Global analysis of SUMO chain function reveals multiple roles in chromatin regulation.

Like ubiquitin, the small ubiquitin-related modifier (SUMO) proteins can form oligomeric "chains," but the biological functions of these superstructures are not well understood. Here, we created mutant yeast strains unable to synthesize SUMO chains (smt3(allR)) and subjected them to high-content microscopic screening, synthetic genetic array (SGA) analysis, and high-density transcript profiling to perform the first global analysis of SUMO chain function. This comprehensive assessment identified 144 proteins with altered localization or intensity in smt3(allR) cells, 149 synthetic genetic interactions, and 225 mRNA transcripts (primarily consisting of stress- and nutrient-response genes) that displayed a >1.5-fold increase in expression levels. This information-rich resource strongly implicates SUMO chains in the regulation of chromatin. Indeed, using several different approaches, we demonstrate that SUMO chains are required for the maintenance of normal higher-order chromatin structure and transcriptional repression of environmental stress response genes in budding yeast.

Complete image-based screen data

https://doi.org/10.1083/jcb.201210019.dv

Introduction

The small ubiquitin-related modifier (SUMO) system plays important roles in many diverse biological processes in all eukaryotes (Johnson, 2004; Kerscher et al., 2006). Like ubiquitin, SUMO modification is effected via covalent conjugation to an epsilon amine moiety of a lysine residue in a targeted protein, via the sequential action of SUMO-specific E1, E2, and E3 proteins. SUMO conjugation can be reversed by a family of SUMO-specific proteases (Johnson, 2004; Kerscher et al., 2006; Shin et al., 2012).

The sole budding yeast SUMO protein is encoded by the essential SMT3 gene. Invertebrates also express a single SUMO protein, whereas vertebrates and plants express multiple SUMO isoforms (Hay, 2005; Castro et al., 2012). Systematic proteomics screens have identified >500 putative SUMO conjugates in budding yeast (among others, Wohlschlegel et al., 2004; Denison et al., 2005; Cremona et al., 2012) and hundreds more in plants, insects, and mammals (Nie et al., 2009; Elrouby and Coupland, 2010; Bruderer et al., 2011). Ectopic expression of the human SUMO-1 protein rescues smt3 lethality (Takahashi et al., 1999), highlighting the usefulness of Saccharomyces cerevisiae as a model organism for assessing SUMO function in eukaryotes.

The SUMO proteins interact with small hydrophobic domains referred to as SUMO-interacting motifs (SIMs). SIMs confer low affinity binding to SUMOs, often occur in tandem, and can confer specificity for particular SUMO isoforms (Prudden et al., 2007; Sun et al., 2007; Perry et al., 2008; Tatham et al., 2008). Sumoylation thus represents a rapid and efficient way to regulate protein–protein interactions. SUMO–SIM interactions have been implicated in a variety of biological functions, including transcriptional control (Ouyang et al., 2009; Santiago et al., 2009; Saether et al., 2011), DNA damage repair (Li et al., 2010; Galanty et al., 2012; Yin et al., 2012), protein degradation (Prudden et al., 2007; Perry et al., 2008), and the assembly of DNA–protein superstructures such as PML (Lallemand-Breitenbach et al., 2008; Tatham et al., 2008) and insulator bodies (MacPherson et al., 2009; Golovnin et al., 2012).

Notably, SUMO can be conjugated to proteins in a monomeric form, or as oligomeric SUMO “chain” structures. In budding yeast, SUMO–SUMO linkages are formed primarily via K15 (Bencsath et al., 2002), although we and others have detected linkages at additional lysine residues in vitro (Bencsath et al., 2002; Jeram et al., 2010). The best characterized function of SUMO chains is as a secondary degradation signal. The SUMO-targeted ubiquitin ligases (STUbLs) are an evolutionarily conserved family of ubiquitin E3 proteins that contain multiple SIMs. The STUbLs are thus recruited to polysumoylated proteins and effect their ubiquitylation, marking them for 26S proteasome-mediated destruction (Perry et al., 2008). A few STUbL targets have been identified, including PML (Lallemand-Breitenbach et al., 2008; Tatham et al., 2008), the HTLV-1 Tax protein (Fryrear et al., 2012), the Drosophila melanogaster transcriptional repressor Hairy (Abed et al., 2011), and the budding yeast transcriptional regulator Mot1 (Wang et al., 2006; Wang and Prelich, 2009). Importantly, however, the biological functions of SUMO chains remain poorly characterized.

Many studies have implicated the SUMO system in transcriptional regulation (Garcia-Dominguez and Reyes, 2009; Abed et al., 2011). Transcription factors and coregulators, chromatin remodeling proteins, and histones are all modified by SUMO (Shiio and Eisenman, 2003; Nathan et al., 2006). Most studies have indicated that SUMO plays a negative regulatory role in transcription, and SUMOs can bind to SIMs in transcriptional co-repressors such as CoREST1 (Ouyang et al., 2009) and Daxx, and other types of proteins that regulate chromatin structure, including histone methyltransferases (SETDB1, SUV4-2OH) and the chromatin remodeler Mi2 (Ivanov et al., 2007; Stielow et al., 2008a,b), possibly to effect local heterochromatization (Ross et al., 2002; Yang and Sharrocks, 2004; Ivanov et al., 2007).

Here, using a combination of high-content microscopic screening, functional genomics analysis, and high-density transcript profiling, we conducted the first global study of SUMO chain function. Using this data-rich resource, we implicate the SUMO system in the maintenance of transcriptional repression and higher-order chromatin structure.

Results

smt3 strains exhibit chromosome segregation defects and replication-associated DNA damage

To better understand the biological roles of SUMO chains, we generated haploid yeast strains in which the endogenous SUMO gene (SMT3) was replaced by an ORF in which all nine lysine codons were mutated to code for arginine (as in Bylebyl et al., 2003). The resulting mutant SUMO “allR” polypeptide can thus be conjugated to other proteins as a monomer, but lacks the ability to form SUMO chains (Fig. 1 A). Although smt3 deletants arrest in G2/M with short spindles and replicated DNA (Seufert et al., 1995; Li and Hochstrasser, 1999; Hochstrasser, 2000), an earlier study demonstrated that smt3 strains are viable and that the SUMO allR polypeptide is conjugated to the septin protein Cdc11 in vivo (Bylebyl et al., 2003). SUMO function is thus at least partially fulfilled by the SUMO allR protein. Consistent with these data, we found that a recombinant SUMO allR protein is conjugated to a model substrate (a biotinylated 11-aa peptide containing the SUMO consensus sequence) in vitro as efficiently as the wild-type (WT) protein (Fig. 1 B), which indicates that the K-to-R mutations do not appreciably affect the ability of this polypeptide to be recognized by the SUMO E1 or E2 proteins.

Figure 1.

A SUMO allR polypeptide can be conjugated to target proteins, but is unable to form SUMO chains in vitro and in vivo. (A) Schematic representation of the WT SUMO and SUMO allR proteins. Although both SUMO protein variants can be covalently conjugated to substrates (also known as “target” proteins), the allR SUMO polypeptide lacks lysine residues, and is therefore unable to form SUMO chains. (B) WT SUMO and the SUMO allR protein are conjugated to a biotinylated polypeptide (a model substrate containing the sumoylation consensus sequence) at similar efficiencies in vitro. Reactions were conducted in the presence (+) and absence (−) of ATP. (Lane 1) SUMO E1 and E2 proteins, along with the biotinylated substrate peptide (reaction mix). (Lanes 2 and 3) Reaction mix plus WT SUMO protein. (Lanes 4 and 5) Reaction mix plus allR SUMO protein. (C) smt3 strains do not form high-molecular-weight SUMO conjugates in response to environmental stress. WT and smt3 cells were exposed to 10% ethanol (EtOH) for 1 h, and SUMO conjugates were visualized by Western blot analysis of whole cell lysates. Unconjugated SUMO is shown in the middle panel (a longer exposure of the same Western blot), and actin (loading control) in the bottom panel. The pro-smt3 strain expresses a SUMO allR pro-protein, which possesses three additional C-terminal residues that must be cleaved to generate the mature SUMO protein. The smt3 strain expresses a mature form of the allR SUMO protein. (D) The SUMO antibody does not detect the SUMO allR protein with the same efficiency as the WT SUMO polypeptide. Equal amounts of purified recombinant SUMO WT and allR proteins were subjected to Coomassie blue staining (top) and Western blotting (bottom).

Several previous studies have demonstrated that steady-state sumoylation increases in response to stress (Zhou et al., 2004; Tempé et al., 2008). To determine whether the smt3 strain is able to respond to environmental stresses commonly encountered by yeast, we assessed its response to high ethanol (EtOH) concentrations. As expected, exposure of WT cells to 10% EtOH (for 1 h) led to a dramatic increase in high-molecular-weight SUMO conjugates (Fig. 1 C). Although smt3 cells displayed a decrease in unconjugated (free) SUMO, only a very minor increase in high-molecular-weight SUMO conjugates in response to EtOH treatment was observed (Fig. 1 C; the minor high molecular signal most likely reflects multi-monosumoylation of high-molecular-weight targets, or could represent, e.g., proteins that are both sumoylated and ubiquitylated in response to stress). In addition, here we tested two different smt3 strains: one in which the C-terminal three amino acid extension of the SUMO protein was maintained in the coding region (pro-smt3) and a second in which this region was removed to express the mature SUMO polypeptide (smt3). No differences in division time (not depicted) or EtOH response were observed between the two strain types (Fig. 1 C), which indicates that SUMO maturation activity is not limiting in these cells.

