicotinamide adenine dinucleotidephosphate. (c,d) Enrichment analysis using the hypergeometric methodwas used to identify enrichments in protein groups showing localizationchanges in hydroxyurea (c) or MMS (d). Significant terms with a false

discovery rate less than 0.01 are shown. Each node represents asingle enriched biological process/protein complex and is coloured bybiological process as in Fig. 1d. Node size is proportional to prevalenceof the Gene Ontology term in the GFP strain collection and edge widthis proportional to the degree of gene overlap between two nodes. Somenode names within a group were replaced with a general term for clarity.All node names are shown in Supplementary Fig. S3.

(p= 2.6×10−16; Fig. 4c and Supplementary Fig. S5). Mining physicalinteractions among the 41 proteins in the cytoplasmic focus classrevealed a highly connected network of interactions, with a 6-foldhigher interaction density than expected by chance (p= 9.9×10−16;Fig. 5d,e). Inspection of the proteins involved revealed that most arecomponents of cytoplasmic mRNA processing bodies (P-bodies) thatform when excess non-translating mRNAs are present35, indicating afunctional link betweenDNA replication stress andmRNAprocessing.

CMR1 defines a previously unknown class of replicationstress fociWe first mined existing data to identify biological processes connectedto CMR1. The genetic interaction similarity profile and physicalinteraction networks for CMR1 were enriched for DNA repair andhomologous recombination processes, respectively (p= 1.4× 10−3

for DNA repair and p= 2.3×10−4 for homologous recombination;Fig. 6a). To systematically explore these functional enrichments weundertook a synthetic genetic array (SGA) analysis36 of CMR1. Thenegative CMR1 genetic interactions defined in this screen revealedenrichment for recombinational repair (p= 3.1×10−4; Fig. 6b and

Supplementary Table S8).We found that H2A Ser 129 phosphorylationincreased almost twofold in cmr11 cells treated with MMS (Fig. 6c),consistent with a role for Cmr1 in preventing DNA damage duringexposure to replication stress.Proteins in the nuclear focus localization class share common

functions and genetic and physical interactions, suggesting a functional‘neighbourhood’ that could be mined for regulatory relationships.We first imaged mini-arrays of the 27 nuclear focus strains as GFPfusions deleted for CMR1 and identified a positive regulator of Cmr1focus formation, the deacetylase Hos2, and a negative regulator, themolecular chaperone Apj1 (Fig. 6d and Supplementary Fig. S6a).By carrying out the reciprocal experiment and imaging Cmr1–GFPstrains with the 24 non-essential members of its neighbourhooddeleted we found that Cmr1 suppressed the ability of Apj1 andthe phosphatase Pph21 to form foci (Fig. 6e and SupplementaryFig. S6b). Finally, we interrogated the relationships among Hos2,Apj1 and Pph21 (Supplementary Fig. S6b), determined that Pph21focus formation requires Apj1 and ultimately defined the pathwaythat regulates DNA-damage-induced focus formation among thisgroup of proteins (Fig. 6f).

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a

b

c

Number of proteins

HUUntreated

Number of proteins

To cytoplasmic foci To nuclear foci

DNA repair

Cell cycle/ubiquitinationRegulation of translation

Cell polarity

From bud neck/tipTo cytoplasm To nucleus

75

25

5

Other

Nucleus Bud neck/tip

Cytoplasm

Mitochondria

Vacuole

PM

Cytoplasmic foci

Cytoplasm

Cytoplasm*

Nuclear foci

Other

Nucleus

Cytoplasm Cytoplasm

Cytoplasmic foci* Nucleus

Cytoplasmic foci

Nucleus

Other

PM

Number of proteins:

MMSUntreated

Vacuole

Mitochondria

Plasma membraneNucleus

Nucleolus

Nuclear fociNuclear periphery

ER fociER

Cytoplasmic fociCytoplasm

Bud neck/tip

0 20 40 60 0 20 40 60 80 100

To nuclear foci From bud neck/tipTo nucleus To cytoplasmic fociCell cycle checkpointMitotic spindle mRNA decapping Signal transduction

Cytoplasm

Nuclear foci

Nuclear foci*

Bud neck/ tip

Nucleus

Bud neck/tip

Figure 4 Global analysis of protein relocalization in response to replicationstress. (a) The number of proteins in each subcellular compartment before(blue) and after (red) drug treatment. HU, hydroxyurea. ER, endoplasmicreticulum. (b) Relocalization maps illustrate the initial subcellular locationsof proteins that contribute to each indicated relocalization class. Thenode size is proportional to the number of proteins (the scale is indicatedon the left). For ‘To Nucleus’, proteins designated Cytoplasm* showed a

nuclear–cytoplasmic distribution before drug treatment, with the proportionof protein in the nucleus increasing after drug. For ‘To Cytoplasmic Foci’and ‘To Nuclear Foci’, Cytoplasmic Foci* and Nuclear Foci* represent initialsubcellular locations where the number of cells with foci or the intensity ofthe foci increased after drug. (c) Functional enrichment analysis of indicatedrelocalization classes. See Fig. 3 legend for details. All node names areshown in Supplementary Fig. S5.

Hos2 and Cmr1 foci co-localize (Fig. 6g), suggesting they arerecruited to the same structures. Although we noted that the proteins inthe Cmr1 pathway formed foci with a distinctive perinuclear location(Fig. 6d,e), these foci did not co-localize with the ribosomal DNA(Fig. 6i), nor did they co-localize with the canonical DNA repair focusmember Rad52 (Fig. 6h). Thus, Cmr1, Hos2, Apj1 and Pph21 define adistinct subnuclear DNA damage response focus.

