Title: a Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic

bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

1 Title Page:

3 Title: A yeast phenomic model for the influence of Warburg metabolism on genetic

4 buffering of doxorubicin

6 Authors: Sean M. Santos1 and John L. Hartman IV1

7 1. University of Alabama at Birmingham, Department of Genetics, Birmingham, AL

8 Email: [email protected], [email protected]

9 Corresponding author: [email protected]

25 1

26 Abstract:

27 Background:

28 Saccharomyces cerevisiae represses respiration in the presence of adequate glucose,

29 mimicking the Warburg effect, termed aerobic glycolysis. We conducted yeast phenomic

30 experiments to characterize differential doxorubicin-gene interaction, in the context of

31 respiration vs. glycolysis. The resulting systems level biology about doxorubicin

32 cytotoxicity, including the influence of the Warburg effect, was integrated with cancer

33 pharmacogenomics data to identify potentially causal correlations between differential

34 gene expression and anti-cancer efficacy.

35 Methods:

36 Quantitative high-throughput cell array phenotyping (Q-HTCP) was used to measure cell

37 proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library, treated

38 with escalating doxorubicin concentrations in fermentable and non-fermentable media.

39 Doxorubicin-gene interaction was quantified by departure of the observed and expected

40 phenotypes for the doxorubicin-treated mutant strain, with respect to phenotypes for the

41 untreated mutant strain and both the treated and untreated reference strain. Recursive

42 expectation-maximization clustering (REMc) and Gene Ontology-based analyses of

43 interactions were used to identify functional biological modules that buffer doxorubicin

44 cytotoxicity, and to characterize their Warburg-dependence. Yeast phenomic data was

45 applied to cancer cell line pharmacogenomics data to predict differential gene

46 expression that causally influences the anti-tumor efficacy, and potentially the

47 anthracycline-associated host toxicity, of doxorubicin.

48 Results:

49 Doxorubicin cytotoxicity was greater with respiration, suggesting the Warburg effect can

50 influence therapeutic efficacy. Accordingly, doxorubicin drug-gene interaction was more 2

51 extensive with respiration, including increased buffering by cellular processes related to

52 chromatin organization, protein folding and modification, translation reinitiation, spermine

53 metabolism, and fatty acid beta-oxidation. Pathway enrichment was less notable for

54 glycolysis-specific buffering. Cellular processes exerting influence relatively

55 independently, with respect to Warburg status, included homologous recombination,

56 sphingolipid homeostasis, telomere tethering at nuclear periphery, and actin cortical

57 patch localization. Causality for differential gene expression associated with doxorubicin

58 cytotoxicity in tumor cells was predicted within the biological context of the phenomic

59 model.

60 Conclusions:

61 Warburg status influences the genetic requirements to buffer doxorubicin toxicity. Yeast

62 phenomics provides an experimental platform to model the complexity of gene

63 interaction networks that influence human disease phenotypes, as in this example of

64 chemotherapy response. High-resolution, systems level yeast phenotyping is useful to

65 predict the biological influence of functional variation on disease, offering the potential to

66 fundamentally advance precision medicine.

68 Keywords:

69 genetic buffering, yeast phenomics, quantitative high throughput cell array

70 phenotyping (Q-HTCP), cell proliferation parameters (CPPs), doxorubicin,

71 Warburg metabolism, differential gene interaction networks, recursive

72 expectation-maximization clustering (REMc), pharmacogenomics, human-like / HL

73 yeast media

74 Background:

75 Doxorubicin is used widely in oncology to treat both hematologic cancer and solid

76 tumors [1]. Proposed mechanisms of doxorubicin cytotoxicity include topoisomerase II

77 poisoning, DNA adduct formation, oxidative stress, and ceramide overproduction [1-6].

78 Topoisomerase II is an ATP-dependent enzyme that relieves the DNA torsional stress

79 occurring with replication or transcription by catalyzing a double-stranded DNA (dsDNA)

80 break, relaxing positive and negative DNA supercoiling, and finally re-ligating the DNA

81 [7]. Inhibiting this activity can result in irreparable DNA damage and induction of

82 apoptosis, selectively killing rapidly dividing proliferating cells [8-10]. Doxorubicin also

83 causes histone eviction leading to chromatin trapping and damage [2, 11-13]. In addition

84 to its potent anti-cancer therapeutic properties, doxorubicin is known for dose-limiting

85 cardiomyocyte toxicity, causing cardiomyopathy and heart failure years post-treatment

86 [14]. In this regard, topoisomerase IIB is highly expressed specifically in myocardiocytes,

87 where tissue-specific deletion suppresses cardiac toxicity in mice [15]. Clinical guidelines

88 recommend a maximum cumulative lifetime dose of 500 mg/m2; however, doxorubicin

89 toxicity is variable and has a genetic basis [16]. Thus, a detailed understanding of drug-

90 gene interaction could advance the rationale for more precisely prescribing doxorubicin

91 (among other cytotoxic agents) and also predicting toxicity, based on the unique genetic

92 context of each patient’s tumor genetic profile as well as germline functional variation.

