Resource
Features of the Chaperone
Cellular NetworkRevealed through Systematic Interaction MappingGraphical Abstract
Highlights
d Chaperone interaction map elucidated through integrated
proteomic approaches
d A large functional chaperone supercomplex, named NAJ
complex, is revealed
d Many chaperone interactors are found to form condensates
d The AAA+ ATPases Rvb1 and Rvb2 form condensates under
nutrient starvation
Rizzolo et al., 2017, Cell Reports 20, 2735–2748September 12, 2017 ª 2017 The Author(s).http://dx.doi.org/10.1016/j.celrep.2017.08.074
Authors
Kamran Rizzolo, Jennifer Huen,
Ashwani Kumar, ..., Charles Boone,
Mohan Babu, Walid A. Houry
[email protected] (M.B.),[email protected] (W.A.H.)
In Brief
Rizzolo et al. use a systematic integrative
approach combining physical and
genetic interaction data to construct a
comprehensive chaperone network. This
analysis revealed the presence of a large
functional chaperone supercomplex, the
NAJ complex. Furthermore, many
chaperone interactors were found to form
condensates.
Cell Reports
Resource
Features of the Chaperone Cellular NetworkRevealed through Systematic Interaction MappingKamran Rizzolo,1,11 Jennifer Huen,1,11 Ashwani Kumar,2,11 Sadhna Phanse,3,4 James Vlasblom,4 Yoshito Kakihara,1,12
Hussein A. Zeineddine,1,13 Zoran Minic,4 Jamie Snider,3 Wen Wang,5,6 Carles Pons,7 Thiago V. Seraphim,1,4
Edgar Erik Boczek,8 Simon Alberti,8 Michael Costanzo,3 Chad L. Myers,5,6 Igor Stagljar,1,3,9 Charles Boone,3,9
Mohan Babu,4,* and Walid A. Houry1,10,14,*1Department of Biochemistry, University of Toronto, Toronto, ON M5G 1M1, Canada2Department of Computer Science, University of Regina, Regina, SK S4S 0A2, Canada3The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada4Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada5Department of Computer Science & Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA6Program in Bioinformatics and Computational Biology, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA7Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain8Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany9Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada10Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada11These authors contributed equally12Present address: Division of Dental Pharmacology, Niigata University Graduate School of Medical and Dental Sciences,
2-5274 Gakkocho-dori, Chuo-ku, Japan13Present address: Department of Neurosurgery, The University of Texas Medical School at Houston, Houston, TX, USA14Lead Contact
*Correspondence: [email protected] (M.B.), [email protected] (W.A.H.)http://dx.doi.org/10.1016/j.celrep.2017.08.074
SUMMARY
A comprehensive view of molecular chaperone func-tion in the cell was obtained through a systematicglobal integrative network approach based onphysical (protein-protein) and genetic (gene-geneor epistatic) interaction mapping. This allowed us todecipher interactions involving all core chaperones(67) and cochaperones (15) of Saccharomyces cere-visiae. Our analysis revealed the presence of alarge chaperone functional supercomplex, whichwe named the naturally joined (NAJ) chaperone com-plex, encompassing Hsp40, Hsp70, Hsp90, AAA+,CCT, and small Hsps. We further found that manychaperones interact with proteins that form focior condensates under stress conditions. Using anin vitro reconstitution approach, we demonstratecondensate formation for the highly conservedAAA+ ATPases Rvb1 and Rvb2, which are part ofthe R2TP complex that interacts with Hsp90. Thisexpanded view of the chaperone network in the cellclearly demonstrates the distinction between chap-erones having broad versus narrow substrate speci-ficities in protein homeostasis.
INTRODUCTION
Molecular chaperones are a highly interactive group of cellular
proteins that fulfill central roles in all aspects of protein homeo-
Cell ReportThis is an open access article und
stasis, including protein folding, assembly, and unfolding of
substrates. Chaperones are typically categorized into families
based on their sequence similarity and function. Proteins of
the major chaperone families in the budding yeast Saccharo-
myces cerevisiae are 2 Hsp90s, 14 Hsp70s, 22 Hsp40s, 8
CCTs, 1 Hsp60, 1 Hsp10, 6 prefoldins, 5 ATPases associated
with diverse cellular activities (AAA+), 7 small heat shock pro-
teins (sHsps), and 1 calnexin (total of 67; Table S1; Figure 1A).
In addition, the Hsp70s and Hsp90s function with 4 and 11
partner proteins, respectively, that are termed cochaperones
(total of 15; Table S1; Figure 1A). Despite many mechanistic
and functional studies, the spectrum of cellular substrates
and many of the functions mediated by these chaperones
remain largely undetermined.
Given their involvement in many cellular protein homeostasis
processes, it is necessary to study chaperones at a global
systems level. In 2005, our group published a comprehensive
physical (protein-protein) and genetic (gene-gene or epistatic)
analysis of the yeast Hsp90 chaperone interaction network,
showing the broad role this central chaperone has in many
cellular pathways (Zhao et al., 2005). Subsequently, in 2009,
we published a physical interaction atlas for 63 yeast chaper-
ones (Gong et al., 2009). Here, genetic interaction data from syn-
thetic genetic array (SGA) technology in yeast (Costanzo et al.,
2016) was combined with protein-protein interactions (PPIs) to
systematically build a comprehensive high-fidelity chaperone
network from a total of 67 chaperones and 15 cochaperones.
The network revealed several features of the chaperone func-
tional distribution in the cell, and it indicated the presence of a
functional chaperone supercomplex required for cellular protein
homeostasis.
s 20, 2735–2748, September 12, 2017 ª 2017 The Author(s). 2735er the CC BY license (http://creativecommons.org/licenses/by/4.0/).
PCC
0.1
0.8
C
B
0.200-0.20
SGA score
ER
tra
nslo
cati
on S
EC
63
CN
E1
KA
R2
SC
J1
ECM33 ERD1 HAC1 IRE1 YFL032W GPI16 ERO1 MSL5 ADE16 PRP42 SPC3 ALG6 DIE2 YAL058C-A SSS1 YPL245W WBP1 SPC2 SEC62 SEC66 STT3
GIM
4 G
IM3
GIM
2 G
IM1
GIM
6
DAM1 ELP6 CCT1 ARP4 CCT4 CCT5 CCT6
CC
T c
hape
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CC
T4
CC
T1
CC
T5
BRX1 NOP2 QNS1 LCP5 TRF5 RRP15 NOP56 SSF1 DRS1 KRE29 PUP2 KAE1 RPL12A RRP14 RIA1 BRR2 PDR11 RPL11A DIA4 YOR012W RGI1 YER189W YHR086W-A YOR309C
Rib
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62 chaperone and 15 cochaperone genes
4,58
3 ye
ast g
enes
A
2
14
22
1
86 5
7
1 14
11
Hsp90Hsp
70Hsp
40Hsp
60CCT
Prefold
inAAA+
sHsp
Calnex
inHsp
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Hsp70
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apero
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67 chaperones15 cochaperones
Num
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f pro
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s Localization of CCo
D
threshold cutoff(|SGA score|>0.08 and P<0.05)
393,926 CCo GI profile correlation similarities
425,751 double mutants involving CCo(62 chaperones & 15 cochaperones)
22,443 final GIs (13,704 negative & 8,739 positive)
i
ii iii
CC
T3
CC
T4
CCT5
CCT6CCT7
CCT8
CCT1
GIM1
GIM2
GIM3
GIM4
GIM6APJ1
CAJ1CWC23
DJP1
HLJ1
JEM1JJJ1
JJJ3M
DJ2
PAM
18
SCJ1
SEC63SI
S1
SWA
2
XDJ1
YDJ1
SSC3KA
R2LHS1SS
A2
SSA3SSB2SSC1
SSE1
SSE2
SSZ1FES1
MGE1SIL1
SNL1HSP90-TS
HSC82
HSP82AHA1
CDC37
CNS1
CPR6
CPR7
HCH1
PIH1
PPT1
SBA1
STI1HSP104RVB1
RVB2HSP12
HSP31HSP32
HSP33H
SP42C
NE1
HSP10
HSP60M
CX1
Nucleus &Cytoplasm
ER
Mito10
13
59C
CT2
Figure 1. CCo Network Based on GI Data
(A) Bar plot showing the yeast chaperones and cochaperones divided into different families. The colors used for the bars for each CCo family are the same in all
the figures. On the right is a diagram showing the cellular localization of the CCos.
(B) Flow-gram showing the acquisition and analysis of the CCoGI data. CCoGIs were compiled and then GIs and GI profile correlation similarities were obtained.
GIs were filtered at an intermediate threshold (jSGA scorej > 0.08 and p < 0.05), and GI profile correlation similarities were computed. See also Figures S1A–S1D.
(C) Heatmap clustering generated from the GI dataset is shown where the 77 CCos are organized on the x axis as both query and array and 4,583 yeast genes on
the y axis. Below are three examples of GI clusters involving CCos: (i) GI cluster involving Sec63 (see also Figure S1E), (ii) GI cluster of the CCT and the prefoldin
families, and (iii) GI cluster showing predominantly positive GIs between CCT and proteins involved in ribosome biogenesis (see also Figures S2 and S3).
(D) Circos plot (Krzywinski et al., 2009) showing correlations in the GI profiles among CCos. CCo families are grouped and colored according to (A). Profiles were
calculated for all CCo gene pairs andmeasured based on Pearson correlation coefficients (PCCs) from the complete GImatrix. Pairs having a PCC> 0.1 threshold
are plotted. Ribbon width corresponds to the magnitude of the PCC value and the color of the ribbon corresponds to the color of the originating segment.