Asc1 and Mec1/Tel1 regulate P-bodies induced by replicationstressThe cytoplasmic foci formed following replication stress, particularlyhydroxyurea, were reminiscent of P-bodies, and all known P-body components in our screen formed these foci in hydroxyurea(Supplementary Fig. S7a–c). The cytoplasmic foci formed by twoP-body components, Lsm1 andDhh1, either co-localized or were foundadjacent to each other after hydroxyurea treatment, consistent with theknown distribution of P-body markers35 and indistinguishable from

their co-localization after a combination of two typical P-body inducers,osmotic and glucose deprivation stresses35 (Fig. 7a). Deletion of twogenes,PUB1 andTIF4632 (ref. 37), which are required for the formationof cytoplasmic stress granules, had no effect on hydroxyurea-inducedLsm1 focus formation (Fig. 7b and Supplementary Fig. S8a). Weconclude that the cytoplasmic foci that form in response to DNAreplication stress are P-bodies.Combining microscopic screening with SGA analysis is a powerful

means of identifying the complement of genes that regulate thesubcellular localization of a given protein38,39. We used SGA (ref. 36)to cross Lsm1–GFP into the non-essential gene deletion collection andimaged control and hydroxyurea-treated cultures. These 86,016 rawimages are also available from the Yeast Resource Center Public ImageRepository (http://images.yeastrc.org/tkach_brown/replication_stress).Positives were re-imaged after treatment with hydroxyurea or water(Fig. 7c). We found that PAT1 and EDC3 are required for Lsm1 P-bodyformation in response to osmotic stress/starvation, consistent with their

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LSB1TUB1

CSM3

SLX4

RAD50

RPS18AMRE11

DDC2

RTT107

CMR1

DNA2

SLX8

SGS1

DPB11

DDC1

Nuclear foci, protein–protein interactions:

c d

To cytoplasmic foci To nuclear foci From bud neck/tipTo cytoplasm To nucleus

Enrichment P value

Enrichment P value

Genetic interactions

Protein–protein interactions

LSM1LSM7

PAT1

TSA1 PBY1

NAM7

LSM3

LSM4

PRS5

EDC2

BCH1

DCP2

HSP104

PEX21

ATG16SGT2

EDC1 SCD6

NMD4LAP4

YHB1

SIP5RTR2

EDC3

GSY1

YIL108W

GCD6

DHH1

DCP1

5.0-fold

P = 1.5 × 10–14

6.3-fold

P = 1.9 × 10–14

0.7-fold P = 0.96

1.2-fold P = 0.02

LSM1LSM2

PAT1

LSM4HSP42

TSA1

LSM3

HSP104

GCD6

LAP4

DHH1

YHB1

MKT1

LSM7

GSY1

SGT2EDC3

GLN1

FAA4NAM7

DCP1

BCH1

SCD6DCP2

PBY1

PNC1

EDC2

EDC1

NMD4

YAR009C

YLR126C

CSM3

SLX8

HOS2

RAD50

SGS1

DDC1

MRE11

RAD54

RTT107SLX4

RAD5

RAD59

PSO2DPB11

APJ1

MGS1

CMR1DNA2

DDC2

a

b e

2.0-fold

P = 1.4 × 10–6

2.4-fold

P = 9.3 × 10–4

2.8-fold

P = 5.5 × 10–5

0.6-fold P = 0.98

1.4-fold P = 0.005

6.0-fold

P = 9.9 × 10–16

Cytoplasmic foci, genetic interactions: Cytoplasmic foci, protein–protein interactions:Nuclear foci, genetic interactions:

Figure 5 Relocalization change classes are enriched for protein–proteinand genetic interactions. (a–d) Genetic and physical interaction networksfor the indicated relocalization classes were generated using GeneMANIA.Nodes represent genes/proteins and edges represent interactions. All

nodes are coloured by biological process as in Fig. 1d. (e) Summary ofinteraction enrichments for the given relocalization classes. P valuescalculated using the hypergeometric method. See Methods for details ofanalysis.

documented roles in this response40,41. Both genes were also requiredfor P-body formation in hydroxyurea, suggesting that these proteinsmight control P-body formation in response to diverse stimuli. Deletionof the gene encoding Lsm1 complexmember Lsm6 reduced, but did notblock, Lsm1–GFP focus formation, and is consistent with LSM complexmembers contributing to P-body assembly during glucose starvation40.Of particular interest, we found that the translation regulator Asc1is required for P-body formation specifically in hydroxyurea (Fig. 7cand Supplementary Fig. S8a). This indicates that the formation ofP-bodies in hydroxyurea is not a general stress response, as it isregulated in a manner that is distinct from P-body formation followingosmotic stress/starvation.Pat1 is a central regulator of P-body formation in the canonical

glucose deprivation pathway42, and is itself a component of P-bodies35.Pat1 foci formed in water were unaffected by ASC1 deletion, butcompletely failed to form in hydroxyurea (Fig. 7d). Thus, Asc1is upstream of Pat1 in a hydroxyurea-specific branch of the P-body pathway (Fig. 7g). The key components of the hydroxyurea-induced P-body assembly pathway, Pat1, Lsm1 and Asc1, are allencoded by genes that confer hydroxyurea sensitivity when deleted(Fig. 7e and Supplementary Fig. S7b), connecting this response tohydroxyurea resistance.The checkpoint kinases Mec1and Tel1 are critical regulators of the

response to DNA replication stress11. To test a connection betweenP-body formation in hydroxyurea and the checkpoint response, wedeleted MEC1 and its homologue TEL1 and assessed the effect onP-body formation. Surprisingly, P-body formation, as measured byboth Lsm1 and Pat1 foci, increased in the absence of Mec1 and Tel1,even in untreated cells, indicating that the checkpoint kinases arerepressors of P-body formation (Fig. 7f,g and Supplementary Fig. S8b).We propose that activation of Mec1 in response to hydroxyurea, either

directly or indirectly, relieves this repression, enabling Asc1 to activatePat1 and, subsequently, P-body formation.