The signal strength of the unconjugated SUMO allR protein in Western blot analysis was markedly lower than that observed for the endogenous WT SUMO protein (Fig. 1 C). However, when equal amounts of purified recombinant WT and allR SUMO polypeptides were subjected to SDS-PAGE and Coomassie blue staining (Fig. 1 D, top) or Western blotting analysis (Fig. 1 D, bottom), we found that the allR SUMO protein is simply not recognized as efficiently by the SUMO antibody (with this antibody, the allR protein yields <20% of the signal intensity of an equivalent amount of the WT SUMO protein). Indeed, quantification of SUMO signal intensity in parental and smt3 yeast strains based on these data indicate that the SUMO allR protein is expressed at levels similar to (or even higher than) the endogenous SUMO protein (see Materials and methods for details).

As expected (Bylebyl et al., 2003), under standard culture conditions the doubling time of smt3 cells is increased ∼1.5-fold (180 ± 6.7 min) as compared with parental strains (119 ± 1.3 min; P < 0.01; Fig. 2 A). FACS of SYTOX green–stained cells revealed a slight increase in a supra-G2 population, and an approximately twofold increase in cells with >2n DNA content (Fig. 2 B) in the smt3 cell population (P < 0.01). Consistent with observations in other SUMO pathway mutants (Felberbaum and Hochstrasser, 2008; Lee et al., 2011), DAPI staining revealed chromosome segregation defects in a subset of the smt3 population (∼40% of large budded cells; Fig. 2 C and Fig. S1 A). A lack of SUMO chain synthesis thus appears to negatively affect the efficient segregation of chromosomes, which in turn leads to an increase in population ploidy.

Figure 2.

(A) Doubling time (mean ± SD) was measured over an 8-h period of log-phase growth for smt3 and parental strains. Strains (as indicated) were also transfected with a galactose-inducible SMT3 (WT) or smt3 plasmid (+pSMT3 or +psmt3, respectively), which was induced for 18 h before the first doubling time measurement. (B) FACS analysis of untransfected parental and smt3 strains, and the same strains expressing the WT or allR SUMO proteins (as in A). DNA was stained with SYTOX green and data were collected on 50,000 events. The insets highlight the polyploid (>2n) population in each analysis. (C) Parental and smt3 strains were stained with DAPI and imaged using confocal microscopy. Two representative images from each strain are shown. Cells displaying abnormal chromosome segregation are highlighted with arrowheads. Bar, 10 µm. (D) Log-phase cells were treated as indicated for 1 h, serially diluted (10×), and spotted onto YPD plates (HU, hydroxyurea; MMS, methyl methanesulfonate; Zeo, zeocin; 4-NQO, 4-nitroquinoline 1-oxide; DTT, dithiothreitol; Linger and Tyler, 2005; Rand and Grant, 2006; Tang et al., 2009). Colonies were grown for 2 d at 30°C. (E) Spontaneous DNA damage foci were quantified in parental and smt3 strains using GFP-tagged RAD52, DDC1, and RFA1. The mean number of foci (±SD) from four fields is tabulated. Bar, 10 µm. (F) Cell size distribution (mean ± SD) was measured on a Z2 counter (Beckman Coulter), as in Jorgensen et al. (2002). The gray box highlights the cell population with a volume >80 fL in the parental (black line) and smt3 (red line) strains. Data shown are from a single representative experiment, conducted twice.

Consistent with a role for SUMO chains in DNA replication, smt3 cells also displayed hypersensitivity to the ribonucleotide reductase inhibitor hydroxyurea (HU) and the alkylating agent methyl methanesulfonate (MMS), but did not exhibit increased sensitivity to DNA damage induced by zeocin or 4-nitroquinoline 1-oxide (4NQO; Fig. 2 D and Fig. S1 B), and did not display increased sensitivity to high or low temperatures, or protein-damaging agents (Fig. 2 D and Fig. S1, B and C).

Strikingly, untreated smt3 strains displayed a >10-fold increase in the number of steady-state DNA damage foci, as visualized via RAD52-GFP, DDC1-GFP, and RFA1-GFP (parental strain average for all markers = 1.51 ± 0.63 foci/field; smt3 average = 17.1 ± 2.93 foci/field; Fig. 2 E). To further explore the role of SUMO chains in replication-associated DNA damage, we crossed the smt3 strain with 384 yeast strains expressing GFP-tagged proteins (Huh et al., 2003) previously linked to the DNA damage response (Tkach et al., 2012). Live cells were imaged using automated high-throughput confocal microscopy (Tkach et al., 2012) and the resulting images were examined for differences in localization and signal intensity in the SUMO chain mutant (Table 1 and Table S1). This high content screen (HCS) highlighted changes in localization and/or intensity in smt3 cells for 144 proteins, most of which are involved in DNA replication, segregation, or repair processes (Table 1 and Table S1). These data are consistent with several earlier publications linking the SUMO system to replication stress (Branzei et al., 2006; Xiong et al., 2009), yet significantly expand the repertoire of DNA damage–associated proteins demonstrated to be affected in response to SUMO system defects. Most importantly, these data for the first time also specifically implicate SUMO chains in this function.

Table 1.

smt3 HCS

Group	Protein
DNA replication and repair	AQR1	DPB11	HST4	MGS1	NUP53	RAD59	RPL40A	SLD3	XRS2
	CGR1	DUN1	IPL1	MKT1	PNC1	RFA1	RPN4	SLX4	YDL156W
	DBF4	DUS3	LCD1	MRE11	RAD50	RFA2	SAE2	STP1	YJR056C
	DDC1	GLN1	MCM2	MRS6	RAD52	RFC2	SGF11	TRM112	YML108W
	DNA2	SRS2	MCM4	MSN2	RAD57	RNR4	SGS1	TSR1	ZPR1
Polarization/budding/bud site selection	BUD14	GSP2	MSB1	NBA1	CDC24	GYL1	MSB3	OPY2
Ion homeostasis (pH)	ARN1	CTR1	VMA10	VMA4	YLR126C	YOL092W	CRD1	POR1	VMA2
	VPH1	YML018C
mRNA catabolic processes	DCP1	EDC2	LSM1	LSM3	LSM7	NMD4	PBP4	DHH1	EDC3
	LSM2	LSM4	NAM7	PAT1
Spindle defects	ASE1	DAD3	CNM67	DAD4
Vacuole function	LAP4	PEP8	VPS1	YLR297W	MTC5	PIB1	YIR014W
Ribosome biogenesis	ATC1	CMS1	GDT1	NOP13	RMT2	ATG29	ECM1	HGH1	NOP58
	RPL7B
Stress response	AHA1	CUE1	HSP42	ITR1	TSA1	YKL069W	APJ1	GSY2	HXT3
	SCH9	WSC4
Cell shape defects	DSE3	NEO1	SEC10	SEC6	VPS41	FLC1	RAS1	SEC3	SEC8
Other	ATG16	FAT1	KTR3	PBY1	PPH21	SRP68	YDL085C-A	YGR042W	YKR011C
	CHS7	HOM6	LSB1	PEX21	RSM10	YBR259W	YDR090C	YHR140W	YLR363W-A
	FAA1	IRC22	MDM12	PIL1	SGT2	YDC1	YDR170W-A	YIL108W	YMR111C

144 GFP-tagged proteins displayed a change in localization and/or intensity when expressed in the smt3 mutant grown in rich medium. Proteins are grouped according to ten functional categories.

SMT3 was first characterized as a high-copy suppressor of mif2, a kinetochore protein required for structural integrity of the mitotic spindle (Meluh and Koshland, 1995; Vizeacoumar et al., 2010). Chromosomal passenger complex protein localization is also regulated by the SUMO system, to mediate spindle disassembly (Vizeacoumar et al., 2010). Consistent with a role for SUMO chains in mitotic spindle dynamics, the HCS highlighted mislocalization of several additional proteins (4 of 11 proteins in the screen) involved in spindle function (Table 1 and Fig. S2).

Also of note, although a majority of cells fell within the normal size range, a subpopulation of smt3 cells exhibited significant increases in volume (P < 0.001; Fig. 2 F). The proportion of cells with a volume >80 fl (more than two standard deviations from the mean) was 11 ± 4% for parental strains and 30 ± 6% for smt3 strains. Both large and normal sized smt3 cells successfully produced colonies, and gave rise to a mix of normal and large cells in similar proportions (unpublished data), which indicates that the large cell phenotype is neither terminal nor heritable. The size increase thus likely reflects a cell cycle delay caused by an increased DNA repair load and chromosome segregation defects.

smt3 cells display characteristics of an activated environmental stress response

The HCS also highlighted several GFP-tagged vacuolar proteins with clear changes in localization in smt3 cells; e.g., VPS1-GFP and VPS41-GFP displayed more numerous puncta than parental cells (Fig. S2). Multiple mitochondrial markers (e.g., MDM12-GFP and POR1-GFP) also displayed markedly increased signal intensity in the smt3 strains (Fig. S2). Consistent with these data, electron micrographs revealed a large subset of smt3 cells with fragmented vacuoles, increased mitochondrial volume, and thicker cell walls than parental strains (Fig. 3 A and Fig. S3 A). These defects were unexpected and were investigated further.