RESULTS
A Comprehensive High-Fidelity Genetic InteractionChaperone and Cochaperone NetworkThe genome-wide genetic interaction (GI) data to build the chap-
erone and cochaperone (CCo) GI network are described in the
Experimental Procedures, and they were acquired as part of
the effort to map the full GI network in yeast (Costanzo et al.,
2016). For essential genes (Giaever et al., 2002), both Decreased
Abundance by mRNA Perturbation (DAmP) strains (Schuldiner
et al., 2005) as well as temperature-sensitive (TS) alleles were
2736 Cell Reports 20, 2735–2748, September 12, 2017
used (Costanzo et al., 2010, 2016). The initial CCo GI raw data
with 425,751 double mutants were subsequently filtered with a
threshold score cutoff (jSGA scorej > 0.08 and p < 0.05) as
described (Costanzo et al., 2016), and they resulted in
22,443 high-confidence GIs where 13,704 were negative (SGA
score < 0; i.e., double mutants with a more severe fitness defect
than the expected multiplicative effect of combining the individ-
ual mutants, with the extreme case being synthetic lethality) and
8,739 were positive (SGA score > 0; i.e., double mutants with a
less severe defect in fitness than expected) (Table S2; Figure 1B).
The GI data included 62 chaperones and 15 cochaperones with
only 4 missing, namely, Ssa4, Ssc2, Mdj1, and Gim5 (note that
Ssa4, Ssc2, and Gim5 are present in the PPI dataset; see below).
CCo genes had an average of 293 GIs compared to 246 for non-
CCo genes (Figure S1A). Together, the current GI dataset con-
tains three times the number of GIs involving a CCo gene
compared to previous low- and high-throughput studies
compiled from BioGrid (Breitkreutz et al., 2008). Notably, this
comparison shows that our dataset includes almost double the
number of essential genes in yeast compared to previous studies.
To gain insights into the functional ranking of the chaperone
families, we looked at the total number and overlap of GIs among
CCos (Figures S1B–S1D). Hsp70, Hsp40, and Hsp90 chaperone
families tended to have the highest total number and the highest
unique number of GIs (Figures S1B and S1C). Also, the Hsp70
and Hsp40 family members had the highest number of GI inter-
actor overlap with other chaperones (Figure S1D). The Hsp70,
Hsp40, Hsp90, and to a lesser extent CCT members shared
many interactors (Figure S1D).
Figure 1C shows a global view of the positive and negative
CCo GIs encompassing 4,583 yeast genes. CCo genes within
a cluster enriched for a particular molecular pathway were
mostly connected by negative GIs (Figure 1C). For example,
genes encoding for proteins involved in endoplasmic reticulum
(ER) translocation that form a cluster with SEC63, CNE1,
KAR2, and SCJ1 were highly enriched for negative GIs (Fig-
ure 1C, lower panel i). To further verify some of these hits, we per-
formed membrane yeast two-hybrid assays (MYTH) (Snider and
Stagljar, 2016) with Sec63, the essential subunit of the ER trans-
locon containing a J domain, as a bait. Our results showed that
all 6 prey proteins interacting with Sec63 in MYTH (Figure S1E)
had a significant negative GI with SEC63, indicating that a nega-
tive GI with an essential gene is a likely indicator of physical inter-
action. These results further validated the GI data.
Genes that encode for proteins that form part of the same
complex tend to be biased toward a single type of GI (Baryshni-
kova et al., 2010; Costanzo et al., 2016). The type of GI depends
on the essentiality of the genes involved; typically, as mentioned
above, negative GIs were found with essential genes, which was
the case for CCos. For example, the CCT complex, which is
essential for cell viability, displayed mainly negative GIs with
the prefoldin (or GIM) complex (Figure 1C, lower panel ii). This
is consistent with the experimental evidence that CCT and pre-
foldin physically interact and function together to mainly fold
and assemble actin and tubulin in addition to many other sub-
strates (Dekker et al., 2008).
For the essential CCT complex, we observed striking positive
GIs with genes whose proteins are involved in ribosome biogen-
esis (Figure 1C, lower panel iii). This was also observed in stan-
dard liquid growth as shown in Figure S2A. The CCT4-TS strain
grew about 4 times slower at 30�C compared to the wild-type
(WT) strain. In comparison, a TRF5 gene knockout strain (Fig-
ure 1C, lower panel iii) exhibited a slightly slower growth profile
than WT, whereas the double-mutant strain grew better than
CCT4-TS, but not as well as trf5D or WT. TRF5 is part of the
Trf4/Air2/Mtr4 polyadenylation (TRAMP) complex that has a
poly(A) RNA polymerase activity and is involved in post-tran-
scriptional quality control mechanisms (Houseley and Tollervey,
2006). The absence of TRF5 leads to production of misas-
sembled ribosomes (Woolford and Baserga, 2013), which likely
causes a reduction in protein synthesis. This is indeed what we
observed when comparing total protein content of cells at the
same cell density for WT, single-mutant, and double-mutant
strains grown at 30�C to stationary phase (Figure S2B). The total
protein content was lowest for trf5D than for trf5DCCT4-TS dou-
ble-mutant strain, whileWT andCCT4-TS had similar higher total
protein (Figure S2C). Notably, the presence of insoluble proteins
was reduced in the double-mutant strain compared to CCT4-TS
or trf5D single-mutant strains (Figure S2D). Hence, we interpret
the presence of positive GI between the CCT complex and ribo-
some biogenesis genes as resulting from the fact that a CCT-TS
strain exhibits accumulation of toxic misfolded proteins, which is
mitigated by reducing the total number of proteins in the cell
through the reduction of ribosome biogenesis.
CCos Are Highly Biased in Positive GIs with EssentialGenesGiven the observations made with CCT, GIs of CCos against the
essential (i.e., TS or DAmP allele strains) and nonessential genes
were further evaluated. CCos were enriched in positive GIs
against the essential array when compared to the rest of the
tested genes (Figure S2E; p < 0.05). The positive:negative ratio
against the essential array was also found to be significantly
higher for CCos (Figure S2F; p < 0.05). This suggests that,
despite CCos having a larger number of negative GIs in the
network, they are more biased toward positive GIs with essential
genes compared to other genes in yeast.
By performing gene ontology (GO) bioprocess enrichment
analysis of the essential genes interacting with CCos with a
log2 positive:negative GI ratio above zero, significant cellular
processes involved in these interactions were identified (Fig-
ure S3; p < 0.05). Of the 22 CCos, 17 were found to be interacting
with genes that were significantly enriched in distinct bio-
processes (Figure S3). Some CCos displayed enrichments for
positive GIs with diverse bioprocesses, as observed for CCTs
and prefoldins. Other CCos had positive GIs with essential genes
belonging to one or more bioprocesses, such as SSC1with tran-
scription and chromatin organization and SWA2 with proteaso-
mal genes (discussed further below).
The CCo GI Profile Correlation Similarity NetworkThe GI profile of a given gene is composed of a set of positive
and negative GIs with other genes in the genome. Genes whose
GI profiles correlate tend to be part of the same complex or
function in similar pathways (Costanzo et al., 2016). This prop-
erty was used to look at the connectivity between CCos by build-
ing a GI profile correlation similarity network (Figure 1D; Table
S3). The network highlights the inherent functional organization
within and between the CCo families. Highly connected CCo
families form part of discernible biological processes, such as
the CCTs with the prefoldins or the Hsp40s with the Hsp70s (Fig-
ure 1D). We also found that the gene for the constitutive Hsp90
chaperone, HSC82, connects primarily with STI1 (known as
Hop in mammalian cells), consistent with the fact that Sti1 is
one of the main Hsp90 cofactors (Li et al., 2012). Interestingly,
HSC82 also connects with YDJ1, the main cytoplasmic Hsp40
(Figure 1D). Indeed, there are 24 genes that overlap between
Cell Reports 20, 2735–2748, September 12, 2017 2737
Enrichment P-value
1 10-8 10-16
A
BCCT family
CCT2CCT1
CCT4CCT5
CCT6CCT3
CCT7
CCT8
Bioprocesses
Hsp40:SEC63
Cell polarity & morphogenesis
Glycosylation, Protein folding/targeting,
Cell wall biogenesis
Vesicle transport
Ribosome biogenesis,rRNA & ncRNA processing
Respiration, Oxidative phosphorylation,
Mitochondrial targeting
Mitosis & chromosome segregation,
Transcription, DNA repair
Peroxisome
CPrefoldin family
GIM6GIM2
GIM1
GIM3GIM4
D
Hsp70: SSB2F AAA+: HSP104GHsp40: YDJ1E
CCo GI profile correlation similarity network
Figure 2. The GI Profile Correlation Similar-
ity Network of CCos
(A) The GI profile correlation similarity network, left
panel, was constructed using PCCs of GI profile
similarities (edges) for all CCo genes pairs (nodes).
CCos are color coded as in Figure 1A. CCo gene
pairs with profile similarity PCC > 0.1 were con-
nected and plotted using a spring-embedded
layout algorithm in Cytoscape. Genes that have a
similar GI profile are close to each other, and
genes with less similar GI profiles are positioned
farther apart. Subsequently, genes were anno-
tated using the SAFE program (Baryshnikova,
2016), right panel, identifying network regions en-
riched for similar GO bioprocess terms.
(B–G) Regions of the CCo similarity network
significantly enriched for genes with similar GI
profiles are shown forCCT (B),GIM (C), SEC63 (D),
YDJ1 (E), SSB2 (F), and HSP104 (G).
See also Figure S4.
the Hsp90/Hsp90 cochaperones and Ydj1 in the GI profile corre-
lation similarity network. This suggests that Ydj1 might be
involved in the proper regulation of diverse Hsp90 clients rather
than interacting promiscuously.
The GI Profile Correlation Similarity Network ProvidesNew Insights into CCo FunctionsWe built a GI profile correlation similarity network composed of
CCo-CCo and CCo-non-CCo correlation pairs evaluated using
a Pearson correlation coefficient (PCC) threshold >0.1, yielding
a total of 10,443 pairs (Table S3). The spatial analysis of func-
tional enrichment (SAFE) program (Baryshnikova, 2016) was
used to functionally annotate regions enriched for particular
GO cellular bioprocesses (Figure 2A). SAFE highlights regions
that are densely connected with a particular attribute, and, in
this case, the network was evaluated with 4,373 biological pro-
cess terms from GO (Ashburner et al., 2000). The CCo network
contained 402 significantly enrichedGO terms grouped into 7 re-
gions involving 726 genes (Figure 2A).