DISCUSSIONHydroxyurea andMMS are commonly used to induce replication stressand DNA damage in yeast. Despite the clear effect of hydroxyurea onDNA replication (Supplementary Fig. S1b), proteins involved in theDNA damage response were not significantly enriched in either theabundance or localization change hydroxyurea categories, probablyowing to the lack of DNA damage in hydroxyurea-treated cells. Wedid not detect a significant increase in Ddc2 foci, which is a commonproxy for DNA damage43,44, during hydroxyurea treatment, consistentwith previous reports45 and consistent with the absence of hydroxyurea-inducedDNA damage in cells that have an intact checkpoint46. Proteinsinvolved in iron transport were enriched in the hydroxyurea abundancehits and could counteract the loss of iron at the catalytic ribonucleotidereductase subunit29 as has been suggested by transcriptome analysis47.Notably, this suggests that disrupting iron transport might augmentthe chemotherapeutic efficacy of hydroxyurea.MMS treatment causes multiple DNA alkylation adducts, includ-

ing an N3-deoxyadenosine lesion that inhibits DNA polymeraseelongation22. The MMS localization change category showed robustenrichment of DNA repair and checkpoint genes, consistent with itsmajor mode of action and distinguishing the MMS response fromthe hydroxyurea response. Consistent with MMS increasing cellularreactive oxygen species48, we also see a strong enrichment for oxidativestress response processes in the MMS abundance change category.Together, our results indicate that there is considerable specificity in thefunctional enrichments, both for different agents and for localizationversus abundance changes. This points to the usefulness of microscopicscreening to characterize the biological properties of drugs.

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DNA replication & repair

Protein folding

Unknown

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Other

Golgi/ endosome/ vacuole/ sorting

Ribosome/ translation

Metabolism/ mitochondria

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Physical interactionsGenetic profilesDNA replication/repair

AS

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ASE1

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RFA2SNZ2

IMD2DID2

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Rad52–GFPCmr1–mCherry Mergeh ig

Figure 6 Cmr1 represents a distinct class of DNA damage responsefoci. (a) CMR1 was used as the query in GeneMANIA to generate anetwork of 20 genes with highly correlated synthetic genetic profiles (left)and a network of ten physically interacting proteins (right). The size isproportional to the degree of connectivity within the network and theedge width is proportional to the confidence of the connection. The greynodes represent the query ORF (CMR1) and the white nodes representthe ORFs returned by GeneMANIA. Nodes representing ORFs returned byGeneMANIA that function in DNA repair are coloured red. (b) SGA networkfor CMR1 negative genetic interactions. Nodes represent genes, and thoseconnected by two edges indicate that the interaction was detected usingCMR1 as both a query and an array strain. Nodes are coloured by biologicalprocess as in Fig. 1d. (c) Western blot analysis for H2A-P. The indicatedstrains were arrested in G1 (Alpha), released into MMS for 1 h and left

to recover in fresh yeast extract peptone dextrose (YPD) for 1 h. Celllysates were probed for H2A-P and total H2A. The cmr11 strain showsa 1.7-fold increase in H2A-P signal when compared with wild type afternormalizing to total H2A. (d) Live cells expressing Cmr1–GFP and withthe indicated gene deleted were imaged by confocal microscopy before(Control) or after MMS treatment. (e) Live cells expressing the indicatedGFP-fusion protein and with CMR1 deleted were imaged by confocalmicroscopy before (Control) or after MMS treatment. (f) Model of thepathway regulating Cmr1 focus formation. (g,h) Live cells co-expressingCmr1–mCherry and Hos2–GFP (g) or Rad52–GFP (h) were imaged before(Control) and after MMS treatment. (i) Live cells co-expressing Cmr1–GFPand the nucleolar marker Nop56–mCherry were imaged before (Control)and after MMS treatment. Scale bars, 5 µm. Uncropped images of blotsare shown in Supplementary Fig. S9.

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a

Lsm

1D

hh1

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ild t

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Figure 7 P-body formation in response to hydroxyurea is regulated byASC1, MEC1 and TEL1. (a) Live cells co-expressing chromosomally taggedLsm1–GFP and Dhh1–mCherry were imaged by confocal microscopy before(Control) and after treatment with hydroxyurea (HU) or water. (b–d) Livecells expressing Lsm1–GFP (b,c) or Pat1–GFP (d) and with the indicatedgene deleted were imaged by confocal microscopy before (Control) and aftertreatment with hydroxyurea or water. (e) Cultures of the indicated strainswere serially diluted and spotted on YPD and YPD containing 200mMhydroxyurea and grown for 2–3 days. (f) Wild type cells or strains withMEC1 and TEL1 deleted (mec11tel11) expressing either Lsm1–GFP (left)or Pat1–GFP (right) were imaged by confocal microscopy before (Control)and after treatment with hydroxyurea. (g) Regulation of P-body formation inresponse to hydroxyurea-induced replication stress. Scale bars, 5 µm.