Figure 3.

Ultrastructural characterization of (A) Electron micrographs of parental and smt3 cells, highlighting the nucleus (N), vacuoles (V), and mitochondria (arrowheads). Bars, 500 nm. (B) Mitochondrial-targeted GFP (mtGFP) and MitoTracker red CMXRos staining highlight increased mitochondrial volume in smt3 mutant cells. (C) Basal oxygen consumption of parental and smt3 mutant cells (error bars indicate mean ± SD) grown in YPD. Azide treatment inactivates oxidative respiration and indicates levels of nonmitochondrial oxygen consumption.

Signal intensities for a GFP bearing a mitochondrial targeting sequence (Westermann and Neupert, 2000) and Mitotracker red, a thiol-reactive dye that accumulates in active mitochondria, were strikingly enhanced in cells defective for SUMO chain synthesis (Fig. 3 B). smt3 cells also exhibited a significant increase (more than fourfold; P < 0.01) in basal oxygen consumption rates (Fig. 3 C), even when maintained in glucose-containing culture media (a condition in which glycolysis is the preferred mode of energy production). smt3 cells thus maintain abnormally high levels of mitochondria that are metabolically active even in the presence of glucose.

Vacuolar fragmentation is observed in cells in a hypertonic environment (Ryan et al., 2008). Glycerol is the primary osmoprotectant in S. cerevisiae, and is synthesized in response to hyperosmotic conditions to maintain cell turgor (Hohmann, 2009). smt3 cells grown in isosmotic media displayed highly fragmented vacuoles and a more than twofold increase (P < 0.01) in intracellular glycerol concentrations, as compared with parental strains (Fig. S3, B and C). These data suggest that SUMO chain mutants are also subject to chronic osmotic stress or exhibit aberrant osmotic stress signaling.

Together, our data reveal that disruption of SUMO chain assembly gives rise to a pleiotropic cell population exhibiting several different physiological defects. We did not observe any clear correlation between, e.g., ploidy and the number of DNA damage foci or mitochondrial mass, which suggests that these phenotypes are largely independent of one another.

Replication-associated DNA damage is observed in other types of SUMO system mutants (Branzei et al., 2006; Schwartz et al., 2007), and our analysis implicates SUMO chains in this process. However, we also observed phenotypic characteristics in smt3 cells that have not previously been described for other types of SUMO mutants. Many of these traits are reminiscent of an inappropriately activated response to environmental stress or nutrient-poor media conditions.

The smt3 phenotype is caused by a lack of SUMO chains

To confirm that the smt3 phenotype is caused by a lack of SUMO chains, and not to secondary mutations that could arise in such mutants, we transformed plasmids coding for galactose-inducible WT or allR SUMO proteins into parental and smt3 strains, and assessed their effects on doubling time, ploidy, and vacuolar morphology. Additional SUMO allR protein expression in the smt3 strain (induced for 16 h) had no apparent effect on cycling time (188 ± 6 min), ploidy, or vacuole size and number (Fig. 2, A and B; and Fig. S3 D). Similarly, overexpression of the WT SUMO protein in parental strains had no discernible effect on these phenotypic features (Fig. 2, A and B; and Fig. S3 D). However, overexpression of the SUMO allR protein in parental (WT) strains led to a significant increase in doubling time (177 ± 8 min, P < 0.001; Fig. 2 A), an increase in the number of cells with >2n DNA ploidy (Fig. 2 B), and an increase in vacuolar fragmentation (Fig. S3 D). Conversely, expression of the WT SUMO protein in smt3 strains led to a decrease in doubling time (121 ± 2 min, P < 0.001), a decrease in the proportion of cells with >2n DNA ploidy, and a decrease in vacuolar fragmentation (Fig. 2, A and B; and Fig. S3 D). The smt3 phenotype can thus be at least partially rescued by expression of a SUMO protein that can form chains, and overexpression of the SUMO allR protein in WT cells can effect changes in cycling time, ploidy, and vacuolar morphology even in the presence of the endogenous SUMO polypeptide. Together, these data indicate that the smt3 phenotype is not caused by a limited supply of the SUMO protein for conjugation, or to secondary mutations in these strains, but is indeed caused by a lack of SUMO chains. These data also demonstrate that the SUMO allR protein can act in a dominant manner in the presence of the endogenous SUMO polypeptide, presumably by preventing SUMO chain formation.

Previous studies have indicated that SUMO chains in vivo are linked primarily via N-terminal lysine residues (mostly through K15; Bencsath et al., 2002). To determine whether the smt3 phenotype could be recapitulated by disrupting only the N-terminal lysine residues, we also expressed a SUMO 3KR mutant (in which only lysines 11, 15, and 19 are mutated to arginine residues) in WT cells. Division time and ploidy were indistinguishable from cells expressing the SUMO allR mutant (Fig. S4), which further suggests that the smt3 phenotype is caused by the disruption of SUMO chains. In the remaining work presented here, we used smt3 strains to avoid any possibility of SUMO chain synthesis via the use of alternative lysine residues (as we and others have observed in vitro; Bencsath et al., 2002; Bylebyl et al., 2003; Jeram et al., 2010).

A SUMO chain genetic interaction network

To identify cellular pathways that specifically compensate for disrupted SUMO chain synthesis, the smt3 strain was subjected to synthetic genetic array (SGA) analysis, as in Makhnevych et al. (2009) and Costanzo et al. (2010). The smt3 mutant was crossed with an ordered array of ∼4,700 viable yeast deletion mutants, and the resulting strains were scored for colony growth (Baryshnikova et al., 2010). To avoid the possibility of false-positive interactions caused by secondary mutations in the SUMO chain mutant, SGA was conducted twice, using two different smt3 strains (one expressing pro-SMT3 and one expressing the mature SMT3 polypeptide, as in Fig. 1 C). 149 high-confidence synthetic genetic interactions were detected in both analyses (Table S2). The resultant SUMO chain genetic interaction network represents the first global genetic analysis of SUMO chain function in any organism. Gene ontology (GO) analysis (Table S2) highlighted significant enrichment in interactions with genes involved in DNA replication, DNA damage repair, chromatin remodeling, cell cycle control, stress responses, protein catabolism, nuclear transport, and meiosis.

SGA correlation analysis (i.e., the comparison of genetic interaction maps) is useful for gaining insight into the function of a gene of interest, because genes that share similar patterns of genetic interactions are likely to share similar biological roles (Costanzo et al., 2010). The smt3 SGA profile was thus compared with SGA-derived genetic interaction profiles of 4,458 mutant strains available in the data repository of the yeast genetic interactions database (DRYGIN; Koh et al., 2010). 194 genes displayed a significant positive correlation with the smt3 genetic interaction map (Fig. 4, Table 2, and Table S2). Attesting to the robustness of this analysis, three of the four highest correlated genes were derived from components of the SUMO system itself: ubc9 (ubc9-2), mms21 (mms21-sp), and smt3 (smt3-damp; decreased abundance by mRNA perturbation; Yan et al., 2008). ulp1 was also a top-scoring hit (ulp1-333). Likely reflecting a role in a subset of SUMO functions, siz2 (nfi1) displayed a significant, but lower, overlap with the smt3 interaction profile. Consistent with STUbL-mediated degradation as a major function for SUMO chains, the second most highly correlated genetic interaction map in our screen was slx8. The gene coding for its binding partner slx5 was also a top-scoring hit.

Figure 4.

194 genes yielded a significant positive correlation with the smt3 genetic interaction network. Edge width corresponds to correlation values.