2738 Cell Reports 20, 2735–2748, September 12, 2017
The network provides insights into the
functional specialization of the CCos. In
general, CCos were found to have broad
specificity and to interact with a large
variety of substrates. Hence, many corre-
lated interactors tended to belong to mul-
tiple cellular processes, and they showed
no significant enrichment in any specific
bioprocesses in the network. However,
both the CCT and prefoldin chaperones
exhibited significant specialization in their
functions (Figures 2B and 2C). Many of
their interactors in the CCo GI profile cor-
relation similarity network were involved
in cell polarity, morphogenesis, mitosis,
chromosome segregation, glycosylation,
protein folding, and cell wall biogenesis
(Figures 2B and 2C). This specialization
might be due to the fact that CCT and prefoldins mainly func-
tion to fold and assemble actin and tubulin (Leroux and Hartl,
2000). Most other chaperones displayed either weak or no
specialization (Figures 2D–2G; Figure S4).
The Hsp90 NetworkThe enrichment landscape of genes correlated with Hsp90 and
its cofactors, including the R2TP (consisting of Rvb1, Rvb2,
Tah1, and Pih1) complex (Kakihara and Houry, 2012), is shown
in Figure 3A. Most of the Hsp90 CCos were dispersed
throughout the GI profile correlation similarity network, and
they typically fell outside the functionally enriched regions,
except for CPR7, CNS1, PIH1, PPT1, and RVB2 (Figure 3A).
To gain further insight into the Hsp90 family interactors, in
Figure 3B we show the GI profile correlation similarity network
consisting of a total of 1,303 genes interacting with Hsp90
and its cofactors. GO annotations for each interacting gene ob-
tained from the enriched regions in Figure 3A were manually
curated to a more specific biological term. The order of Hsp90
A
B
RVB1PPT1
RVB2
PIH1
CNS1
AHA1
TAH1
HCH1HSP82
SBA1
STI1
CDC37HSC82
CPR7
CPR6
Hsp90 family & R2TP
Cell polarity & Morphogenesis
Glycosylation, Protein folding/targeting,
Cell wall biogenesis
Vesicle transport
Ribosome biogenesis,rRNA & ncRNA processing
Respiration, Oxidative phosphorylation, Mitochondrial targeting
Peroxisome
Enrichment P-value
1 10-8 10-16 Mitosis & chromosome segregation, DNA replication & repair
Hsp90-TS
OtherProtein folding, PTM & Metabolism
Actin &Morphogenesis Chromatin & Nucleic acid Vesicle
traffickingRibosomal
& RNAMito Perox
CDS1
ATG29
RIB7
AAC3
PBI1
CST26
YPL276W
SMD1
SUB2
MRPL19
MRPL50
MRPL37
TIF34
GCD1
GCD2
PRP39
GCD7
ANT1
CTR1
SWA2
TAH18
BDH1
HAA1
COQ1 RPS18B
RPS4A
RPS16A
ASC1
RPS30A
RPS22B
RPS17A
RPS21A
RPS9A
RPS8A
RPS0B
PEX1
PEX14
PEX13
PEX3
PEX10
PEX5
PEX2
NMD2
DBP6
RSA3
MAK21
DCP2
NOC3
NOP8
RPL37B
RPL35A
RPL24A
RPP2B
RPL2A
RPL12A
RPL27B
BMT6
JJJ1
SSB2
MIP6
ARX1
MAK5
SBP1
RNA1
TRM1
NOG2 APS3
KXD1
APL6
VMA11
VMA8
PBI2
RAV2
TED1
LST7
PRY3
GVP36 YFR024C
YLR269C
YGL217C
YLR366W
YLR374C
YMR294W-A
YIL032C
AHT1
YLR217W
YIL025C
YLR123C
YKL200CAPM2MRM2
FCF1
APL4
APS1
NHP2
RRS1
APS2
YIP1
VMA3 MEP1 YMC2
STV1 BET1 YNL234W
GRH1 YSY6
SLY41
RCR1
PFY1
RSB1
ROX1
ISU2
UBC7
CRT10
FPR4
SFL1
FUN30
OTU1
KNS1
YGR122W
IXR1
CFD1
OPI1
RRN3
RFX1
TOM70PIK1
IMP2
TOM7ARO2
SCS3
ABZ1
TIM22
TIM23
MAS1
PTC5
CDC21
TIR4
PAU3
SRL1
NFI1
THI2
TBF1
SPN1
HSP42
SUA7
EDS1
CKS1
PHR1
YPL216W
SRL4
SNO3
WRS1
ERG26
COQ2
IRA2
BIO4
COQ8
APN1
RBA50
CAB1
SPF1
UTP30
FRD1
POL5
ADE6
MGL2
PMR1
POX1
ERG8
ERG25
DML1
CHO2YDC1
TSC10
LYS20
ALD6
THR4
BTS1
CHA1
UPS1
FIS1
CUP1-2
BNA5
SMF2
RRG1
MRS6
GDH2
ERG10
TRP1
KRS1
ATF1
BEM4
ATG21
COX11
ARO4
SHM1
IDI1
ILV5
ISF1
YHR020W
RRM3
LCB5
MIH1
WAR1
SEC39
VPS51
VPS53
SEC15
SEC6
SEC31
SEC24
SLM4
GTR2
SEC3
NPL3
RLI1
CCA1
TIS11
DXO1
EBS1
IPK1
PTH1
DBP2
VTS1
MAK11
DBP7
SQT1
BRX1
NOP12
DBP9
RBG2
FES1
REX4
COG6
COG3
GET2
GET3
COG4
COG7
GET1
COG2
COG8
COG5
TRS65
TRS23
BET3
VPS35
PEP8
VPS29
BET5
TRS31
SBE2
COF1
VTA1
BOS1
SED5
YDR338C
VBA5
SRP40
GOT1SEC22
YMR279CSLY1
LSB5YPT6
TVP15
PRM9
SEO1
SYN8
ARR3
SEC1
ENT5
ART10
VBA3
RSN1
RGT2
IWR1
SNF3
VTI1
ERP3
YML018C
YBR220C
FIT1
PIC2
GMH1
YKT6
STL1
COY1
LST4
VPS60
DBF20
CTF4
SPT10
GZF3
JIP5
YAL037W
YPR084W
URA6
GPX1
NOP16
TIR1
PHM8
MXR1
CDC8
CMR3
SPO24
YBL028C
AIM2
YPR078C
TDA8
TMC1
MGT1
FMP41
IFM1
HNT1
NRK1
MHF1
PAB1
YPT1
CAF16
TAD2
HSP150
PAU5
BLM10
SRS2
RTR1
FAS2
SUR1
SAM35
ORM1
CSH1
PIP2
PKP1
FTH1
COX5A
IDH2
LSC2
ATG16
GYL1
COX13
WHI2
CUE4
YDR249C
YBL029C-A
CSE4
ACM1
HBN1
CUE5
CTL1
RNT1
CDC1
DDR48
STN1
NOP6
ATM1
GUD1
PAU19
NST1
DIM1
MRM1
HAS1
TRM3
NOP9
MAK16
SDA1
GSP1
CEX1
RPL16A
RPL42B
RPL43A
RPL40B
RPL21B
RPL11A
RPL20B
RPL19A
RPL43B
ITT1
STP1
TIF6
YRA2
GFD1
FAF1
CDC123
RRP5
NOP2
UTP23
YJL211C
BIO2
PEX31
PIB1
PAU2
INP1
PEX29
TVP18
YGL152C
RRP4
RIX1
PRP21
IPI1
AIR1
NTC20
SHQ1
MTR4
PRP31
LEA1
PRP6
SPP381
PRP4
PRP38
PRP8
RPL22A
RPL3
RPL37A
RPL36A
RPL16B
NOP56
UTP13
UTP18
UTP15
LSM7
UTP4
RPS24A
RPS4B
RPS0A
RPS13
RPL6A
RPP0
RPS23A
YNL089C
VPS63
YHR049C-AYMR306C-A
MBB1
YER097W
YER091C-A PSY1
YKL147C
YJL175W
YIL080W
YIL141W
YKL177W
YKL097C
YDL062W
YPL205C
YPL080C
YCR102W-A
YOR366W
YDL026W
YOR318C
YDL038C
YOR309C
YCR001W
YAL016C-B
YPR197C YBR134W
YPR012WYPR130C
YCL012W
IRC13
YOR277C
YPR076W
YDR095C
YOL099C
FYV1
YDL187C
YOR015W
YOL079W
YDR344C
YDR290W
RRT16
YDR199W
YNL109W
YDR193W
YDR149C
PEX25
PEX22ALT2
ARO1
CCC2 PEX18
PEX19DFR1
SER1
MNE1
PEX21
ARG80
LCB2PHS1
YLR118C
GTT1
SDH2ASP3-3
DCG1
TRP2
TRP3PDR11
RRT5
IRC5
NSA1
SLD3
RAD54 PAU4
AMD1
YDR222W
APT1
ALO1
YFR006W
YOR011W-A
SHE1
YML007C-A
YJR149W
YOR008C-A
YPL260W GRX2
FMN1
ESF1
PDR8
NEJ1
CDC45
PDR15
HST3
ARC1
GUS1
MES1
YJR120W
MGM101