The functional enrichments evident in our data are differentfrom those observed when the yeast genome was screened forhydroxyurea- or MMS-sensitive mutants (for example, see refs 7,49). Compiling all hydroxyurea-sensitive genes from SGD yields apotent enrichment for DNA damage response, DNA repair and stressresponse, but does not reveal the enrichment for iron homeostasis.Similarly, the striking enrichment of mRNA decapping processes inthe hydroxyurea localization response is not evident in the group

of hydroxyurea-sensitive strains. Thus, analysis of protein dynamicsaffords a viewof cellular response that is not captured by othermethods.

Post-transcriptional regulation in the response to MMSComparison of the protein abundance changes that occur duringMMS treatment with their corresponding mRNA changes8 yielded apositive correlation for the top 300 abundance changes (r = 0.457;Supplementary Fig. S2b), indicating that mRNA changes accountfor 21% of the variance in protein abundance changes. Thus, manyincreases in transcript levels did not result in corresponding proteinchanges, and including the entire set of proteins analysed resultedin a poorer correlation (r = 0.281; Supplementary Fig. S2d). Theseobservations suggest that in the case of the MMS response post-transcriptional regulation is a critical determinant of the ultimatechanges in protein abundance. This contrasts with the response toosmotic shock50 and rapamycin treatment51 in yeast, where 80%and 36% of the protein abundance changes could be explained bycognate changes in mRNA abundance. It seems that the relationshipbetween mRNA abundance and protein abundance varies greatlydepending on the cell stress, indicating stress-specific roles forpost-transcriptional regulation. It is interesting in this respect thatone of the biological modules we identified in hydroxyurea regulatesmRNA translation and stability.

Identifying regulators in localization change neighbourhoodsThe high degree of biological process, genetic and physical interactionenrichment in most of the localization classes suggested that eachclass could represent a functionally connected ‘neighbourhood’ ofproteins. Consistent with this possibility, the ‘to nuclear foci’ class wasenriched for DNA repair proteins, and we connected a protein in thatclass, Cmr1, with DNA repair in several ways (Fig. 6a–c). We furtherinterrogated the nuclear focus ‘neighbourhood’ to identify regulatorsof Cmr1 focus formation. It is interesting that our analysis of 27 genesidentified three regulators, whereas in the case of Lsm1 screening theentire gene deletion collection of∼4,500 genes identified only sevenregulators (Fig. 7 and data not shown). Whereas this single case hasnot yet been extrapolated, it is tempting to speculate that localizationchange categories will be enriched for regulatory relationships.We also found that analysing the co-localization of proteins within

the nuclear focus class revealed a distinct kind of sub-nuclear focusconsisting of Cmr1, Hos2, Apj1 and Pph21 that is not associated withthe canonical DNA repair foci represented by proteins such as Rad52and Ddc2. The role of these proteins in the DNA damage response isunclear, because the deletion of any one gene does not result in a strongdamage sensitivity phenotype. However, one member of this group,Cmr1, contributes to genome stability32 and was recently demonstratedto interact with ultraviolet-damaged DNA in vitro and in vivo52. Inone scenario, the chromatin remodelling activity of Hos2 might berequired to permit Cmr1 to access DNA lesions. Alternatively, as Hos2is required for the activation of DNA damage-inducible genes53, thesefoci might represent not sites of DNA damage, but rather sites ofdamage-induced transcription.

A pathway regulating cytoplasmic P-bodiesThe redistribution of proteins from a diffuse cytoplasmic distributionto cytoplasmic foci formed the most striking relocalization class in

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our screen and represents the formation of P-bodies (Fig. 7a). Wedemonstrate that replication stress is a potent inducer of P-bodyformation, suggesting that replication stress causes an increase innon-translating mRNAs, and indicating an important role for post-transcriptional regulation in the genotoxic stress response. We foundthat Asc1 is required for P-body formation in the hydroxyurearesponse, but not in response to glucose deprivation/osmotic stress,and acts upstream of the key regulator of P-body formation, Pat1.Thus, hydroxyurea induction of P-bodies is regulated differently frominduction by more classical conditions, and so forms a distinct branchof the P-body pathway. Both Pat1 and Lsm1 are required for resistanceto hydroxyurea (Supplementary Fig. S7d) and the topoisomerase Ipoison camptothecin49. It was recently shown that Lsm1 contributes tothe turnover of histonemRNA and that loss of this function contributesto the hydroxyurea sensitivity of lsm11 strains54. Thus, the P-bodieswe observe may in part reflect the turnover of histone mRNA inresponse to replication stress.Asc1 and its mammalian homologue RACK1 are signalling adaptor

proteins that regulate diverse cellular processes55. In addition, bothAsc1 and RACK1 are stoichiometric components of the ribosomeand are thought to recruit regulators to the ribosome to modulatetranslation55,56. It will be interesting to determine if RACK1 modulatesP-body assembly in response to replication stress in mammalian cells,and whether such a role is relevant to the upregulation of RACK1 thatis common in neoplasias55.We provide a comprehensive resource detailing the protein

abundance and localization changes that occur during replicationstress in yeast. Our data demonstrate the potential of high-throughputmicroscopic screening to identify previously unknown responsepathways and their regulators. The methodology can be readily appliedto virtually any genetic or chemical perturbation. �

METHODSMethods and any associated references are available in the onlineversion of the paper.