Table 2.

smt3 SGA correlation analysis

Category	Gene	Correlation
SUMO system
SUMO system components	ubc9-2	0.430
	SMT3_damp	0.338
	mms21-1	0.329
	ulp1-333	0.263
	NFI1	0.087
NPC components–Ulp1 localization	NUP60	0.328
	NUP133	0.228
	nup145-R4	0.223
	SRP1_damp	0.149
	NUP84	0.113
	GLE2	0.130
	YLL023C	0.109
	nup57-E17	0.094
	NUP49_damp	0.091
Chromatin remodeling
Histone chaperone	ASF1	0.247
Chromatin silencing	ESC2	0.294
	RTT109	0.141
	RAP1_damp	0.116
	MOT3	0.110
	YAP1	0.109
	RIF1	0.099
Chromatin assembly factor (CAF-1)	CAC2	0.176
	RLF2	0.148
	MSI1	0.090
Histones	HTA1	0.139
	HTZ1	0.123
	HHF1	0.119
SWR1 complex	SWR1	0.144
	HTZ1	0.123
	VPS71	0.108
	ARP6	0.105
	swc4-4	0.099
	VPS72	0.098
	SWC3	0.097
DNA replication and repair
MRX complex	MRE11	0.166
	XRS2	0.154
	RAD50	0.138
	SAE2	0.127
MCM complex	cdc47-ts	0.217
	mcm3-1	0.132
	cdc46-1	0.127
Mms21–Smc5–Smc6 complex	mms21-1	0.329
	nse3-ts4	0.287
	nse4-ts2	0.263
	kre29-ts2	0.213
	nse4-ts4	0.183
	nse3-ts3	0.175
	nse5-ts4	0.165
	smc5-6	0.152
	nse4-ts3	0.151
	smc6-9	0.147
	nse5-ts2	0.118
Pol2–TOF1–MRC1–CSM3 complex	MRC1	0.206
	pol2-12	0.182
	CSM3	0.180
	TOF1	0.141
Origin recognition complex	orc2-2	0.157
	orc2-4	0.096
	orc3-70	0.095
Ribonuclease 2	RNH203	0.138
	RNH202	0.137
Polymerase delta	POL32	0.231
	cdc2-1	0.219
	cdc2-7	0.185
	cdc2-2	0.167
Mms4–Mus81 complex	MMS4	0.177
	MUS81	0.156
Pol1-DNA primase	pol12-ts	0.192
	pol1-13	0.128
	pol1-ts	0.120
	pol1-1	0.118
	pri2-1	0.109
RFC complex	ELG1	0.271
	rfc4-20	0.214
	rfc5-1	0.153
	RAD24	0.116
	CTF18	0.101
	CHL1	0.097
	DCC1	0.092
Other	RAD27	0.242
	RRM3	0.192
	RTT107	0.168
	psf1-1	0.158
	DUN1	0.146
	DDC1	0.133
	RNR4	0.120
	CLB5	0.119
	RAP1_damp	0.116
	dpb11-1	0.111
	MMS22	0.108
	RAD5	0.100
	RAD54	0.098
	REV3	0.098
	RAD17	0.097
	cdc6-1	0.097
	RAD55	0.090
Ubiquitin–proteasome system
STUbL	SLX8	0.393
	SLX5	0.247
Cdc48	cdc48-2	0.183
	SHP1	0.160
	cdc48-3	0.145
	OTU1	0.081
APC/C	apc5-CA	0.162
	apc2-8	0.161
	cdc20-2	0.161
	cdc20-1	0.134
	cdc16-1	0.130
	cdc23-1	0.102
SCF	DIA2	0.169
	UBC4	0.105
Proteasome	rpn12-1	0.155
	rpn11-8	0.127
	SCL1_damp	0.118
	rpn11-14	0.107
	rpt1-1	0.107
	RPN4	0.100
	rpt6-20	0.091
Miscellaneous
Spindle/kinetochore	spc105-15	0.154
	LTE1	0.152
	BUB3	0.149
	KAR3	0.135
	CIK1	0.129
	CLB5	0.119
	BUB1	0.105
	stu2-12	0.094
	stu2-11	0.092
HOG pathway signaling	SSK2	0.120
	PBS2	0.082
Vesicle/vacuole	ALF1_damp	0.160
	LTE1	0.152
	VID22	0.133
	ICE2	0.105
	PGA3_damp	0.104
	EMC2	0.102
	VPS21	0.101
Mitochondrial function	MRH4	0.193
	MSW1	0.187
	PET111	0.131
	MRP49	0.100
	YDR065W	0.100
	PET8	0.092
	SOV1	0.092
	QCR8	0.090
	MRPL19	0.090

194 genes display a positive correlation with the smt3allR genetic map. Genes are grouped according to functional categories.

As expected, ubiquitin-proteasome system (UPS) components were also highlighted in this analysis; the UPS works with Slx5-Slx8 to effect SUMO-targeted protein degradation. We also observed overlap with the cdc48 (p97) SGA map. This protein was recently reported to work with the Slx5-Slx8 proteins to mediate genome stability (Nie et al., 2012). Another set of highly correlated genes corresponded to nuclear pore complex (NPC) components and karyopherins (nup60, nup133, nup145-R4, nup84, and srp1-damp). This is also not unexpected, as strains with a loss of function in any of these genes display aberrant Ulp1 localization, which directly impacts SUMO system function (Panse et al., 2003; Makhnevych et al., 2007).

Consistent with the smt3 phenotype, several proteins involved in DNA replication and repair shared significant similarity with the smt3 genetic interaction profile, including several DNA polymerases, helicases, and exonucleases (e.g., rad27, cdc2-1, pol32, pol12-ts, pol1-13, rrm3, etc.), and genes implicated in stalled replication fork stabilization (e.g., tof1, mrc1, and csm3, and the MCM helicase complex: mcm3-1, cdc47-ts, and cdc46-1). Recent work has also demonstrated that the SUMO E3 ligase Mms21, as part of the Smc5-6 complex, plays a critical role in resolving recombination intermediates at damaged DNA templates (Branzei et al., 2006; Chavez et al., 2010). Smc5-6 mutants undergo aberrant mitosis, in which chromosome segregation of repetitive regions is impaired (Torres-Rosell et al., 2005). A failure to resolve this type of DNA damage can lead to chromosomal rearrangements and increased ploidy. Indeed, multiple components of the Smc5-Smc6 complex (mms21-1, nse3-ts4, nse4-ts2, kre29-ts2, etc.) were highly correlated in our analysis. Also as observed in our HCS, genetic interaction maps for esc2, sgs1, mus84, and mms1, all of which play an important role in resolving homologous recombination repair DNA intermediates in response to replication stress (Ashton and Hickson, 2010; Rossi et al., 2010; Hickson and Mankouri, 2011), were highly correlated with the smt3 interaction map.

Notably, SGA correlation analysis also highlighted similarity between smt3 and several proteins involved in chromatin organization and remodeling. For example, significant correlations were observed with the histone chaperone asf1, several components of chromatin assembly factor-1 (CAF-1; cac2, rlf2, and msi1), the histone acetyltransferase rtt109, the histone H2A.Z exchange complex SWR1 (swr1, vps71, arp6, swc4-4, etc.), histone deletants (hta1, htz1, hhf1), and spt21 (required for proper histone gene transcription).

Interestingly, we also observed similarity with genes implicated in mitochondrial function (e.g. mrh4, msw1, and mrp49) and osmotic stress signaling (ssk2 and pbs2). Consistent with our HCS data and several previous publications linking the SUMO system to spindle function (Vizeacoumar et al., 2010; Pérez de Castro et al., 2011; Wan et al., 2012), SGA correlation analysis also highlighted several spindle and kinetochore genes (e.g., bub3, spc105-15, lte1, kar3, clk1, stu2-11, and stu2-12).

In sum, our genetic data implicate SUMO chains in several functions previously ascribed to the SUMO system, such as resolving DNA replication–associated repair structures, but also link them to some previously unsuspected biological roles, such as osmoregulation and higher order chromatin structure.

Derepression of stress- and nutrient-regulated gene transcription and aberrant transcription of cryptic intergenic regions in smt3 strains

High-resolution whole genome nucleotide tiling arrays (see Materials and methods for details) were next used to characterize the transcription profile of cells defective for SUMO chain synthesis (as in Tsui et al., 2012). 36 genes were repressed and 225 mRNAs were expressed >1.5-fold higher in the smt3 strain, as compared with parental cells (Table 3 and Table S3). The up-regulated mRNAs consisted primarily of genes implicated in stress responses, nutrient adaptation, cell wall components, mitochondrial proteins, sporulation, and mating; i.e., genes that are normally repressed under standard laboratory culture conditions, where cells are maintained in media with optimal carbon and nitrogen sources, and at optimal growth temperature. Increased transcription of this gene set likely accounts for many aspects of the pleiotropic smt3 phenotype. For example, several genes implicated in mitochondrial function (e.g., STF1, ALD4, and CYC7) and cell wall integrity signaling (e.g., YGP1, KDX1, and PRM5) are up-regulated in this strain. These data suggest that SUMO chains are likely to be involved indirectly in each of these biological functions, via transcriptional control.