MAE1
RGI1
ENB1
FAA2
IZH1
OXP1
GYP1YJL144W
LCB3
ATG10 YDR444W
BCD1
ENP2
DCD1
HUR1
SUA5
WSS1
RTC3
LCB1
TIM18
LAT1
KGD1
SNC1
TRS20 DAN1
RCY1
EMP47
MST28
TRS85 GEA1RPL21A
RPL26BSPP382
RPL35B
SKI6
RPL24B
RPL9A
RRP42
DIS3
NUP57
PML39
RRP8
DBP3
GRC3
POA1
PET494
KRR1
ABD1PPM2
SEC27
COP1
SEC26
RET3
RET2 ARL3
SEC17
SNC2
SFT2
TUL1
PRE3
PRE2
CUE1
RAM2
CGI121
GID7
YDJ1
BET2
GPI16
GPI8
VID28
GON7
BUD32
GPI17
MAK10
SSA3
HSE1
MAK3
OCA5
OCA1
RSP5
UBP15
TIM44
SEC62
SEC11
GTB1
SBH1
SPC2
PAM16
MGE1
PSY4
YME1
MGR1
WBP1
STT3
OST3
OST1
VPS38
CCT6
PKH1
GPI2
PIL1
ERI1
GPI1
CCT5
GPI19
SSH1
ALG13
SBH2
BRE5
TPS2
BCY1
TPK3
TPK1
CCT8
AOS1
PHB1
VPS30
GIM6
ATG14
GIM2
GIM4
YLR352W
MVP1
SSK2
RCE1
STE6
PBS2
YMR085W
ADI1
DAK1
DCR2
IMH1
STR2
BAT2
YUH1
UFD4
KDX1
KKQ8
YPT52
GLG2
YPS6
SYS1
GWT1
MET3
DAL1
STS1
MNL1
EPS1
MNT3
PIG2
DPH1
HPM1
GSY2
ARV1
MIC60
HRI1
GPI13
YPS1
KIN2
RAD5
COA4
QRI1
USO1
RTK1
ERS1
CIT2
UBP1
MAL31
EFM2
PGI1
ARL1
AGP2
UBC4
AKL1
UBP14
RKM3
ALG1
RKM4
HEM1
RUB1
KIN1
TMN2
PDC2
ASK1
BCH1
CHS5
LRS4
RTT101
MMS22
DAD1
CDC7
CLB4
CLB5
DBF4
CLB6
CLN2
CDC28
CLN3
UBC5
PTP1
THI13
HEM13
TPI1
NUS1
RBS1
AST1
GDB1
YEL1
YPK3
GPI18
MAP2
PKC1
RFA2
PDS1
RFA1
DOP1
MDM12
ARC40
ARC35
ARP2
CLB1
ARC15
CTF13
CDC39
CLB2
CDC36
CBF2
CHS7
CNE1
SSE2
HSP32
SSE1
DJP1
MCX1
SPL2
YHR112C
SCY1
FMP48
MCY1
NIF3
YFR018C
IGD1
CMK1
DDI1
TMN3
ARO10
ARH1
YPQ2
ERD1
ASI2
YNR071C
PPG1
CPR8
SNZ2
DAL82
MPA43
PFA4
TLG2
ATG19
MPD2
ALG6
AKR2
DDP1
LAS17
RUD3
ALG8
YNL108C
EAR1
GPI12
PRC1
YNL010W
PKR1
ASI1
MGR3
CPR2
AAP1
INM1
DOG2
YHL018W
MAL11
YCH1
PMT6
HKR1
FPR2
PRB1
UBC8
MNN1
GDA1
RMT2
UBC6
PPM1ALG5
ISR1
CIN2
ENV7
YIG1
FLC1
RKM1
GPH1
MET16
DPM1
PPQ1
RIM20
CIN1
PEP4
GPB1
VEL1
CDC6
RFU1
TMA17
YBL111C
MIT1
DUN1
YDL057W
GIP3
TFS1
CUE3
MIX23
YNL195C
YLR161W
YJR015W
KIP2
ESS1
PMT7
ECO1
SDD3
YPL257W
PDP3
YJR141W
AFG2
VIK1
SGM1
PHO92
YBL081W
EGO2
YNR065C
MPO1
YPL225W
YCR102C
YNR040W
PIB2
YLR361C-A
YDR514C
YPL229W
TEL1
AIM25
BXI1
BUD17
YDR541C
NPA3
SPC105
MRX3
YPL199C
TMA10
IRC22
MPS1
YBR296C-A
EPO1
HSL1
YHR054C
YDR186C
YER186C
ARF3
YHR045W
CHK1
YKL183C-A
YER084W
YMR130W
CIA2
GLE1
BRN1
RPP1
POP1
POP7
NUP170
RAD23
RAD14
CET1
SMC3
EGD1
YCS4
CEG1
MCD1
SMC1
ABF1
NSE3
SMC5
NSE4
YHR086W-A
FYV4
YER189W
YML108W
LAM4
YKL075C
IES5
YCL001W-A
ASE1
YDR169C-A
BIT2
AIM36
YER076C
YHL048C-A
YOR097C
NSE1
TAF4
TAF9
TAF1
TAF5
SMC6
NSE5
TAF7
NHP10
IES1
GTR1
ISW1
ACT1
NPR2
HAT1
PSF1
ELP2
SLD5
POB3
GAL3
GAL80
SPT16
IKI1
YER053C-A
ANS1
TOF2
YMR209C
RCN2
AIM17
YMR247W-A
SUP45
SUP35
SSL2
TFC6
TFC8
TFC1
SSL1
CCL1
KIN28
TFG2
TFB3
HDA1
RNA14
PCF11
CFT2
HDA3
HDA2
DEP1
RSC3
RXT2
RSC8
RSC6
HTL1
RRN9
RSC58
CDC14RIC1
SLI15
CAB2
SCC2
TEF2
RGP1
CAB5
IPL1
YIL055C
YMR265C
BIM1
COS6
YKR015C
SPG1
YBR200W-A
GPN2
VTC5
OM14
PXA1
HTB2
ORC6
ORC3
RRN7
ORC2
ELG1
RPB2
RPC82
RPC11
RPC34
SNF8
ELA1
SRN2
ECM30
ESA1
EAF5
EAF3
YAF9
SWC4
EAF6
MCM5
MCM3
SEN1APC5
CDC23
CDH1
DPB4
MND2
SWM1
CDC27
APC4
PRI2
POL1
TEL2
DNL4
BUB2
SWD3
CDC24
HAP4
CDC48
POL3
POL32
POL31
TAP42
SLX1
SRP54
CYR1
BDF1
VPS71
CYC8
SAP155
PRI1
SIN4
MED1
NUT1
MED6
MED8
NUT2
SSN8
SRB6
MED7
MBP1
NRM1
CSE2
SSN3
NNF1
MCM7
MCM4
SNF5
RTT102
CHD1
SGV1
WHI5
POL2
RPA34
CTF8
DCC1
DPB3
CTF18
IES3
RTT109
SAP30
SDS3
SIN3
SPO20
NDI1
TUB3GIN4
YPL068CTMA23
AIM29
FMP52
EMC10
YOR376W-A
AIM19
YBR197C
RHO3
RPT2PDS5SCH9
SGF11
SGF29
ECM11 ASA1 LTE1
SSN2
AFR1
CHL4
SSO2
PAM1
IVY1
ROT1
DOM34
INP2
SEC14TAO3
DNA2
AXL2
YHR182W
FKH1
PFS1
KSP1
SPO12
SET2
FLO11
REC107
SSF1
MDH1
GIC1
YHR022C
MCK1
ENT1
HBT1
PPH22
RTN2
VAM10
PRM7
RGA1
VPS21
SHE4
RCN1
RHO4
CWP1
SHE10
MIF2
ERV14
ENT4
HSF1
VPS13
CCW12
KIP3
VPS45
FPS1
SPO74
STU2
DNM1
FPR3
YPT7
MDM30
VRP1
SPR6
MID2
BUD27
CAK1
RIM15
ACS2
IML3
SEC18
MYO2
HCM1
PDE2
IRC15
AIM44
SLK19
IST2
REI1
TOR2
TSC11
SEC4
CDC4
CPR1
CMD1
KOG1
LST8
SNF1
SGT1
CDC53
SPS2
AQY1
RDH54
JEM1
OPY2
TIP41
CDC15
KAR9
PGA3
STU1
CDC12
SPR3
PPH21
CDC10
CDC11
RTS1
MRC1
HSP104
YBR137W
GET4
MDY2
SGT2
TOF1
CKA1
CDC20
PAC11
DYN3
RAD57
HRR25
SPC98
PEA2
BNI1DDC1
CCZ1
SGS1
NUF2
SPC25
NBP1
MOB2
FAR3
VPS64
FAR7
FAR11
FAR10
GLC7
CYK3
MUS81
HYM1
DBF2
LGE1
BRE1
MLH3
MEI5
MRE11
CNB1
CMP2
UME6
TUB2
PAN1
CSM3
LAM6
YBR013C
YGR015C
MTC1
YNL181W
YDL121C
GTT3
YPL060C-A
YNL144C
YGR026W
YEL025C
YJL027C
DGR1
YBR063C
YEL023C
YPL038W-A
YLL056C
YGR035W-A
ECM8
YNL058C
UTR5
YIL175W
ULP1
FKH2
YDL157C
YBR090C
YIL174W
NNF2
MLP1
YMR317W
CHL1
YEL076C
YMR310C
YRB1
OM45
FEX1
CIN8
YIL102C
HGH1
YKR075C
ZDS1
YOR385W
TVP38
RAD61
OPY1
YGR168C
SDC25
YDL211C
ECM33
YEL043W
MTE1
PPT1 RVB1 TAH1SBA1STI1 PIH1RVB2HSC82 HSP82 Hsp90-TS AHA1CDC37 HCH1 CPR6CPR7CNS1
Manually curated bioprocesses
Figure 3. The Hsp90 Family and R2TP Networks
(A) SAFE analysis of the Hsp90 and R2TP on the CCo GI profile correlation similarity network is shown with the annotated bioprocess regions obtained from
Figure 2A. The Hsp90 family and R2TP members are overlaid.
(B) Shown are the GI profile correlation similarities (PCC > 0.1) of interactors for Hsp90 CCos and R2TP genes grouped according to manually curated bio-
processes from (A). Ontology terms were manually curated to reflect a more specific term. Edges are color coded based on the interactor’s respective bio-
process. The sizes of the nodes of Hsp90, Hsp90 cofactors, and R2TP genes reflect the number of interactors. All CCo nodes are color coded as in Figure 1A.
cochaperones based on the number of interactors from most to
least is as follows: CDC37 (291 interactors), CNS1 (250), CPR7
(223), RVB2 (172), PIH1 (131), HCH1 (106), CPR6 (83), AHA1
(67), RVB1 (66), TAH1 (61), STI1 (36), PPT1 (20), and SBA1 (19)
(Figure 3B).