Note: Supplementary Information is available in the online version of the paper

ACKNOWLEDGEMENTSWe thank M. Cox and B. Andrews for assistance with high-throughput microscopy,A. Baryshnikova, and Q. Morris for advice on the data analysis and R. Tsienfor providing the mCherry fusion plasmid. This work was supported by grant020254 from the Canadian Cancer Society Research Institute to G.W.B., by grant1R01HG005853 from the National Institutes of Health, grants MOP-102629 andMOP-97939 from the Canadian Institutes of Health Research and grant GL2-01-22from the Ontario Research Fund to C.B., by grants P41 RR11823 (NCRR) and P41GM103533 (NIGMS) from the National Institutes of Health to T.N.D., by grantsfrom the Canadian Foundation for Innovation (16304) and the Ontario Institute ofCancer Research Equipment Competition (2007) to J.M., and by a grant from theNational Human Genome Research Institute to C.N.

AUTHOR CONTRIBUTIONSJ.M.T. and G.W.B. designed experiments and carried out data analysis. J.M.T.carried out the primary screen and carried out or co-ordinated experiments forFigs 6 and 7. A.Y. carried out experiments for Figs 6 and 7. A.Y.L. and C.N. carriedout GSEA and biological enrichment analysis and generated enrichment networks.M.R., D.J. and T.N.D. constructed the database of images available through theYeast Resource Center. J.M. established the high-throughput microscopy platformand provided advice on the microscopy and analysis. M.C. and C.B. co-ordinatedthe CMR1 SGA analysis. J.A.H. and J.O. carried out functional analysis of CMR1and LSM1, respectively. The manuscript was written by J.M.T. and G.W.B. withcontributions from C.N.

COMPETING FINANCIAL INTERESTSThe authors declare no competing financial interests.

Published online at www.nature.com/doifinder/10.1038/ncb2549Reprints and permissions information is available online at www.nature.com/reprints

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DOI: 10.1038/ncb2549 METHODS

METHODSStrains and media. Yeast strains used in this study (Supplementary Information,Table S9) are derivatives of BY4741 (ref. 57). Unless indicated otherwise, standardyeastmedia and growth conditionswere used58. For high-throughput screening, low-fluorescence media, yeast nitrogen base (MP Biomedicals) was supplemented with5 g l−1 ammonium sulphate, 2% (w/v) glucose and standard amounts ofmethionine,histidine, leucine and uracil58. For all other microscopy, low-fluorescencemedium containing ammonium sulphate and glucose was supplemented withstandard amounts of adenine, arginine, isoleucine, valine, histidine, leucine, lysine,methionine, phenylalanine, threonine, tryptophan, tyrosine and uracil.

Screen to identify protein abundance and localization changes in responseto replication stress. JTY7 containing a NUP49–mCherry::CaURA3 marker wascrossed with the yeast GFP collection by SGA (ref. 36). The resulting strainswere grown to saturation (∼24 h growth time) in a 96-well format and furthersubcultured tomid-log phase (∼0.3ODml−1) at 30 ◦C in low-fluorescencemedium(∼16 h growth time). Cells were transferred to a 384-well slide to a final density of0.045ODml−1 (Perkin-Elmer) and incubated at 30 ◦C for 2 h with further medium(control), 0.2M hydroxyurea (Sigma) or 0.03% methyl methanesulphonate (MMS,Sigma). Images from three areas per well in the green (405/488/640 primary dichroic,540/75 emission band-pass filter, 800ms exposure) and red channels (405/561/640primary dichroic, 600/40 emission band-pass filter, 2,000ms exposure) wereobtained using the EVOTEC Opera confocal microscope system (PerkinElmer). Allraw images are available from the Yeast Resource Center Public Image Repository(http://images.yeastrc.org/tkach_brown/replication_stress).

Localization change raw data scoring and refinement. The images wereblinded and scored manually for localization changes in drug-treated samples. Foreach protein undergoing localization change, a brief description of the proteinlocalization in control and drug-treated cells was recorded (Supplementary TableS1). For cases where the protein was present in more than one compartment, thecompartments are listed in order of phenotypic prominence; a protein located inboth the nucleus and cytoplasm but appearing more abundantly in the nucleuswould have the designation ‘Nucleus, cytoplasm’. In the case where the proteinis distributed equally, ‘and’ is used to separate the compartments (for example‘Nucleus and cytoplasm’).Where distinct populations of cells were observed, ‘or’ wasused to separate the descriptions (for example ‘Nucleus and cytoplasm or nucleus’).When assessing the change that occurred after drug treatment, it was possible that theprotein was still present in the same compartments but that its relative distributionhad changed. For example, a protein present in the nucleus and cytoplasm couldbecome more nuclear after drug treatment while retaining some cytoplasmic signal.In this case, ‘Nucleus, cytoplasm’ indicates a re-distribution to the nucleus. Althoughwe gathered detailed information regarding each protein localization, to facilitatefurther analysis each localization call was refined to a single term representing thepredominant localization (all ‘Nucleus and cytoplasm’ were designated ‘Nucleus’).The localization class represents the net change in protein distribution betweencontrol and drug-treated samples. All classes represent the predominant localizationafter drug treatment with the exception of the ‘From bud neck/tip’ category.