Table 3.

smt3 tiling array gene expression analysis

Category	Gene	log2 (fold change)	Gene	log2 (fold change)	Gene	log2 (fold change)	Gene	log2 (fold change)	Gene	log2 (fold change)	Gene	log2 (fold change)	Gene	log2 (fold change)
Nutrient/stress response
	HSP12	2.912	HSP104	1.229	ECM4	0.954	SPG4	0.765	HMX1	0.606	DDR2	2.872	ALD3	1.200
	MOH1	0.933	TPS2	0.756	YMR090W	0.602	HSP26	2.805	SOL4	1.159	HOR2	0.907	DUR1,2	0.753
	MSN4	0.602	HUG1	2.404	CRG1	1.139	USV1	0.898	HSP31	0.746	PHO12	–0.602	TMA10	1.817
	ADR1	1.133	TMA17	0.896	RNY1	0.741	PHO11	–0.604	MSC1	1.803	NTH1	1.131	UBC5	0.893
	YOR052C	0.731	SPL2	–0.660	TSL1	1.793	ATG8	1.129	HOR7	0.882	YJL144W	0.711	ZRT1	–0.760
	GAD1	1.753	CTT1	1.104	PUT1	0.874	AHA1	0.691	RSN1	–0.805	HSP42	1.664	FRE7	1.045
	SOM1	0.860	YNR014W	0.677	AAH1	–0.805	GLK1	1.635	PRB1	1.037	ATH1	0.852	YNL134C	0.676
	PHM6	–0.815	TFS1	1.522	NCE103	1.036	SSA3	0.833	GRX1	0.664	HMS2	–0.832	PNC1	1.470
	GTT1	1.022	IGD1	0.826	EDC2	0.637	SSA4	1.465	SSE2	1.009	YJR096W	0.811	SPI1	0.631
	GRE1	1.394	GAC1	1.004	TPS1	0.808	RAD51	0.630	GCY1	1.382	PLM2	0.990	GPD1	0.802
	RCN2	0.630	HSP78	1.338	MCR1	0.980	GRE3	0.800	YAP6	0.628	PGM2	1.317	YDL124W	0.968
	CAR2	0.796	PEP4	0.620	XBP1	1.304	PRX1	0.966	PUT4	0.784	YOR289W	0.614	YDR034W-B	1.255
	SDS24	0.962	YKL151C	0.771	DAN4	0.609
Mating and sporulation
	AGA2	2.532	GPG1	1.320	CWP1	1.064	EMI2	0.694	TPK1	0.606	MFA1	1.555	PRM1	1.227
	BAR1	1.034	GSM1	0.678	PST2	0.604	HBT1	1.552	UBI4	1.223	PRM6	0.931	SPO12	0.651
	TCB2	–0.726	FIG1	1.450	FIG2	1.195	RMD5	0.784	FUS1	0.642	PRM7	–0.945
	GSC2	1.377	AFR1	1.145	YOR338W	0.755	AGA1	0.641	RIM4	1.363	PRM2	1.108	FUS2	0.737
	PTP2	0.628	MFA2	1.345	STE2	1.102	KAR4	0.712	SPS100	0.616
Carbohydrate metabolism
	GPH1	2.447	HXT6	1.136	GND2	0.789	PFK26	0.679	HXK1	1.863	HXT7	1.130	YBR056W	0.723
	YLR345W	0.673	AMS1	1.699	GSY2	1.056	HXT5	0.720	RKI1	–0.616	NQM1	1.423	PIG1	0.914
	CIT1	0.717	HXT1	–0.710	GPM2	1.278	GSY1	0.868	UGP1	0.695	GDB1	1.252	GIP2	0.842
	PYK2	0.695	GLC3	1.192	PCK1	0.789	GUT2	0.689
Cell wall
	YGP1	2.467	KDX1	1.176	YPS6	1.017	DSE1	–0.650	EGT2	–0.718	YPS5	1.204	PRM5	1.103
	PIR3	0.988	SUN4	–0.685	PRY3	–1.042
Autophagy
	LAP4	1.229	DCS1	0.981	ATG34	0.878	PAI3	0.738	DCS2	1.025	ALD2	0.892	ATG33	0.743
	ATG19	0.732
Mitochondrial
	FMP16	1.902	CYC7	1.036	AIM17	0.984	MRP8	0.702	COX5B	0.656	CTP1	–0.806	STF1	1.622
	INH1	0.993	OM45	0.918	UIP4	0.699	MPM1	0.644	ALD4	1.305	FMP33	0.986	YNL200C	0.892
	GOR1	0.664	SDH2	0.633
Other
	YPR160W-A	2.593	YDR042C	1.159	VMR1	0.901	YMR181C	0.747	PIC2	0.641	SFG1	–0.605	YGL101W	–0.823
	YIL082W	2.021	YJL133C-A	1.145	LEE1	0.901	YLR312C	0.732	YNL058C	0.637	LIA1	–0.614	YBR191W-A	–0.862
	RTN2	1.860	ROM1	1.143	YOR343C	0.867	YBR139W	0.726	YPL088W	0.632	BSC1	–0.615	YMR317W	–0.907
	YMR196W	1.797	BOP2	1.142	PET10	0.858	YOR192C-C	0.709	YHR052W-A	0.624	NIP7	–0.629	PLB2	–0.923
	NCA3	1.724	CRG1	1.139	YLR307C-A	0.822	YER053C-A	0.692	YPR145C-A	0.623	LYS1	–0.633	YMR046W-A	–1.003
	RNR3	1.683	RTS3	1.115	YLR108C	0.798	COS12	0.690	YCL076W	0.622	HTB2	–0.654	YOL014W	–1.206
	PHM8	1.500	GSP2	1.097	PRY1	0.787	BNA2	0.685	PEX27	0.617	YBL029W	–0.688	YFR052C-A	1.498
	YKR011C	1.053	YDL247W-A	0.783	GAP1	0.684	YLR042C	0.617	YPR002C-A	–0.708	YNR034W-A	1.365	PBI2	0.993
	YBR201C-A	0.780	YOR114W	0.673	VPS73	0.614	ADE17	–0.712	RTC3	1.332	SRL3	0.987	CUR1	0.769
	YNL115C	0.670	REC104	0.611	HTA2	–0.719	YHR138C	1.263	ECL1	0.986	YCL021W-A	0.767	GGA1	0.663
	YHR007C-A	0.608	YNL217W	–0.729	YLR149C	1.179	YCL042W	0.954	YDR379C-A	0.758	HER1	0.657	RGC1	0.603
	ARG8	–0.764	YBR085C-A	1.178	BTN2	0.926	YCL049C	0.758	YBR053C	0.652	YHR177W	0.602	YDL038C	–0.799

High-resolution gene expression analysis of the smt3 mutant revealed that 261 genes were over- or underexpressed as compared to parental cells.

We also observed a notable increase in transcription from silenced mating type and sporulation genes (e.g., MFA1, MFA2, RIM4, and PRM1), as well as several intergenic regions (Fig. S5 A and Table S3); e.g., 47 cryptic unstable transcripts (CUTs) were expressed >1.5-fold higher in the smt3 strain than in parental cells. Together, these data indicate that disruption of SUMO chain synthesis has a wide-ranging negative effect on the maintenance of transcriptional repression. (It should also be noted that, although overall changes in the expression of individual transcripts are not extremely large in these mutants, this number reflects a population average. Because the phenotypes of individual smt3 cells are pleiotropic, we suspect that these averages reflect much larger changes in a smaller subpopulation of cells.)

SUMO chains are required to establish a basal transcription “setpoint” for stress-regulated genes

The transcription of stress-response genes is rapidly increased in response to changes in the extracellular environment (Gasch et al., 2000). To explore the role of SUMO chains in the transcriptional stress response, we subjected parental and smt3 cells to hyperosmotic culture conditions (1 M NaCl for 30 min), followed by a 120-min recovery in isosmotic media. Using real-time qRT-PCR, expression levels of four different mRNAs that are overexpressed in smt3 cells, and which are up-regulated in response to osmotic shock (HSP12, SPS100, GRE1, and HUG1), were monitored. As expected, in parental cells all four of the genes in the test set displayed a rapid increase in mRNA levels in response to hyperosmotic shock (Fig. 5 and Fig. S5 B). After a return to isosmotic media, a gradual decrease in mRNA abundance was observed, returning to pre-stress levels within 60–120 min (Fig. 5 and Fig. S5 B). Consistent with our tiling array data, this gene set was already expressed at higher levels in untreated smt3 strains (Fig. 5 and Fig. S5 B). In response to osmotic shock, the four gene set was up-regulated to approximately the same expression levels (or slightly higher in some cases) as the parental strain, and removal of the stress resulted in a similar gradual decrease in mRNA abundance to near basal smt3 transcript levels (Fig. 5 and Fig. S5 B). Identical results were observed in cells expressing the 3KR SUMO protein (Fig. S4 C). A deficiency in SUMO chain function does not therefore appear to significantly affect the activation kinetics or maximal mRNA expression levels in response to stress, but instead influences the basal transcription setpoint of this highly regulated group of genes.

Figure 5.

SUMO chains are required to establish a basal transcription setpoint for stress-regulated genes. Parental and smt3 strains were grown in YPD and treated with 1 M NaCl for 30 min, then allowed to recover in YPD medium. Aliquots were collected at the indicated time points for RNA preparation. HSP12, SPS100, and DDR2 mRNA were monitored by qRT-PCR and values were normalized to ACT1 levels. Error bars indicate standard deviation from three or more biological replicates

SUMO chain disruption affects multiple aspects of higher-order chromatin organization

Aberrant mitotic chromosome condensation and segregation, transcriptional derepression of stress- and nutrient-regulated genes, and aberrant transcription from intergenic regions suggested that smt3 strains could have a chromatin condensation defect. To this end, we subjected smt3 and parental cells to several different assays of higher-order chromatin structure.

The lacO/lacR chromosome marker system.