The R2TP complex was initially discovered by our group as an
Hsp90-interacting protein complex in yeast (Zhao et al., 2005),
and it was found to be required for the assembly of other critical
complexes (Nano and Houry, 2013). Rvb1 and Rvb2 are highly
conserved AAA+ ATPases in eukaryotes and are essential for
cell viability. Despite advances in characterizing the R2TP com-
plex, there is no clear understanding of its cellular functions. We
found that members of R2TP have GI profile correlation similar-
ities with genes involved in several bioprocesses (Figure 3B), but
especially between RVB2 and genes encoding for cell polarity/
morphogenesis and mitosis/chromosome segregation/DNA
replication and repair (50% of its interacting genes) and between
PIH1 and ribosomal-related processes (44% of its interacting
genes).
A High-Fidelity Physical Interaction CCo NetworkTo generate a network based on physical and GI data, proteins
physically interacting with CCos were identified by collecting
PPI pairs from several large-scale PPI screens, including ours
(Gong et al., 2009), for all the CCos listed in Table S1. These
studies comprised PPI data obtained by tandem-affinity purifica-
tion (TAP) followed by mass spectrometry using large-scale
approaches to characterize soluble multiprotein complexes
Cell Reports 20, 2735–2748, September 12, 2017 2739
8,518 PPIs: 328 pairs include at least one CCo
Markov clustering (MCL)
Krogan et al.(7,874)
Gong et al.(21,687)
Gavin et al.(2,987)
Babu et al.(21,996)
889
1,813
93
118
69
6 734
36232
8921,289
316 5,134
12,54418,729
600
400
200
05 6 7 9 108
0
0.2
0.4
0.6
0.8
1
Hart-score
CC
o pr
ecis
ion
Num
ber of PP
Is with C
Co
56 CCos
Hart threshold = 6
Hart scoring
+ 189 isolated complexes
370 complexes: 15 multimeric
CCo-containing complexes
non-CCo
CCo (colors correspond to families)
300
500
AAA+ family
Hsp70 chaperonesHsp70 cochaperonesHsp40 family
CCT familysHsp family
Hsp90 chaperonesHsp90 cochaperones
The CCo NAJ complex:
PPT1
TDA10
KAR2
YBR139W
HSP42STI1
TOS1
CCT7TAP42
CCT4CCT3
PLP2
VID27
CCT2SIS1
CCT6
CAJ1
HSP78HSC82
SSA4
ZUO1
SSZ1
YEF3
SSA3
SCP160
HSP26
TIM44HSP82
SCJ1
DJP1JEM1HLJ1
YDJ1
SSC1HSP104 SSE1SSB2
SSA1
SSB1
GRE3
SSA2
ECM10SSQ1
Figure 4. Building the CCo Physical Interaction NetworkSchematic representation of the construction of the PPI CCo network. The first step is the compilation of CCo-containing interaction pairs (CCo-to-non-CCo or
CCo-to-CCo interactions) from major studies shown as a Venn diagram. The total number of interaction pairs from each study is given in parentheses. The
compiled PPIs were then scored and filtered using the indicated threshold as described in the Experimental Procedures (see also Figures S5D and S5E), resulting
in 8,518 PPIs. Subsequently, Markov clustering was applied to define multiprotein complexes. The final PPI network is shown using the spring-embedded al-
gorithm from Cytoscape. A zoom-in of the CCo supercomplex, the NAJ CCo complex, is shown with the size of each node corresponding to the degree of
connectivity. All CCo nodes in the network are colored according to Figure 1A and non-CCo proteins are in gray.
(Gavin et al., 2002; Krogan et al., 2006), membrane protein inter-
actors (Babu et al., 2012), and chaperone interactors in yeast
(Gong et al., 2009), totaling 43,020 interactions (Figure 4).
The overlap between the complete GI (positive plus negative)
dataset and the PPI dataset was significant (p = 2.2 3 10�16;
Fisher’s exact test), and this corresponded to about 5% of the
PPIs and 9%of theGIs (positive plus negative). The highest num-
ber of interaction overlap between the 43,020 CCo PPIs and the
CCo GIs was with negative GIs, followed by the GI profile corre-
lation similarity network, and lastly positive GIs with 1,236, 795,
and 774 common interactions, respectively (Figures S5A–S5C).
We found that the CCos with the highest number of common in-
teractions included theHsp70s (Sse1/2, Ssb1/2, Ssz1, andKar2),
Hsp40s (Ydj1 and Swa2), and the Hsp90 (Hsc82) (Figures S5A–
2740 Cell Reports 20, 2735–2748, September 12, 2017
S5C). Furthermore, in characterizing the physicochemical prop-
erties of CCo interactors, we found that the molecular weight
and protein abundance of interactors in the PPI dataset were
significantly greater compared to those of the GI datasets (p =
1.2 3 10�10 and 1.9 3 10�11, respectively; Mann-Whitney-Wil-
coxon test). This was likely due to a higher number of larger
(>70 kDa) abundant proteins in the cell interacting with CCos.
All other properties were similar across different datasets.
To select high-fidelity PPI interactions, several scoring
methods were evaluated. The goal was to obtain the largest
number of CCo-containing interaction pairs while maintaining
approximately equivalent precision (selected as R70%) against
a reference set of PPIs derived from the CYC2014 catalog of
manually curated protein complexes (Pu et al., 2009). We found
CCos in complexes:1. Cdc37 (Ych020) 2. Sec63 (Ych120)3. Swa2 (Ych011)4. NAJ complex: Hsp90, Hsp70, Hsp40, AAA+, CCT and sHsp- containing complex (Ych005) 5. Gim1-6 (Ych060) 6. R2TP-containing complex (Ych009)7. Sse2 (Ych076) 8. Cwc23 (Ych002) 9. Sil1 (Ych101)
10. Mge1 (Ych156)
Negative PCC(inter-complex)
Positive PCC
CCo-containing complex
Non-CCo-containing complex
|PCC|0.03
0.5
R2TP-containing complexSec63-containingcomplex
1
2
3
45
6
7 8
9
10
RNA pol III
Histone deacetylase RNA
processing
Histone methylation
Transcriptionfactor
RNA pol II
Kinetochore
Anaphasepromoting
N-termacetyltransferase
Actinnucleation
Serine/threoninekinase
DNA pol
ERAD
Myosin
DNA replication
ER transport
Histone deacetylase
Transcriptionregulation
Ribosomebiogenesis
Transport
Cytochrome-c
Mito. ribosome
Mito.enzymes
Metabolicenzymes
snRNP
U1-snRNP
Transcription
Vesicletransport
Proteasome
DNAhelicase
Tubulin
Retromercomplex
Vesicle
Translationinitiation
Elongatorcomplex
Vacuole ATPase
ER translocase
Nuclear pore
Replication factor
Gim1-6-containing complex
Cdc37, kinase & ATP synthase-containing
complex
Mito. protease
OST
Actin motility & nucleation
SSA3TDA10
STI1
SIS1
CCT2 HLJ1
CAJ1
CCT6ZUO1
GRE3 HSP82
TIM44
PPT1TOS1 SSB1
HSP42 SSA2
HSP26
SSB2 JEM1KAR2
SSZ1
CCT7
HSP78SCJ1
SSC1
CCT3
CCT4 DJP1
TAP42
PLP2
HSP104
YBR139W
YDJ1
ECM10HSC82SSE1
IES5
SWR1
KTR6
VPS71SWC3
NHP10
IES1
IES3TAH1
PIH1
ESA1
EAF1
ACT1
ARP4
EAF6
YTA6
RVB2
EAF7
YAF9
SWC4
SWC5
VPS72
SWC7
EAF5
BDF1
ARP6
RVB1
NAJ complex:Hsp90, Hsp70, Hsp40, AAA+, CCT and sHsp-containing complex
RPN12
UBP6RPN10
RPN7
RPT2
RPN5
SEM1POL4 RPN1
RPT6 RPN11RPT1
YSP2
ECM29
SLX5
RPN6
NAS6RPT3
RPT4
SWA2
RPN13
SEC18 SPG5
ESC1
ATP14
YMR118CATG8
DDR48
FIT1
YCK1CDC37
DCW1ATP7YCK2
ATP5
RAD27
SEC63
SEC72SEC66
SEC62
GIM1
BUD27
GIM6
GIM2 GIM3
GIM4
Swa2 & proteasomecap-containing complex
Figure 5. Overlay of the GI Profile Correlation Similarities onto MCL Clusters from PPIs
Shown is a nested network of the GI profile correlation similarities between CCo- (light blue) and non-CCo- (light gray) containing MCL clusters (complexes)
described in Table S5. Positive and negative correlations are shown between complexes and only positive correlations are shown within complexes. CCos
present in each complex are indicated in the bottom-right inset. Six complexes are shown in detail. The size of each node within the complexes corresponds to
the number of interactors. For simplicity, complexes containing only two proteins are not shown.
Hart scoring (Hart et al., 2007) a better predictor of CCo interac-
tions (Figures S5D and S5E). Using a Hart score threshold of 6,
we obtained a finalized network containing 8,518 non-redundant
pairwise associations (i.e., CCo-to-non-CCo, CCo-to-CCo, or
non-CCo-to-non-CCo) (Table S4). Among these, we identified
328 associations involving a CCo with 56 of the 82 CCos
captured (Figure 4; Figures S5D and S5E; Table S4). Markov
clustering (MCL) (Enright et al., 2002) was then employed to
organize the physical network. This resulted in a total of 370
complexes with 15 of them containing a CCo (Figure 4; Figures
S5F–S5K; Table S5). Strikingly, most CCos were found to cluster
together in a specific region of the proteome, specifically
the families of Hsp90, Hsp70, Hsp40, AAA+, CCT, and sHsp
(Figure 4; discussed further below). Such connectivity among
six different CCo families has not been reported before. It should
be emphasized that the MCL complexes obtained represent
functional clusters and not necessarily tightly associating phys-
ical complexes.