Automated analysis to determine abundance changes. To determine overallabundance the .flex image files were analysed using the provided CellProfilerpipeline (ScreenAnalysis.cp; Supplementary Note 1). Briefly, the RFP channel wasanalysed for primary objects (nuclei) using global robust background thresholding.For this method, the brightest and dimmest pixel intensities are trimmed by 5%and the threshold is calculated as the mean plus two standard deviations of theremaining pixel values. The primary objects were overlaid onto the correspondingGFP channel and measurements corresponding to the nuclei were obtained. Theedge of the nuclear object was extended by six pixels to obtain a secondaryobject referred to as the ‘cytoplasmic ring’. Fluorescence measurements within thecytoplasmic ring were obtained. R scripts ‘ReadExtractCombine.R’, ‘TakeMedian.R’and ‘CalculateZScore.R’ were used to select relevant output data from CellProfilerand calculate fluorescence intensities and Z scores (Supplementary Note 1). Basedon examination of approximately 200 cells per sample it was estimated that thenucleus consisted of approximately 30% and 35% of the cell area in the controland drug-treated images, respectively. The estimated cytoplasmic area was thencalculated (nuclear area/(0.3 or 0.35) − nuclear area) and used to calculate thetotal cytoplasmic intensity. The sum of the intensities measured for the nucleusand calculated for the cytoplasm represents the total cellular fluorescence. We next

compared the median fluorescence intensity of all three control images with themedian intensity of all three drug-treated images to calculate an abundance changeratio (Supplementary Table S1) for each strain. The median was used to buffereffects from small numbers of cells with fluorescence intensities that were greatlydifferent from the rest of the population, or from spurious objects detected duringthe automated analysis. The Z score was calculated based on the medians of thedrug and control samples and the median absolute deviation (MAD) of the controlsample ((mediandrug−mediancon)/MADcon). Z scores of−2 and 2 representing twoMADs from the control median were chosen as cutoff values.

Screen for regulators of hydroxyurea-induced Lsm1 P-bodies. AYY5, whichexpresses Lsm1–GFP from the native LSM1 locus, was crossedwith the yeast deletioncollection59 by SGA (ref. 36) and the resulting array was grown and imaged aftertreatment withmedia (control) or hydroxyurea as described above. The images wereblinded and scored manually for strains that exhibited defects in Lsm1–GFP P-bodyformation. Raw images are available from the Yeast Resource Center Public ImageRepository (http://images.yeastrc.org/tkach_brown/replication_stress).

Gene-Set Enrichment Analysis (GSEA) of proteins that change abundance.An abundance profile was defined such that each gene in the GFP collection wasassociated with a Z score as an index of protein abundance change. The profileswere analysed by GSEA (ref. 60) v2.07 in pre-rank mode. All default parameterswere used except that the minimum and maximum gene set sizes were restrictedto 5 and 300, respectively. Biological process and protein complex gene annotationswere obtained fromGeneOntology (http://berkeleybop.org/goose) on 13April 2011.Further protein complex annotations based on consensus across different studieswere obtained from ref. 61. Enrichment maps were generated with the EnrichmentMap Plugin v1.1 (ref. 62) developed for Cytoscape63 using default parameters. Thenodes in the map were clustered with the Markov clustering algorithm64, using theoverlap coefficient computed by the plugin as the similarity metric (coefficients lessthan 0.5 were set to zero) and an inflation of 2.

GeneOntology enrichment analysis of proteins that change localization. ForFigs 3c,d and 4c and Supplementary Figs S3 and S5, each gene set was analysed forenrichment with Gene Ontology biological processes and protein complexes (usingthe annotations used for GSEA, except that gene set sizes were only restricted tobe ≤300). The significance of enrichment was computed using the hypergeometrictest, relative to the genes in the GFP collection. False discovery rate values werecomputed from the resulting P values using the Benjamini and Hochberg method64.For each gene set, an enrichment map was generated to illustrate the significantlyenriched categories (false discovery rate≤ 0.01). Node clustering was carried outas described for the GSEA-based enrichment maps. For Fig. 2, each gene set wasanalysed for Gene Ontology biological process enrichment when compared with theGFP collection using the Generic Gene Ontology Term Finder (http://go.princeton.edu/cgi-bin/GOTermFinder) using Bonferroni correction and all evidence codes.Enriched Gene Ontology terms were further refined using ReviGO (ref. 65) witha cutoff of P < 0.01. The top two or three refined terms are listed.

Interaction enrichment analyses GeneMANIA (http://www.genemania.org/;ref. 66) was used to generate protein–protein and genetic interaction networks.For both networks the ‘equal by network’ network weighting method was usedand only input genes were included in the networks (that is no related genes werereturned). For the protein–protein interaction network, all available data sets wereused (GeneMANIA datasets as of June 2011). For the genetic interaction network theCostanzo-Boone-2010_positive/negative_interactions_full datasets were excluded.To calculate the interaction enrichment of the network and the associated P-value,the total number of pairwise interactions indicated by GeneMANIA was comparedwith the total number of pairwise interactions among the yeast GFP collection genesin GeneMANIA, using a hypergeometric test. Nodes were manually arranged forclarity, but the overall shape of each network was preserved. For the GeneMANIAanalysis of CMR1, the Costanzo-Boone-profile-similarity database was used togenerate the profile similarity network (top ten genes returned) and all databaseswere used to generate the physical interaction network (top 20 genes returned).GeneMANIA datasets were accessed December 2011.

SGA analysis of CMR1 was carried out as described36. Negative geneticinteractions with CMR1, as both the query and array strain, scored as in ref. 67, andwith the intermediate cutoff (−0.08) recommended in ref. 68 were used to constructthe CMR1 genetic interaction network in Cytoscape 2.8 (ref. 63).

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METHODS DOI: 10.1038/ncb2549

Nodes in all networks were coloured according to the biological processannotation provided in ref. 68. Genes absent from this set were manually annotated(Supplementary Table S10).