To begin to assess how a lack of SUMO chains impacts chromatin structure, we used a yeast strain bearing two lac operon repeat insertions on chromosome IV, separated by ∼450 kb (strain AVY89; Vas et al., 2007). When the lacR-GFP protein is bound to its cognate operon, confocal microscopy can be used to measure the distance between the two GFP foci (Vas et al., 2007). Plasmids encoding the gal-inducible WT or allR SUMO proteins were transformed into this strain, cells were exposed to galactose to induce SUMO protein expression for 16 h, and cells were treated with α factor to synchronize them in G1. The distance between GFP signals was then quantified, as in Vas et al. (2007). In cells expressing the WT SUMO protein, the two GFP foci were 1.19 ± 0.04 µm apart on average, the same as that observed in the untransformed parental strain (Fig. 6, A and B) and similar to measurements previously reported in other laboratory strains (Vas et al., 2007). Notably, in the strain expressing the SUMO allR protein, the mean distance between the GFP-marked chromosome regions was significantly increased (1.45 ± 0.05; P < 0.01; Fig. 6, A and B). Inhibition of SUMO chain formation thus negatively affects chromosome IV compaction and/or organization.

Figure 6.

Higher-order chromatin organization is disrupted in cells expressing the SUMO allR protein. (A) WT SUMO or smt3allR protein expression was induced in AVY89 (lacO/lacR-GFP) cells for 16 h, and the distance between GFP foci on chromosome IV was measured as in Vas et al. (2007). Bar, 5 µm. (B) Data (from >100 cells) are presented in tabular form (values are expressed in micrometers) and as a bar graph with binned distance values, as indicated. Data shown are from a single representative experiment, conducted twice.

Telomere clusters.

The SUMO system was also previously linked to telomere silencing and localization (Chen et al., 2007; Mekhail et al., 2008; Ferreira et al., 2011). During interphase, budding yeast telomeres are clustered into 3–8 foci located near the inner nuclear membrane (INM; Mekhail et al., 2008). To determine if SUMO chains are important for proper telomere organization, we examined the localization of the telomere regulatory protein SIR2 in parental and smt3 strains. As expected, in parental strains, SIR2-GFP was found in a small number of foci near the INM. However, smt3 cells displayed an increased number of (generally smaller) SIR2-GFP foci, and many cells possessed an additional diffuse nuclear SIR2 signal (Fig. 7 A), which indicates widespread SIR2 mislocalization.

Figure 7.

Nucleolar and telomere organization are disrupted in cells expressing the SUMO allR protein. (A) SIR2-GFP was imaged in log-phase cells in parental and smt3 backgrounds. (B–E) GFP-tagged NOP58, NOP13, and NET1 strains were arrested in S phase by HU treatment and released into nocodazole-containing medium. Nucleolar/rDNA area was analyzed by quantifying NOP58 (n > 400), NOP13 (n > 500), and NET1 (n > 1,200) GFP signals. Volocity software was used to automate measurements of GFP signal volume across 9 z stacks. (D) Confocal micrographs of NET1-GFP in parental and smt3 cells. Data shown are from a single representative experiment, conducted twice. Bars, 5 µm. (F) rDNA copy number (relative to the WT strain Y7092) was measured by qPCR using the ΔΔCt method. Experiments were performed in triplicate (where each reaction was also performed in triplicate); error bars indicate standard deviation.

Nucleolar chromatin organization.

The ribosomal DNA (rDNA) genes occur in a tandem array of ∼150 copies in budding yeast laboratory strains, comprising ∼1 Mb of chromosome XII (Johzuka and Horiuchi, 2009), and are organized into a compact structure localized near the INM, the nucleolus (Chan et al., 2011). Transcription of rDNA is tightly controlled, and specialized silencing mechanisms are required to prevent homologous recombination between rDNA repeats and to maintain rDNA copy number (Conconi et al., 1989; Dammann et al., 1995). The SUMO system plays an important (but poorly understood) role in these processes (Takahashi et al., 2008; unpublished data). To better understand the role of SUMO chains in rDNA organization and maintenance, several different nucleolar markers were expressed and analyzed in parental and smt3 cells. Notably, the NOP2-GFP protein exhibited a much more diffuse pattern in cycling smt3 cells (Fig. S5 C), implicating SUMO chain function in the organization of nucleolar DNA. To confirm and extend this result, NOP58-GFP–, NOP13-GFP–, and NET1-GFP–expressing cells were arrested in S phase by HU treatment (0.2 M for 90 min) and released into nocodazole-containing medium (15 µg/ml for 90 min) to synchronize them at the G2/M boundary, when budding yeast rDNA is partially compacted in preparation for mitosis (Guacci et al., 1994; D’Ambrosio et al., 2008). The signal volume of NOP58-GFP and NOP13-GFP was much more variable in smt3 cells as compared with parental strains (Fig. 7, B and C). Similarly, although the total NET1-GFP fluorescence signal intensity was equal in both strains, the signal volume was much more variable, and larger on average, in smt3 cells (P < 0.0001; Fig. 7, D and E). Together, these data indicate that nucleolar DNA organization is also altered in a budding yeast mutant unable to synthesize SUMO chains.

Previous work has demonstrated that a loss of rDNA repeat organization or localization can lead to changes in rDNA copy number (Takahashi et al., 2008; Chan et al., 2011). Using quantitative PCR (qPCR), we found that the rDNA repeat number is significantly increased in smt3 cells, as compared with their parental counterparts (Fig. 7 F). Similar to chromosome IV and telomeres, rDNA compaction and/or organization (as judged by several different GFP markers and quantitation of rDNA repeat number) is thus also compromised when SUMO chain function is disrupted.

Discussion

Many transcription factors, coregulators, and chromatin remodeling proteins are SUMO targets (for review see Gill, 2005), and sumoylation of chromatin remodelers in yeast and mammalian cells has been suggested to be required for the formation of a local heterochromatin-like state on some promoters (Uchimura et al., 2006). A recent study indicated that Ubc9 inactivation in S. cerevisiae leads to increased transcription at the inducible ARG1 gene and impaired the ability of these cells to inactivate ARG1 transcription after removal of the activation signal (Rosonina et al., 2010). SUMO has also been reported to be enriched in heterochromatic DNA regions (Uchimura et al., 2006), and sumoylation of the ubiquitous transcription factor Sp3 has been linked to local heterochromatinization (Stielow et al., 2008b), whereas expression of an unsumoylatable Sp3 protein leads to derepression of several tissue-specific genes in mammalian cells (Stielow et al., 2010). Here, we find that disruption of SUMO chains in yeast negatively affects higher-order chromatin organization and the maintenance of transcriptional repression. We propose that a general, widespread defect in chromatin packaging (as reflected by increased distances between two chromosomal markers, disorganized telomere clustering, and altered nucleolar rDNA organization) leads to transcriptional derepression throughout the genome. In this way, SUMO chains appear to play an important role in establishing a basal transcription setpoint. Our data also indicate that SUMO chains are not required for stress-regulated transcriptional activation. However, the precise role of SUMO chains in transcriptional inactivation is not yet clear: although SUMO chains are clearly required to maintain transcriptional repression in yeast, they do not seem to be required for at least a partial inactivation of transcription after stress (Fig. 5). Additional exploration of the role of SUMO chains in transcriptional inactivation may shed further light on these findings.

The SUMO system has also been implicated in DNA replication and DNA damage repair (Makhnevych et al., 2009; Cremona et al., 2012). Our data specifically implicate SUMO chains in DNA replication–associated DNA damage. How might this damage occur in smt3 cells? DNA lesions can block the progress of DNA replication forks. Although replication can restart via repriming downstream of the damaged area (Heller and Marians, 2006), the repriming process generates a single-stranded gap near the lesion (Lehmann and Fuchs, 2006). To fill these gaps, the template switch (TS) pathway may be used. TS utilizes undamaged DNA on the sister chromosome via a mechanism that shares similarities with homologous recombination (Goldfless et al., 2006; Branzei and Foiani, 2007). The TS process gives rise to X-shaped DNA intermediates, with biochemical properties similar to pseudodouble Holliday junctions (for review see Klein, 2006). A failure to resolve these structures can lead to DNA damage and chromosomal rearrangements. The RecQ helicase Sgs1 (the budding yeast orthologue of the human BLM protein) is required for resolution of these structures (Liberi et al., 2005; Wu and Hickson, 2006), and the ability of Sgs1 to promote their dissolution is regulated by the SUMO pathway (Branzei et al., 2006). Recent work has also demonstrated that the Smc5-6 complex, Esc2, and the Mms4-Mus81 complex (all of which were detected in our SGA and HCS analyses) play important roles in resolving these recombination intermediates on damaged DNA templates (Branzei et al., 2006; Chavez et al., 2010). Mutant smc5-6 and smc6-9 cells are sensitive to MMS treatment, and undergo aberrant mitosis in which chromosome segregation of repetitive regions is impaired (Torres-Rosell et al., 2005). Consistent with these data, the SUMO mutant strain smt3-331 was isolated in a high-content screen for cells unable to properly segregate GFP-labeled chromosomes (Biggins et al., 2001). Our smt3 mutant shares several similarities with this group of strains, implicating SUMO chains in the same processes.