A Combined Physical and Genetic CCo NetworkGenes encoding physically interacting or co-complexed proteins
tend to share many GIs in common. By overlaying interactions
based on CCo GI profile correlation similarities onto the pre-
dicted protein MCL complexes obtained from the PPI network,
we built a combined network highlighting CCo complexes (Fig-
ure 5). The complexes are connected based on the average in-
ter-complex GI profile correlation similarities between the genes
in each complex pair. Together, this allows the quantitative and
nested representation of the GI profile correlation similarities be-
tween and within the predicted MCL complexes.
The network shown in Figure 5 displays 10 CCo-containing
complexes with a direct connection to 86 non-CCo-containing
Cell Reports 20, 2735–2748, September 12, 2017 2741
complexes. The Sec63-, Cdc37-kinase-ATP synthase-, R2TP-,
Swa2-proteasome cap-, Gim1-6-, and Hsp90-Hsp70-Hsp40-
AAA+-CCT-sHsp-containing complexes are highlighted in the
figure. We named the Hsp90-Hsp70-Hsp40-AAA+-CCT-sHsp
complex as the naturally joined (NAJ) CCo complex. The pres-
ence of such a chaperone supercomplex clearly suggests a
high degree of interactor overlap and functional coordination or
redundancy among these critical chaperones. The NAJ complex
also highlights the tight cross-talk among the protein homeosta-
sis machinery.
Additionally, we found that complexes that are functionally
specialized tend to have more connectivity in this network than
those that have a broader function (Figure 5). Such is the case
when comparing the Sec63-containing complex with 47 connec-
tions or the Gim1-6-containing complex with 29 connections
against the NAJ and the Cdc37-containing complexes with just
one and two connections, respectively (Figure 5). As expected,
the Sec63-containing complex is well connected with com-
plexes involving various ER processes aswell as with the protea-
some core subunit-containing complex, which likely highlights
the ER-associated degradation pathway. The Gim complex
shows functional specialization by connecting with proteins
involved in the cytoskeleton and transcriptional regulatory pro-
cesses. On the other hand, the Cdc37-containing complex inter-
actions are less specialized, involving diverse functions ranging
from several kinases both at the inter- and intra-complex level,
DNA repair proteins, and interactions with several mitochondrial
complexes such as the ATP synthase (Figure 5). The NAJ com-
plex has a single edge connecting it with actin assembly and
motility. Given that this is a multi-CCo complex with a very broad
involvement in numerous cellular pathways, this connection re-
flects the most common pathway between the NAJ CCo com-
plex and the rest of the proteins.
As shown in Figure S3, the Hsp40 protein Swa2 was found to
have a very strong enrichment for positive GIs with proteasomal
genes. We also see such a strong association between Swa2
and the proteasome cap in Figure 5.SWA2 interacts with 13 sub-
units of the proteasome (RPN1,5-7,10-13 and RPT1-4,6) and the
two proteasome assembly chaperones NAS6 and ECM29. As
well, this complex interacts with the proteasome core particle
complex. Swa2 is a multifunctional protein involved in uncoating
of clathrin-coated vesicles and in interacting with ubiquitin
chains. Together, this suggests that Swa2 may assist in the as-
sembly or regulation of the proteasome. The interaction between
Swa2 and the proteasome has not been reported before in the
literature.
The R2TP complex clusters with members of the histone
exchange complex Swr1, histone acetyltransferase complex
NuA4, and members of the chromatin remodeling complex
Ino80. We also found that the only gene interacting with all three
RVB1, RVB2, and PIH1 is the histone exchange ATPase SWR1
(Figure 5). Importantly, actin is a major interactor of this complex
through the Rvbs by both GI profile correlation similarity and
physical interactions.
The combined network in Figure 5 clearly illustrates the
different degrees of specialization of CCos. On one end, we
find that the Sec63- and Gim-containing complexes have the
highest connections followed by the Swa2-proteasome cap-
2742 Cell Reports 20, 2735–2748, September 12, 2017
containing complex, and, on the other end, we find the NAJ com-
plex with minimal connectivity.
Rvb1/2 Form Condensates in Stressed CellsTo test the predictive value of our interaction data and given the
role of chaperones in preventing protein aggregation and pro-
moting protein folding, we examined CCos that colocalize with
foci-forming proteins on the CCo GI profile correlation similarity
network (Figure S6). We compiled a list of 547 proteins that have
previously been shown either by low- or high-throughput studies
to form foci (or condensates) under heat shock or nutrient limita-
tion (Table S6) (Bolognesi et al., 2016; Narayanaswamy et al.,
2009; O’Connell et al., 2014; Shah et al., 2014; Wallace et al.,
2015).
As shown in Figure S6A, these foci-forming genes are en-
riched (p < 0.05) in regions containing many CCo families, such
as the AAA+, CCTs, prefoldins, small heat shock, Hsp40s, and
notably the Hsp90-R2TP system among others. By extracting
the subset of foci-forming proteins that interact with the
Hsp90-R2TP system, we found that these are highly enriched
in genes involved in ribosome biogenesis and translational pro-
cesses (Figure S6B). Furthermore, these interactions are largely
driven by the CDC37, CNS1, and CPR7 cochaperones and
RVB1, RVB2, and PIH1 components of the R2TP complex (Fig-
ure S6C). Hsp82, Hsc82, and some of the Hsp90 cochaperones
have been previously reported to form foci (Figure S6C; Table
S6). Hence, we investigated whether subunits of the R2TP com-
plex also form foci. No foci formation was observed for Tah1 or
Pih1, but we found Rvb1/2 to form condensates under nutrient
limitation conditions.
Initially, we investigated growth at 30�C in nutrient-rich condi-
tions (YPD). Rvb levels were constant as a function of cell growth
(Figure S7A) and cell cycle (Figure S7B), with Rvb1 levels being
higher than Rvb2.We then generated a strain, WH12, expressing
endogenously tagged RVB1-mRFP and RVB2-GFP (see the
Experimental Procedures) tomonitor Rvb1 andRvb2 localization
throughout the cell cycle. Rvb1 andRvb2 predominantly colocal-
ized to the nucleus (Figure S7C) with a nuclear-to-cytoplasmic
protein (N/C) ratio around 3 to 4 (Figure S7D). Hence, neither
protein expression nor protein localization was significantly
changed under the conditions tested.
Next, the effect of different stresses on Rvb localization was
examined (Figure 6A). Under these stress conditions, Rvb1 and
Rvb2 protein levels did not significantly change, and the two pro-
teins remained predominantly colocalized with an N/C ratio of 3
to 4 (Figure S7E, left panel), similar to that of the N/C ratio in log
phase cells (Figure S7D), with a colocalization Pearson correla-
tion coefficient of 0.81 (Figure S7E, right panel). Cells treated
with water for 1 hr were very heterogeneous, exhibiting various
morphologies with some undergoing apoptosis, and, thus, these
cells were not quantified for N/C ratios.
In carrying out the localization studies, we noticed that a pro-
portion of Rvb1 and Rvb2 forms condensates near the nucleus
under some of the stress conditions tested (indicated by white
arrows in Figure 6A), namely, stationary phase (STAT), growth
in media lacking glucose (�Glc), treatment with 2-deoxy-D-
glucose (DG), and growth in water. It should be noted that
Rvb1 has been reported to form aggregates in cells exposed
EtOH
Mock
NaCl
Rvb1-mRFP Rvb2-GFP Merge
STAT
- N
Rap
MMS
- Glc
DG
g
5 μm
A Rvb2-GFPRvb1-mRFP DAPI
RFP/DAPI GFP/DAPI RFP/GFP
1 μm
DG
Mock
Alexa Fluor DAPI
Rvb2-GFPNup49-mCherry Merge
DG
Mock
B C
D
0
10
20
30
40
50
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5
Distance from nuclear membrane (μm)
Per
cent
of f
oci
rela
tive
to to
tal f
oci (
%)
0.05
1 μm
1 μm
0
10
20
30
40
50
60
0 2 4 6 8 10 12 14Time (mins)
E
DG (2hr)
0+0- 10’ 40’ 60’
Glc
0102030405060708090
100
DG (4hr)
F
befo
retre
atm
ent
Per
cent
cel
ls w
ith fo
ci (%
)P
erce
nt c
ells
with
foci
(%)
H2O
37˚C
Figure 6. Condensate Formation by Rvb1/2
(A) Colocalization of Rvb1 and Rvb2 under various stress conditions. Cells of the WH12 strain were grown to log phase and then treated with various stress
conditions as follows: mock treatment, 10% ethanol (EtOH), 900 mM NaCl (NaCl), heat shock at 37�C (37�C), stationary phase (STAT), minus glucose media
(�Glc), 1 mM 2-deoxy-D-glucose (DG), minus nitrogen media (�N), 0.01% methyl methanosulfonate (MMS), 10 ng/mL rapamycin (Rap), and growth in water
(H2O). White arrowheads in STAT, �Glc, DG, and water treatment point to the Rvb condensates.
(B) Condensate formation by Rvb1 and Rvb2 upon glucose deprivation. Rvb1 and Rvb2 in WH12 strain after treatment with DG for 1 hr exhibit condensates near
the nucleus. White arrow points to a condensate.
(C) Rvb1-FLAG in YK26 (RVB1-FLAG) strain treated with DG (upper panels) or mock treated (lower panels) was labeled with anti-FLAG primary antibodies and
AlexaFluor488 secondary antibodies.
(legend continued on next page)
Cell Reports 20, 2735–2748, September 12, 2017 2743
to a high temperature of 46�C (Wallace et al., 2015), which was
not a condition tested in our study.
Because the Rvb1 and Rvb2 condensates observed here ap-
peared to be specific to glucose deprivation, we examined them
in more detail (Figures 6B–6D; Movie S1). Figure S7F provides a
statistical summary of the distribution of condensates in cells in
stationary phase, �Glc, and DG conditions. Nearly 50% of all
cells in each stress condition harbored condensates, of which
most of the cells had one condensate per cell. Only 31% of cells
contained condensates in the �Glc treatment, possibly due to
the presence of other metabolizable nutrients in the rich media.
Treatment with DG for 2 hr resulted in an increase in the number
of cells harboring three condensates per cell.