Confocal fluorescence microscopy and image analysis. For P-body analyses,cultures were grown to mid-log phase in YPD at 30 ◦C, and washed once inlow-fluorescence medium, water or low-fluorescence medium containing drug.Where indicated, cultures were treated for 15min in water or 2 h in 2 Mhydroxyurea. For analysis of Cmr1–GFP nuclear foci, cultures were grown tosaturation in YPD, diluted into fresh YPD at 0.4ODml−1 and grown for 3 h at30 ◦C before treating with 0.03% MMS for 2 h. 11 z slices with a 0.4 µm stepsize were obtained using Volocity imaging software (PerkinElmer) controllinga Leica DMI6000 microscope with the fluorescein isothiocyanate (FITC), TexasRed and differential interference contrast filter sets (Quorum Technologies).Where indicated, the resulting maximum Z projections were analysed usingCellProfiler pipelines (Supplementary Note 1). For P-body analyses, the pipelinesPbodyFocusMeasure.cp and PbodyFocusMeasure_mec1tel1.cp) were used. Briefly,total cellular fluorescence was used to identify primary objects using an Otsu globalbackground method. The resulting objects were used to mask the GFP imageto ensure that foci were only identified within previously identified objects. Fociwere identified using a robust background method on a per object basis; thismethod detects foci based on their relative intensity when compared with the overallfluorescence within a cell and is not affected by variations in total fluorescencebetween cells or strains. Foci were associated with each parent object and the size andintensity of each focus wasmeasured and output to a spreadsheet. The larger cell sizeof themec11tel11 strain necessitated amodified pipeline to account for this change.For nuclear focus analyses, the pipelines ‘NucFocusIdent_Apj1_Hos2_Ydl.cp’and ‘NucFocusIdent_Pph21.cp’ were used. The pipelines work essentially asdescribed for P-body analysis except that the primary object identification wasmodified to identify nuclei (‘NucFocusIdent_Apj1_Hos2_Ydl.cp’, for analysis ofApj1, Hos2 and Cmr1) or whole cells (‘NucFocusIdent_Pph21.cp’, for analysis ofPph21).

Western blot and drug sensitivity assays. Western blotting: cultures were grownto OD ∼0.5 in YPD at 30 ◦C. 5 OD of cells were treated as indicated and fixed with10% trichloroacetic acid (Sigma-Aldrich) and prepared as described9. Samples wereseparated by SDS–polyacrylamide gel electrophoresis, transferred to nitrocelluloseand blocked with Tris-buffered saline containing 0.05% Tween-20 (TBST) and

5% skimmed milk powder. To detect p-H2A: α-p-H2A (Abcam, ab15083) 1:500overnight at 4 ◦C followed by α-rabbit HRP (Pierce Chemical) 1:10,000 for 1 h atRT. To detect H2A: α-H2A (Abcam, ab13923) 1:2,500 overnight at 4 ◦C followed byα-rabbit HRP (Pierce Chemical) 1:10,000 for 1 h at RT. All antibodies were dilutedin TBST plus milk. Western blots were developed using SuperSignal ECL (PierceChemical), imagedwith a VersadocMP 5000 (Bio-Rad) and quantified using ImageJ(http://imagej.nih.gov/ij/).

Drug sensitivity: Cultures were grown overnight at 30 ◦C in YPD. Cell densitieswere equalized to an optical density of 1, serially diluted tenfold, spotted on theindicated medium and grown for 2–3 days at 30 ◦C before imaging.

57. Brachmann, C. B. et al. Designer deletion strains derived from Saccharomycescerevisiae S288C: a useful set of strains and plasmids for PCR-mediated genedisruption and other applications. Yeast 14, 115–132 (1998).

58. Sherman, F. Getting started with yeast. Methods Enzymol 350, 3–41 (2002).59. Winzeler, E. A. et al. Functional characterization of the S. cerevisiae genome by gene

deletion and parallel analysis. Science 285, 901–906 (1999).60. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach

for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102,15545–15550 (2005).

61. Benschop, J. J. et al. A consensus of core protein complex compositions forSaccharomyces cerevisiae. Mol. Cell 38, 916–928 (2010).

62. Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G. D. Enrichment map:a network-based method for gene-set enrichment visualization and interpretation.PLoS One 5, e13984 (2010).

63. Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P. L. & Ideker, T. Cytoscape 2.8:new features for data integration and network visualization. Bioinformatics 27,431–432 (2011).

64. Van Dongen, S. A Cluster Algorithm for Graphs. Technical Report INS-R0010,National Research Institute for Mathematics and Computer Science in theNetherlands, Amsterdam, May 2000.

65. Supek, F., Bosnjak, M., Skunca, N. & Smuc, T. REVIGO summarizes and visualizeslong lists of gene ontology terms. PLoS One 6, e21800 (2011).

66. Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C. & Morris, Q. GeneMANIA:a real-time multiple association network integration algorithm for predicting genefunction. Gen. Biol. 9 (Suppl 1), S4 (2008).

67. Baryshnikova, A. et al. Quantitative analysis of fitness and genetic interactions inyeast on a genome scale. Nat. Methods 7, 1017–1024 (2010).

68. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

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DOI: 10.1038/ncb2549

0 1 2 3 0 1 2 3 0 1 2 3 h

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Figure S1. The DNA damage response is activated in drug-treated cells. JTY5 was grown to mid-log phase in low fluorescence medium and treated with 0.2 M HU or 0.03% MMS. At the indicated times, aliquots were withdrawn and processed for western blotting with the indicated antibodies (a) or for flow cytometery (b). Bracket indicates phosphorylated forms of Rad53. Numbers underneath western blot indicates normalized fold change in p-H2A levels compared to the levels in control cells at t = 0. (c) Images from the screen corresponding to Rnr3-GFP in control cells (CON) and in cells treated with HU or MMS. Green - Rnr3-GFP, Red - Nup49-mCherry.