It is important to note that the SUMO proteins may be regulated by posttranslational modifications such as phosphorylation, acetylation, and ubiquitylation (Matic et al., 2008; Mazur and van den Burg, 2012). However, acetylation of lysine residues in the human SUMO proteins inhibits (or has no effect on) SUMO–SIM interactions, and K-to-R mutations at acetylation sites do not affect their activity in transcriptional repression and protein binding assays (Ullmann et al., 2012). As reported here, the 3KR yeast SUMO mutant has the same effect as the allR SUMO protein in assays of division time, ploidy, and mRNA expression levels. K-to-R mutations are thus not likely to significantly disrupt SUMO function, other than to abrogate chain synthesis. Nevertheless, because we do not completely understand how the yeast SUMO protein may be posttranslationally modified, we cannot rule out this possibility.

Finally, our data also have clear implications for human disease. For example, a SUMO chain deficit could render cells more susceptible to chemotherapeutic agents because of a heavier DNA damage load and increased chromosome missegregation. Indeed, although the molecular details of this phenomenon are not yet understood, a recent study linked SUMO E1 mutations to improved outcome in some (Myc mutation–associated) breast cancers (Kessler et al., 2012). Combined with our observations, these data suggest that targeting of the SUMO system (and in particular SUMO chain synthesis) could have therapeutic value.

Materials and methods

Yeast strains and plasmids

S. cerevisiae strains used in this study were derivatives of the BY4741/2 haploid cells, unless otherwise specified, and are listed in Table S4. All yeast genetic manipulations were performed according to established procedures. Unless otherwise noted, yeast strains were grown at 30°C to mid-logarithmic phase in YPD or selective minimal (SM) media supplemented with appropriate nutrients and 2% glucose. Transformations were performed as described previously (Delorme, 1989). The AVY89 strain was kindly provided by D.J. Clarke (University of Minnesota Medical School, Minneapolis, MN).

Construction of smt3 strains

Multistep PCR was used to generate a product containing the NatMX cassette from p4339, 207 bp of the Smt3 5′ UTR from genomic DNA, the smt3 coding DNA sequence from Bylebyl et al. (2003), and 273 bp of the Smt3 3′ UTR from genomic DNA. The resulting product was used to transform yeast strains as in Gietz and Woods (2002). See Table S5 for primers.

Whole cell lysate preparation, affinity purification, SDS-PAGE, and Western blotting

Whole cell lysates were prepared by alkaline lysis and trichloroacetic acid protein precipitation of cell pellets derived from 10-ml cultures. Protein pellets were resuspended in SDS-PAGE sample buffer, sonicated for 10 s, and incubated at 90°C for 5 min before SDS-PAGE. Proteins were transferred to a nitrocellulose membrane (Pall) and probed with HA.11 (Covance), anti-Smt3 (Covance), or anti-actin (EMD Millipore). Proteins were visualized with secondary HRP-conjugated anti–mouse or anti–rabbit antibodies (Bio-Rad Laboratories) and ECL (Immuno-Star HRP; Bio-Rad Laboratories).

Recombinant protein purification and quantification

pGEX-6P-1-SMT3 or pGEX-6P-1-smt3allR, encoding an N-terminal GST moiety fused to the SMT3 or smt3 coding regions (1–294), was constructed using standard cloning techniques, and verified by DNA sequencing. The pGEX-6P-1-SUMO proteins were expressed in BL21 Escherichia coli induced with 2 mM isopropyl-β-D-1-thiogalactopyranoside at 16°C for 16 h. Proteins were purified using MagneGST glutathione particles (Promega), according to manufacturer’s instructions. WT and allR SUMO proteins were cleaved free of the GST moiety using a 4% PreScission Protease solution (GE Healthcare) at 4°C for 16 h. Proteins were assessed for purity using SDS-PAGE and quantified with a Bradford assay. Coomassie-stained SDS-PAGE gels were digitized using a scanner (Epson), and intensity measurements on individual bands were made on the digitized images using Photoshop CS4 (Adobe) software.

In vitro sumoylation

Assays were performed with 150 ng of E1 (AOS1/UBA2), 1 µg of E2 (UBC9), 2 µl of 10× sumoylation reaction buffer (200 mM Hepes, pH 7.5, 50 mM MgCl2, and 20 mM ATP), 1 µg of SUMO, and 250 ng of biotinylated substrate (all proteins from Boston Biochem). The reaction mixture was incubated at 30°C for 2 h, then quenched with SDS-PAGE sample buffer. Reactions were analyzed by SDS-PAGE followed by Western blotting using streptavidin-conjugated HRP (Bio-Rad Laboratories). After transfer to a nitrocellulose membrane (Pall), proteins were visualized using a 2% solution of Ponceau S in 1% acetic acid.

Electron microscopy

Samples were prepared as in Wright (2000), and visualized on a transmission electron microscope (H-7000; Hitachi). In brief, cells were fixed with 4% glutaraldehyde at room temperature for 5 min. Cells were washed and secondary fixed with 2% potassium permanganate at room temperature for 5 min. Cells were then washed and overlayed with 1% uranyl acetate for 1 h at room temperature. Cells were then dehydrated by incubating in increasing amounts of ethanol over an 8-h period. Next, cells were infiltrated in Spurr’s resin and samples were polymerized in embedding mold at 60°C for 48 h. 90-nm-thin sections were mounted on 200 mesh copper grids and stained with lead citrate for 5 min before observation with the transmission electron microscope (H7000) at 75 kV. Images were captured in TIF format.

Oxygen consumption rate measurements

Cultures were grown overnight (O/N) in YPD media and diluted in the morning to OD600 0.1 in fresh YPD media. 1 ml of OD600 0.3 culture was collected, washed twice with 50 mM potassium phosphate buffer, pH 6.8, and resuspended to OD600 0.3. Resuspended cells were used to seed XF96 plates (Seahorse Biosciences). Plates were centrifuged at 2,000 rpm for 2 min, then allowed to rest for 30 min at 30°C. The Seahorse sensor cartridge was rehydrated O/N as per the manufacturer’s instructions. XF96 culture plates and sensor cartridge were mated and placed in a Seahorse instrument, set to maintain temperature at 30°C. An initial wait time of 20 min was added to allow equilibration of the culture to instrument conditions. After 1 min of mixing, a 1-min wait time was also included to allow for cell settling, before measuring for 2 min. Three measurements were taken for the basal reading, before the addition of azide to a final concentration of 0.05% in media. Three additional readings were then taken. The mean of the three readings across the 2-min span was calculated for each well. Six wells were used for each strain.

High-content microscopic screen

An array consisting of 384 strains (Table S3) from the yeast GFP collection (Huh et al., 2003) expressing proteins previously demonstrated to display altered localization or intensity in response to replication stress (Tkach et al., 2012) was constructed and crossed with the smt3 mutant (smt3) using SGA (Tong and Boone, 2007) to yield 384 GFP-ORF strains bearing the smt3 allele. GFP protein localization and relative steady-state abundance for each strain in the WT and smt3 mutants were determined essentially as described in Tkach et al. (2012). In brief, cultures were grown to mid-log phase in low-fluorescence medium and transferred to 384-well slides at a final density of 0.045 OD600/ml. Four images per well in the green and red channels (800 ms exposure) were simultaneously acquired, imaged using a high-throughput confocal microscope system (EVOTEC Opera; PerkinElmer) with quad-band dichroic filter (405/488/561/653). The images were blinded and scored manually for localization and relative abundance changes versus the WT GFP-ORF (Huh et al., 2003). A brief description was recorded for each protein undergoing a change in the smt3 or pro-smt3 strains.

Confocal microscopy

Mid-log phase cells were collected from 1-ml cultures, washed in brief in H2O containing 2% glucose, and mounted on a glass slide. Cells were imaged at room temperature using a 100×/1.40 NA Plan-Apochromat lens on an inverted microscope (IX80; Olympus) fitted with a spinning disk confocal scanner unit (Yokogawa CSU10; Quorum Technologies, Inc.) and a 512 × 512 EM charge-coupled device (CCD) camera (Hamamatsu Photonics). Diode lasers at 561 nm (RFP), 491 nm (GFP), and 405 nm (DAPI) were used for excitation combined with the following filter sets: 500/20 nm, 430/10 nm, and 555/28 nm. The system was controlled with Volocity 5.5 software (PerkinElmer). The CCD camera was operated at maximum resolution. Exposure times, gain, and sensitivity varied by protein; however, the same settings were used in WT and smt3 strains. Settings were maintained for all subsequent images of the same strain. Cropping and gamma adjustments of images were performed using Volocity (image export) and Photoshop CS4.

For experiments requiring fluorescent labeling of vacuoles, FM4-64 was added to culture media to a concentration of 20 µM and incubated at 30°C for 30 min. Cultures were washed twice with media, then resuspended in fresh media and allowed to grow for another hour before imaging. To achieve hypertonic shock, cells were treated with 0.4 M NaCl for 10 min before imaging. To achieve hypotonic shock, cells were treated with 20 mM MES for 10 min before imaging. Nine z stacks 0.4 µm apart were acquired. Exposure time, sensitivity, gain, laser power, and binning were kept constant between all strains.