We observed that condensate formation occurred for both
Rvb1 and Rvb2 and that these condensates appeared in the
cytoplasm, close to the nucleus (Figures 6A–6D). Figure 6B
shows a closer view of Rvb1 and Rvb2 colocalized at the nucleus
and in the perinuclear condensates (RFP/GFP panel). To ensure
that condensate formation is not an artifact of GFP tagging, we
performed indirect immunofluorescence labeling on the YK26
strain, expressing endogenous Rvb1-FLAG, in the presence of
DG or mock treated (Figure 6C). Immunofluorescence labeling
showed that Rvb1-FLAG also formed condensates close to the
nucleus, indicating that condensate formation is not an artifact
of endogenous GFP tagging.
We then explicitly determined that Rvb condensates resided in
the cytoplasm using the WH4 strain. The strain expresses
endogenous Rvb2-GFP and Nup49-mCherry, which is a compo-
nent of the nuclear pore complex and can be used as a marker
for the nuclear membrane. WH4 strain was treated with DG for
1 hr and subsequently imaged. Rvb condensates appeared on
the cytoplasmic side of the nuclear membrane and localized
very close to the nucleus (Figure 6D, left panel). However, treat-
ment with DG did not affect Nup49-mCherry localization, and the
distance of the condensate relative to the nuclear membrane did
not change at 30 min, 1 hr, or 2 hr of DG treatment. Most of the
condensates were 0.5 mmor less from the nuclear envelope (Fig-
ure 6D, right panel), and their formation depended on nuclear
export by Crm1 (Figure S7G). Condensate formation was rapid
and occurred within 3 min after DG addition (Figure 6E; Movie
S1). These condensates were also rapidly reversible (within
10 min) upon the addition of glucose (Figure 6F; Figure S7H).
The rapid nature of assembly and dissolution suggests that
these condensates are not aggregates but biomolecular con-
densates (Rabouille and Alberti, 2017). We named these con-
densates Rvb bodies instantly triggered by starvation (Rbits).
Rbits did not colocalize with stress granules using Pbp1-GFP
as a marker (Figure S7I) nor with P-bodies using Dhh1-mRFP as
a marker (Figure S7J). Rbits also did not colocalize with protein
(D) WH4 strain (RVB2-GFP and NUP49-mCHERRY) was grown to log phase and
membrane (upper panels) in comparison to mock treatment (lower panels) whe
distance of the condensates from the nuclear membrane of 1,410 condensates in
relative to total condensates measured.
(E) Percentage of cells of WH12 strain forming Rbits after the addition of DG (see
(F) Average percentages of cells with Rbits are represented in a bar graph obtaine
bars represent one SD.
See also Figures S6 and S7.
2744 Cell Reports 20, 2735–2748, September 12, 2017
aggregates marked by Von Hippel-Lindau factor (VHL) (Fig-
ure S7K). We also tested whether protein components in com-
plexes containing Rvb1 and Rvb2 formed condensates that
might be similar to Rbits under glucose deprivation conditions.
The following proteins were assessed for condensate formation:
Pih1, Nop58, and Bcd1, all of which are involved in box C/D small
nucleolar ribonucleoprotein (snoRNP) assembly; Ino80, Taf14,
and Nhp10, components of the Ino80 chromatin-remodeling
complex; Vps71, component of the Swr1 chromatin-remodeling
complex; Tti1, part of the ASTRA chromatin-remodeling com-
plex and of the TTT complex; and Rpb1, Rpb2, Rpb3, Rpb7,
Rpb8, and Rpb9 subunits of the RNA polymerase II machinery.
None of these components appeared to form condensates
resembling the Rbits under our tested conditions. Together
these data suggest that Rvb1/2 form stress-inducible conden-
sates independently of other proteins and stress-inducible
compartments.
Condensate Formation by Rvb1/2 In Vitro Depends onpH, ATP Levels, and CrowdingTo further investigate the properties of Rbits, we sought to
reconstitute these condensates in vitro. Rvb1/2 complex was
purified from E. coli and the proteins were labeled with a fluoro-
phore. Yeast cells respond in many ways to stress: trehalose
production is increased (Wiemken, 1990), ATP concentration
drops (Weitzel et al., 1987), there is increased crowding (Mour~ao
et al., 2014), and there is a drop in pH (Weitzel et al., 1987). Based
on these observations, we tested how these parameters influ-
ence Rvb1/2 in vitro. Purified Rvb1/2 complex was diffuse in so-
lution under neutral pH conditions (Figures 7A and 7B). However,
at pH 6.0, Rvb1/2 formed round condensates of a size of about
0.1–0.3 mm. Rvb1/2 also formed condensates upon the addition
of polyethylene glycol (PEG) of two different lengths to mimic a
crowded cellular environment (Figures 7A and 7B).We quantified
the amount of condensed material by comparing the fluores-
cence intensity of the condensates with the fluorescence inten-
sity outside of the condensates. We found that condensate
formation increased when using a longer PEG chain in compar-
ison to a shorter one (Figures 7A and 7B).
It has been reported that some proteins are able to form liquid,
viscoelastic, or solid compartments upon crowding (Patel et al.,
2015). To probe the material properties of the Rbits, we applied
fluorescence recovery after photobleaching (FRAP). When Rbits
were photobleached, there was no significant fluorescence re-
covery in vitro (Figures 7C and 7D) or in vivo (using the WH12
strain described above; Figures 7E and 7F), suggesting that
these condensates are solid.
We then analyzed whether the condensates dissolved when
the triggering condition was reversed. For the pH-induced
treated with DG for 1 hr. Rvb2-GFP forms condensates outside the nuclear
re no condensates are observed. Right panel shows the quantification of the
800 cells treated with DG for 30min, presented as a percentage of condensates
also Movie S1).
d from images shown in Figure S7H. Experiments were repeated 3 times. Error
PEG20,000
pH7.5
Rvb1/2
PEG3,350
pH6.0
pre-bleach
post-bleach30 sec 5 min 0 sec
Fluo
eres
cenc
e S
igna
lTime (min)
10 μm
no bleach
bleach
0.1 μm
Int in
/ In
t out
pH7.5
pH6.0
PEG3,350
PEG20 k
Intin
/ In
t out
concentration (mM)
10 μmpH 6.0Rvb1/2
pre-bleach
post-bleach30 sec 5 min 0 sec
Fluo
eres
cenc
e S
igna
l
Time (min)
no bleach
bleach
1 μm
A
B
C
D
pH7.5
Rvb1/2
pH6.0
pH7.5
Intin
/ In
tout
pH 7.5 6.0 6.0 7.5
10 μm
G
H
0 %PEG
Rvb1/2
10 %PEG
0 %PEG
Intin
/ In
tout
PEG 0 % 10 % 10 0 %
10 μm
I
J
Intin
/ In
tout
w/o ATP ADP TRH
10 mMATP
pH 6.0
10 mMADP
230 mMTrehalosew/o
Rvb1/210 μm
K
L
M
E
F
DIC GFP
pH =
7.0
pH =
6.0
en
ergy
dep
letio
n
N
Figure 7. Characterization of Rbits
(A) Condensation of purified Rvb1/2 complex was monitored at different pHs or in 10% PEG of different lengths.
(B) Condensate formation was quantified by plotting the fluorescence intensity inside against the intensity outside the condensates.
(C) The material turnover of a Rvb1/2 condensate was analyzed by FRAP.
(D) Quantification of the FRAP results for a bleached (blue) and a non-bleached condensate (green).
(E) The material turnover of Rbits formed in energy-depleted yeast cells was analyzed by FRAP.
(legend continued on next page)
Cell Reports 20, 2735–2748, September 12, 2017 2745
condensates of purified Rvb1/2, pHwas increased back from 6.0
to 7.5, and this caused the condensates to dissolve (Figures 7G
and 7H). Similarly, reducing crowding of PEG-induced particles
by dialysis resulted in the dissolution of the Rbits (Figures 7I
and 7J). In agreement with a recent report that ATP dissolves
condensates (Patel et al., 2017), the presence of ATP or ADP
at 10 mM prevented condensate formation at low pH (Figures
7K and 7L). A titration experiment revealed that ADP was less
potent than ATP in preventing condensate formation of Rvb1/2
(Figure 7M). Trehalose, which is strongly upregulated in
response to stress, did not prevent condensate formation even
at high concentrations (Figures 7K and 7L).
These results suggest that, in response to energy depletion
(i.e., a drop in ATP concentration), a drop in pH and an increase
in crowding could trigger Rvb1/2 condensate formation in cells.
This in turn would indicate that a drop in the cytosolic pH alone,
independent of a metabolic response, should not induce
condensate formation. To test this, we used 2,4-dinitrophenol
(DNP), a protonophore, which can be used to equilibrate the
pH inside the cells with that outside (Munder et al., 2016). Using
this approach on the WH12 yeast cells, we indeed found that
Rbits did not form at pH 6 (Figure 7N) without energy depletion.
This indicates that cytosolic acidification alone is not sufficient to
trigger condensation but rather multiple triggers must coincide
to promote Rbits formation.
DISCUSSION
In this work, we have provided a comprehensive global survey
of the protein homeostasis network of the yeast cell using
genetic and physical interaction data. This analysis offered an
overview of chaperone connectivity that is consistent with the
extensive biochemical data available from the literature (Fig-
ure 1D); it also allowed us to identify novel features of chaperone
function.
The bias of many chaperones toward positive GIs with essen-
tial genes in comparison with the rest of the genes in yeast
(Figures S2E and S2F) suggests an unexpected finding of chap-
erones preventing or reducing cell viability if an essential gene of
the cell is affected. It is possible that this feature of chaperone
function may underlie a novel mechanism by which chaperones
maintain protein homeostasis in the cell. While counterintuitive,
we propose that chaperones maintain protein homeostasis if
nonessential genes are damaged but reduce cell viability if
essential genes are affected. This observation might have direct
implications for protein evolution.
(F) Quantification of the FRAP results for a bleached (red) and a non-bleached R
(G) pH-induced Rvb1/2 condensates could be dissolved by re-adjusting to neutr
(H) Quantification of the data in (G).
(I) PEG-induced Rvb1/2 condensates could be dissolved by dialysis against buff
(J) Quantification of the data in (I).