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Figure S2. Protein abundance scatter plot correlations. (a) Comparison of protein abundance measured by single cell fluorescence of GFP-fusion strains and by cellular fluorescence measured by microscopy. Comparison of mRNA abundance and protein changes after exposure to MMS for 120 min for the top 300 protein abundance changes (b) and for all proteins in GFP collection (d). (c) Comparison of protein abundance changes after HU or MMS treatment. All plots indicate the Pearson’s correlation co-efficient and a linear-regression line of best-fit (red).

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−2 0 2 4 6Total fluorescence intensity (log2)

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Figure S2. Protein abundance scatter plot correlations. (a) Comparison of protein abundance measured by single cell fluorescence of GFP-fusion strains and by cellular fluorescence measured by microscopy. Comparison of mRNA abundance and protein changes after exposure to MMS for 120 min for the top 300 protein abundance changes (b) and for all proteins in GFP collection (d). (c) Comparison of protein abundance changes after HU or MMS treatment. All plots indicate the Pearson’s correlation co-efficient and a linear-regression line of best-fit (red).

a b

SITE OFPOLARIZED GROWTH

mRNA DECAPPING:

REG OFCELL CYCLE

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PROCESSDNA REPAIR:

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Figure S3. Detailed gene-enrichment maps for screen relocalization positives. Maps corresponding to those in Figure 3 including all node labels for HU (a) and MMS (b) relocalization positives. Nodes are coloured by biological process as in Figure 1d.

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MMS sensitive229

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Figure S4. Comparison among screen positives and genes required for drug resistance. Abundance and relocalization positives versus all genes annotated as MMS sensitive (a) and (c), or HU sensitive (b) and (d). P-values indicating significance of overlap are shown.

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Figure S4. Comparison among screen positives and genes required for drug resistance. Abundance and relocalization positives versus all genes annotated as MMS sensitive (a) and (c), or HU sensitive (b) and (d). P-values indicating significance of overlap are shown.

To Nuclear Foci

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REGULATION OF MITOTIC CELL CYCLE

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mRNA CATABOLIC PROCESS

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REGULATION OF CELL CYCLE ARREST

CELL CYCLE ARREST NEGATIVE REGULATION OF CELL CYCLE

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VESICLE DOCKINGINVOLVED IN EXOCYTOSIS

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REGULATION OF SIGNALING

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REGULATION OF RAB GTPASE ACTIVITY

REGULATION OF RAB PROTEIN SIGNAL TRANSDUCTION

INTRACELLULAR SIGNAL TRANSDUCTION

SIGNAL TRANSDUCTION

CELL POLARITY

Figure S5. Detailed gene-enrichment maps for relocalization classes. Maps corresponding to those in Figure 4c including all node labels for relocalization classes and general terms designated for each cluster. Nodes are coloured by biological process as in Figure 1d.

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Figure S6. Quantification of confocal microscopic images for GFP fusions forming nuclear foci. (a) Cmr1-GFP strains deleted for the indicated gene. Quantification corresponds to Figure 6d. (b) The indicated GFP-fusion was deleted for CMR1 (left panel) or HOS2, PPH21 or APJ1 (right panel). Quantification corresponds to Figure 6e. For each, cultures were untreated (Control- ) or treated with MMS ( ) for 2 h and imaged by confocal microscopy. The percentage of cells containing at least one nuclear focus was quantified using CellProfiler. The means of two independent experiments are shown. Approximately 150 cells were analyzed per experiment.

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Scd6-GFP

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Figure S7. Confocal microscopic images of P-body components from high-throughput screening. Control (left) and HU-treated (right) images are shown for the indicated fusion proteins. P-body components are divided into three classes: (a) Lsm complex components (b) Translation inhibition and decapping components. (c) Unknown function (Pby1) and nonsense-mediated decay components (Nam7/Upf1, Nmd4). Green – GFP fusion. Red – Nup49-mCherry. Scale bar represents 5 mm. (d) HU-sensitivity assay for non-essential p-body components in the ‘to cytoplasmic foci’ class. Serial dilutions of the indicated strains were spotted onto the indicated medium. Also included is kem1∆. The KEM1-GFP strain was not imaged during the screen but is known to form P-bodies.

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Figure S8. (a) Quantification of confocal microscopic images for Lsm1-GFP. Images from strains expressing Lsm1-GFP in the indicated backgrounds corresponding to Figures 7b and 7c were quantified using CellProfiler. The average number of foci detected per cell is shown for untreated cultures (Control- ) and those treated with HU ( ) or water ( ). Results from two independent experiments are shown. Approximately 150 cells were analyzed per experiment. (b) Quantification of confocal microscopic images for P-body components in check-point defective cells. Images from strains expressing the indicated GFP fusion protein corresponding to the images in Figure 7f were analyzed by CellProfiler. Cultures were untreated (Control- ), or treated with HU for 2h ( ) or water for 15 min ( ) and the percentage of cells containing at least one focus was determined. The mean and standard deviation from 3 to 4 independent experiments is shown. Approximately 200 cells were analyzed per experiment.

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Figure S9: Unmodified Versadoc images for western blots in Fig. 6c. The anti-p-H2A blot was performed first, stripped and reprobed for total H2A. For each blot an image of the prestained markers on the membrane and the corresponding molecular weights is shown.

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