For fluorescent mitochondrial labeling, the plasmid pVT100U-mtGFP (Westermann and Neupert, 2000) was transformed into strains using electroporation. Strains were grown O/N, diluted to an OD600 of 0.2, and allowed to grow for 3 h. 0.1 µM MitoTracker red CMXRos (Invitrogen) and 7.5 µg/ml DAPI (Biotium, Inc.) were added to the culture media and incubated at 30°C for 2 h. Cells were then washed once with 1 M sorbitol, resuspended in 0.5 ml of 1 M sorbitol with 30 µl of 37% formaldehyde, and left on the benchtop for 5 min with occasional vortexing. Cells were then pelleted and resuspended in 50 mM Tris, pH 7.4, and stored at 4°C until they were imaged. Nine z stacks 0.4 µm apart were acquired. Exposure time, sensitivity, gain, laser power, and binning were kept constant between all strains.

For nucleolar/rDNA condensation experiments, cells were grown O/N in CSM His‒ media with 2% glucose at 26°C. The next morning, 1 ml of OD600 0.3 cells were collected and resuspended in YPD containing 0.2 M HU for 90 min at 30°C. Cells were washed three times with water and resuspended in YPD containing 15 µg/µl nocodazole for 90 min at 30°C. Cells were then washed once with 1 M sorbitol and resuspended in 0.5 mL of 1 M sorbitol. Formaldehyde was added to 2% (final) and cells were incubated at room temperature for 5 min, with gentle vortexing every 30 s. Cells were then pelleted and resuspended in 50 mM Tris, pH 7.4, and stored at 4°C until they were imaged. Nine z stacks 0.4 µm apart were acquired. Exposure time, sensitivity, gain, laser power, and binning were kept constant between all strains. Volocity software was used to automatically identify cells using brightfield at 8% threshold cutoff (also a cutoff of >2 µm3). Within objects identified as a cell, the fluorescence intensity in the GFP channel was measured within 2 SD of the mean, and the volume occupied by this fluorescence signal was computed. The Mann-Whitney test was used to compare means of fluorescence volumes. To represent data graphically, volumes were binned and shown as a percentage of the population.

Flow cytometry analysis

Approximately 107 mid-log phase cells were resuspended in 70% EtOH for fixation. Flow cytometry analysis was performed with a flow cytometer (FACSCalibur; BD) and CellQuest Pro software (BD). DNA was stained using the fluorescent dye Sytox green (Invitrogen) at a 1:5,000 dilution. Data were analyzed using a free version of Cyflogic (CyFlo Ltd.).

Cellular glycerol levels

To determine total glycerol content, a 1-ml aliquot of YPD grown cells (OD600 = 0.6) was collected by centrifugation, washed twice with water, and resuspended in 0.5 ml of 100 mM Tris, pH 7.4. Samples were boiled for 10 min and centrifuged at 16,000 g for 15 min (4°C), and 10 µl of the supernatants were assayed for glycerol content. Glycerol concentration was determined colorimetrically with a commercial kit (EnzyChrom Glycerol Assay kit, EGLY-200; BioAssay Systems), according to the manufacturer’s instructions.

SGA analysis/SGA correlation analysis

SGA and correlation analyses were conducted as in Baryshnikova et al. (2010) and Costanzo et al. (2010). In brief, smt3 query strains were crossed with 3,885 nonessential deletion mutants to generate double mutants via several selection steps. The fitness of double mutants was evaluated by measuring colony size in an automated fashion (see Baryshnikova et al., 2010 for details). Genetic interaction profile similarities were measured for all query and array gene pairs by computing Pearson correlation coefficients (PCC) for the complete genetic interaction matrix in Costanzo et al. (2010) and the SGA with the smt3. SGA was conducted using two different clones of the smt3 mutant (one expressing the pro-SUMO protein and one expressing the mature SUMO polypeptide) in the Y7092 SGA parental strain. Genes identified to be synthetic sick in both screens were considered to be true positives.

Transcriptome analysis

WT and smt3 strains were grown to mid-log phase in YPD media. Samples were centrifuged and snap-frozen. Total RNA and single-stranded cDNA were prepared according to Juneau et al. (2007), except that actinomycin D was added to a final concentration of 6 µg/ml during cDNA synthesis to prevent antisense artifacts (Perocchi et al., 2007). In brief, RNA was extracted with hot phenol from mid-log phase cultures, and total RNA was treated for 10 min at 37°C with RNase-free DNaseI, repurified using the RNeasy Mini kit (QIAGEN) and eluted with 1× Tris-EDTA buffer, pH 8.0. Single-stranded cDNA was synthesized in 200-µl reactions containing 0.25 µg/µl total RNA, 12.5 ng/µl random primers, 12.5 ng/µl oligo(dT)12-18 primer, 15 units/µl SuperScript II (Invitrogen), 1× first strand buffer, 10 mM DTT, 6 ng/µl actinomycin D, and 10 mM dNTP. After cDNA synthesis, RNA was degraded with 1/3 volume of 1 M NaOH incubated for 30 min, and an addition of 1/3 volume of 1 M HCl was used to neutralize the solution before cleanup with the MinElute Reaction Cleanup kit (QIAGEN). cDNA was fragmented with 2.1 units/µl DNaseI and labeled in 50 µl reactions containing 0.3 mM GeneChip DNA labeling reagent, 1× terminal transfer reaction buffer, and 2 µl of terminal deoxynucleotidyl transferase (Promega) for 60 min at 37°C. Labeled cDNA was hybridized to arrays for 16 h at 45°C. Raw data from Affymetrix GCOS software (.CEL format) was analyzed with Affymetrix Tiling Analysis software (TAS; http://www.affymetrix.com/partners_programs/programs/developer/TilingArrayTools/index.affx). Expression levels were mapped to the chromosomal map from the Saccharomyces Genome Database and are available for download as supplemental .bar files.

qRT-PCR

Strains were grown O/N, diluted to OD600 0.2, and grown to 0.6. Cultures were shocked with 1 M NaCl for 30 min, then allowed to recover in fresh YPD media for 120 min. 5-ml culture aliquots were collected at the indicated time points and snap-frozen. The MasterPure Yeast RNA Purification kit (Epicenter) was used according to manufacturer’s instructions to prepare purified RNA. RNA quality (RIN) was analyzed using an Agilent 2100 Bioanalyzer and RNA quantity was estimated with a spectrophotometer (Nanodrop 1000; Thermo Fisher Scientific). qRT-PCR primers were designed using the cDNA for each desired target with qPCR settings in Primer3Plus (see Table S5; Untergasser et al., 2007). 40 ng of template RNA and 50 nM of each primer were used with the Power SYBR green RNA-to-CT 1-Step kit (Applied Biosystems) in 20-µl reactions, as per the manufacturer’s instructions, on a qPCR system (Mx3000P; Stratagene). Act1 was used as a control for ΔΔCt-based relative quantification (Livak and Schmittgen, 2001).

qPCR

Strains were grown O/N in YPD (200 µg/ml +cloNAT or 100 µg/ml +G418 for mutants), diluted in the morning to OD600 of 0.2. Cultures were grown to OD600 0.8, and 10-ml aliquots were snap-frozen. A MasterPure Yeast DNA Purification kit (Epicentre) was used to isolate genomic DNA according to the manufacturer’s instructions. Samples were incubated with DNase-free RNase for 2 h in TE before storing at −20°C. DNA was quantified with a spectrophotometer (Nanodrop 1000). A Power Sybr green PCR kit was used in 20-µl reactions containing 1 ng of DNA and 50 nM of each primer, as per the manufacturer’s instructions, on a qPCR system (Mx3000P). Primers were as follows: rDNA-F, 5′-TACTGCGAAAGCATTTGCCAAGGACG-3′; rDNA-R, 5′-TCCCCCCAGAACCCAAAGACTTTGAT-3′; act1-F, 5′-CTTTCAACGTTCCAGCCTTC-3′; and act1-R, 5′-CCAGCGTAAATTGGAACGAC-3′.

Online supplemental material

Fig. S1 shows data indicating that the smt3 strains exhibit markedly increased chromosome segregation defects, and additional spot assays. Fig. S2 contains representative images from the HCS showing mislocalized spindle proteins, and highlights characteristics of an environmental stress response in SUMO mutant strains. Fig. S3 contains additional EM images, as well as measurements on internal glycerol content and FM4-64 vacuole-stained images. Fig. S4 contains doubling time, FACS, and gene expression data for strains overexpressing an smt3 protein. Fig. S5 displays a summarized image of microarray data for the smt3 strain, an expression profile for the Gre1 mRNA during stress response, and representative images from the HCS for NOP2-GFP. Table S1 contains localization and intensity data from the HCS, as well as GO analysis. Table S2 contains all SGA and correlation analysis data, as well as GO analysis. Table S3 contains expression data for all ORFs and known CUTs. Table S4 contains details on strains used in this study. Table S5 lists the sequences of all primers used in this study. Two .bar files, containing expression level changes mapped to chromosome location, are available for download. Online supplemental material is available at http://www.jcb.org/cgi/content/full/jcb.201210019/DC1. Additional data are available in the JCB DataViewer at https://doi.org/10.1083/jcb.201210019.dv.