(K) Effect of the pre-addition of ATP, ADP, or trehalose on Rvb1/2 condensation
(L) Quantification of the data in (K).
(M) Titration of ATP and ADP reveals the critical concentrations needed for the inh
same as the representative panel labeled as w/o in (K).
(N) Rbits formation in DNP-treated yeast was monitored by adding energy deple
Distributions and errors shown in the plots of (B), (H), (J), (L), and (M) are derived
2746 Cell Reports 20, 2735–2748, September 12, 2017
Our interaction network provided a clear distinction between
chaperones that act on multiple substrates and impact many
pathways versus those that have a more defined role in the cell
(Figure 2; Figure S4). Most CCos are found to be proteins with
multiple functions (pleiotropic) with only a few members being
more specialized. In this regard, it was very striking to find that
CCT and the prefoldin complex are rather specific chaperones
and act on particular cellular pathways related to cell polarity
and mitosis (Figures 2B and 2C). This specificity likely arises
because such chaperones act predominantly on a select set of
substrates, such as actin and tubulin, rather than on a multitude
of proteins. Hence, these chaperones have been optimized
through evolution to become critical players in specific cellular
pathways.
The procedure we used to obtain a high-fidelity physical inter-
action dataset (Figure 4) revealed the presence of a huge chap-
erone supercomplex that we named the NAJ complex (Figures 4
and 5). This complex reveals that chaperones are physically
and/or functionally linked in the cell. Chaperones are always in
communication with each other even when present in different
cellular compartments. The presence of such a supercomplex
is likely essential for maintaining cellular protein homeostasis
across different organelles.
Another intriguing finding, highlighting the predictive value of
our network, is the observation that many proteins that have
been reported to form condensates under different growth con-
ditions are also interactors of many CCos. Subsequently, we
confirmed that Rvb1 and Rvb2 form perinuclear condensates,
which we named Rbits, in response to nutrient deprivation (Fig-
ures 6 and 7; Figure S7). Although the physiological role of Rbits
is currently not clear, they could possibly act as protective stor-
age depots for the Rvb1/2 proteins in response to nutrient stress.
In conclusion, our comprehensive analysis suggests that a
global integrative network approach with physical and GI data
is necessary to obtain a more thorough functional overview of
such versatile and modular proteins as the chaperones and their
cochaperones. This study reveals new findings on chaperone
functions, and it serves as a resource for gaining a better under-
standing of the mechanisms that govern protein homeostasis in
the cell.
EXPERIMENTAL PROCEDURES
Collection and Analysis of CCo GIs
Experimental approaches used to obtain high-throughput GI data for all chap-
erones and cochaperones have recently been described (Costanzo et al.,
2016). GIs were extracted for the following: the nonessential 3 nonessential
bits (green).
al pH.
er containing no PEG.
.
ibition of condensate formation. Note that the left panel was chosen to be the
tion medium or phosphate buffer at varying pHs to the cells.
from three images of three biological replicates.
(NxN); essential3 nonessential (ExN); essential3 essential (ExE);DAmP data-
sets for Cct8, Cns1, Cwc23, Jac1, Sis1, and Ssz1; and the Hsp90-TS dataset
(Zhao et al., 2005). An intermediate threshold (p < 0.05 and jSGA scorej > 0.08)
was applied to the dataset. GI profile correlation similarities were obtained by
computing PCC between all chaperone query-query and query-array pairs of
strains in the nonessential, essential, DAmP, and TS GI datasets.
Analysis of Positive and Negative GI Ratios from CCos
To calculate positive GI enrichments, positive or negative GI densities of
CCos against the essential (E) and nonessential (N) arrays were computed
as follows:
Positive GI densityðE or NÞ=positive density of gene XðE or NÞ
Average positive density for all query genesðE or NÞ
The enrichment ratio of positive-to-negativeGI in each dataset (i.e., E or N) was
calculated as follows:
Positive : NegativeðE or NÞ= Positive GI densityðE or NÞNegative GI density ðE or NÞ
Positive GI bias was calculated as follows:
Positive GI biasðE or NÞ= Positive : Negative for E
Positive : Negative for N:
GI density was calculated by selecting a single allele per array gene. The
result was averaged across 100 different randomizations. GI densities of
different query strains with mutations on the same gene were averaged to
obtain a single value per query gene. GI data for pairs of genes belonging
to the same protein complex, obtained by merging Table S1 and a recent
list of protein complexes (Costanzo et al., 2016), were removed for this
analysis.
Construction of the Yeast Chaperone PPI Network
We constructed the yeast chaperone PPI network by compiling four protein
interaction datasets (Gavin et al., 2002; Krogan et al., 2006; Babu et al.,
2012; ChaperoneDB fromGong et al., 2009). To obtain the high-fidelity network
of PPI interactions given in Table S4, themass spectrometry (MS) datasets that
included MALDI Z scores or liquid chromatography-tandem mass spectrom-
etry (LC-MS/MS) confidence values were pre-filtered at a MALDI Z score
threshold R1 and/or an LC-MS/MS confidence score R70%. Next, an inte-
grated interaction score was computed by summing the hypergeometric (HG)
interaction scores (Hart et al., 2007) as Gavin_HG + Babu_HG3 0.5 + Krogan_
HG 3 0.5 + Gong_HG 3 0.08. The final network was derived by applying an
integrated score of 6, selected to maximize chaperone interaction coverage
while maintaining a precision R70% against the CYC2014-derived reference
set (Figures S5D and S5E). The resulting integrated PPI network comprises
8,518 interactions between 2,062 proteins (Figure 4; Table S4). From these
interactions, 328 involved at least one CCo capturing a total of 56 of the 82
CCos used in this study.
We compiled a gold standard reference set of chaperone complexes by
using the CYC2014 catalog (Pu et al., 2009) containing 443 manually
curated non-redundant yeast protein complexes. Using chaperone preci-
sion and number of chaperones as a proxy, a Hart score of 6 was chosen
as threshold (Figure 4; Figures S5D and S5E). Next, given that the densely
connected regions of the PPI dataset suggest associated protein units
likely having a similar function (Wodak et al., 2009), we used the MCL
method (Enright et al., 2002) to identify these regions from within the CCo
interaction network. High-confidence 8,518 PPIs were clustered over a
range of Inflation (I) parameters (1.0 to 5.2). Cluster results were bench-
marked based on overall cluster properties, for example, on number of
clusters and average cluster size (Figures S5F and S5G), CYC2014 (Pu
et al., 2009), complex coverage through precision and homogeneity metric
(Pu et al., 2007) (Figures S5H and S5I), and intra- and inter-cluster func-
tional diversity as measured by the Shannon index of GO biological process
and molecular function terms (Figure S5J). Based on the minimization
of intra-cluster average functional diversity and coverage of known
CYC2014 complex members, an inflation parameter of 1.8 (Figure S5J)
was chosen to generate the finalized clustering of the filtered interaction
network, corresponding to 327 clusters possessing 2 or more proteins (Fig-
ure S5K; Table S5).
Construction of the GI Profile Correlation Similarity Network
Overlaid onto Predicted Complexes from PPIs
Genetic profile similarity scores for all CCo GIs were overlaid onto predicted
MCL clusters. Positive and negative GI profile correlation scores were
computed using PCC. In Figure 5, inter-complex edges represent the average
GI profile correlation similarities between the interacting members from each
complex, and the intra-complex edges represent the GI profile correlation sim-
ilarities between genes within each complex. Proteins in the different MCL
complexes are given in Table S5.
Other experimental procedures are provided in the Supplemental Experi-
mental Procedures.
SUPPLEMENTAL INFORMATION
Supplemental Information includes Supplemental Experimental Procedures,
seven figures, six tables, and one movie and can be found with this article
online at http://dx.doi.org/10.1016/j.celrep.2017.08.074.
AUTHOR CONTRIBUTIONS
W.A.H. designed the study. W.A.H. and M.B. supervised the experiments and
data analyses. K.R. and A.K. collected and analyzed the data. J.H., H.A.Z., and
Y.K. carried out the experiments related to foci formation in vivo. E.E.B. carried
out the in vitro and in vivo experiments related to foci characterization with the
supervision of S.A. T.V.S. helped with strain generation. Z.M. carried out the
mass spectrometry. J.S. designed and implemented the MYTH experiments
with the supervision of I.S. S.P. and J.V. contributed to the bioinformatic ana-
lyses. W.W., C.P., C.L.M., M.C., and C.B. contributed the GI experiments and
helped with the analyses.W.A.H. and K.R. wrote the paper with help fromM.B.
and all other authors.
ACKNOWLEDGMENTS
We thank Dr. Craig Smibert (University of Toronto) for the pRS116 DHH1
plasmid, Dr. Judith Frydman (Stanford University) for pESCGFP-VHL plasmid,
Dr. Guri Giaever (University of British Columbia) for theRVB1/rvb1D andRVB2/
rvb2D diploid strains, and Dr. Grant Brown (University of Toronto) for the
NUP49-mCHERRY strain. K.R. was supported by a Canadian Institutes of
Health Research (CIHR) Training Program in Protein Folding and Interaction
Dynamics: Principles and Diseases fellowship and by a University of Toronto
Fellowship from the Department of Biochemistry. J.H. was supported by a
Natural Sciences and Engineering Research Council of Canada PGSD2 fellow-
ship. T.V.S. was supported by a CNPq-Brazil fellowship (202192/2015-6) and a
Saskatchewan Health Research Foundation postdoctoral fellowship. M.B.
holds a CIHR New Investigator award. C.B. and C.L.M. are fellows of the Ca-
nadian Institute for Advanced Research (CIFAR). This work was partially sup-
ported by the National Institutes of Health (R01HG005853 to CB and CLM and
R01HG005084 to CLM). This work was also supported by CIHR (RSN-124512,
MOP-125952, RSN-132191, and FDN-154318 toM.B., MOP-93778 toW.A.H.,
and MOP-81256 to I.S. and W.A.H.).
Received: March 7, 2017
Revised: July 21, 2017
Accepted: August 23, 2017
Published: September 12, 2017
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