93 This work establishes a yeast phenomic model to understand genetic pathways

94 that buffer doxorubicin toxicity [17-23], and how the Warburg effect influences the

95 doxorubicin-gene interaction network. Warburg won the Nobel Prize in 1931, yet there

96 remains lack of consensus about how cancer cells undergo the Warburg transition and

97 how aerobic glycolysis contributes to cancer [24-27]. In humans, aerobic glycolysis is

98 considered a tumor-specific metabolic transition; however, yeast normally repress 4

99 respiration in the presence of adequate glucose [28-30]. Thus, we wondered whether

100 doxorubicin-gene interaction manifests differentially under glycolytic vs. respiratory

101 conditions in yeast and if genetic insights from this model could lead to better

102 understanding its variable anti-tumor efficacy between different patients [17]. We

103 observed increased toxicity of doxorubicin in non-fermentable media, where yeast must

104 respire to proliferate, suggesting the Warburg transition could confer resistance of tumor

105 cells to doxorubicin, and perhaps help explain the dose-limiting toxicity observed in

106 cardiomyocytes, which have respiratory rates among the highest of all cell types [31].

107 We conducted yeast phenomic analysis of doxorubicin-gene interaction,

108 consisting of quantitative high throughput cell array phenotyping (Q-HTCP) of the yeast

109 knockout and knockdown (YKO/KD) libraries, using multiple growth inhibitory

110 concentrations of doxorubicin in either dextrose- (HLD) or ethanol/glycerol-based

111 (HLEG) media. Q-HTCP provided cell proliferation parameters (CPPs) with which to

112 quantify doxorubicin-gene interaction and determine its dependence on respiratory vs.

113 glycolytic metabolism [32-34]. The yeast phenomic model was used to predict causality

114 underlying correlations between doxorubicin sensitivity and increased or decreased

115 expression of the homologous human gene in pharmacogenomics data from cancer cell

116 lines. Thus, the work details genetic pathways for buffering doxorubicin toxicity in yeast

117 and applies the information to predict interactions between doxorubicin and functional

118 genetic variation manifest in cancers from different, individual patients.

119

120 Methods:

121 Strains and media

122 The yeast gene knockout strain library (YKO) was obtained from Research

123 Genetics (Huntsville, AL, USA). The knockdown (KD) collection, also known as the 5

124 Decreased Abundance of mRNA Production (DAmP) library, was obtained from Open

125 Biosystems (Huntsville, AL, USA). The genetic background for the YKO library was

126 BY4741 (S288C MATa ura3-∆0 his3-∆1 leu2-∆0 met17-∆0). Additional information and

127 lists of strains can be obtained at https://dharmacon.horizondiscovery.com/cdnas-and-

128 orfs/non-mammalian-cdnas-and-orfs/yeast/#all. Some mutants appear multiple times in

129 the library and they are treated independently in our analysis. HL yeast media, a

130 modified synthetic complete media [20], was used with either 2% dextrose (HLD) or 3%

131 ethanol and 3% glycerol (HLEG) as the carbon source.

132

133 Quantitative high throughput cell array phenotyping (Q-HTCP)

134 Phenomic data was obtained by Q-HTCP, a custom, automated method of

135 collecting growth curve phenotypes for the YKO/KD library arrayed onto agar media [34].

136 A Caliper Sciclone 3000 liquid handling robot was used for cell array printing, integrated

137 with a custom imaging robot (Hartman laboratory) and Cytomat 6001 (Thermo Fisher

138 Scientific, Asheville, NC, USA) incubator. 384-culture array images were obtained

139 approximately every 2 hours and analyzed as previously described [21, 34]. To obtain

140 CPPs, image data were fit to the logistic equation, G(t) = K/(1 + e−r(t−l)), assuming G(0) <

141 K, where G(t) is the image intensity of a spotted culture vs. time, K is the carrying

142 capacity, r is the maximum specific growth rate, and l is the moment of maximal absolute

143 growth rate, occurring when G(t) = K/2 (the time to reach half of carrying capacity) [32].

144 The resulting CPPs were used as phenotypes to measure doxorubicin-gene interaction.

145

146 Quantification of doxorubicin-gene interaction

147 Gene interaction was defined by departure of the corresponding YKO/KD strain

148 from its expected phenotypic response to doxorubicin. The expected phenotype was 6

149 determined by cell proliferation phenotypes of the mutant without doxorubicin, together

150 with those of the reference strain with and without doxorubicin [17-19, 21]. The

151 concentrations of doxorubicin (0, 2.5, 5, 7.5 and 15 ug/mL) were chosen based on

152 phenotypic responses being functionally discriminating in the parental strain. We tested

153 for effects of mating type or ploidy on doxorubicin growth inhibition (Additional File 1,

154 Fig. S1), and noted only small differences between the YKO/KD parental strain

155 genotypes, BY4741 (MATa ura3-∆0 his3-∆1 leu2-∆0 met17-∆0), BY4742 (MATα ura3-∆0

156 his3-∆1 leu2-∆0 lys2∆0), BY4741R (MATa ura3-∆0 his3-∆1 leu2-∆0 lys2∆0), BY4742R

157 (MATα ura3-∆0 his3-∆1 leu2-∆0 met17-∆0), and diploid strains derived from these

158 haploids. In this regard, haploid MET17/lys2-∆0 was associated with a lower carrying

159 capacity in HLD media (Additional File 1, Fig. S1), but genome-wide experiments were

160 not performed in this background.

161 Interaction scores were calculated as previously described [21], with slight

162 modifications, as summarized below. Variables were defined as:

163 Di = concentration (dose) of doxorubicin

164 Ri = observed mean growth parameter for parental Reference strain at Di

165 Yi = observed growth parameter for the YKO/KD mutant strain at Di

166 Ki = Yi – Ri, the difference in growth parameter between the YKO/KD mutant (Yi) and

167 Reference (Ri) at Di

168 K0 = Y0 - R0, the effect of gene KO/KD on the observed phenotype in the absence of

169 doxorubicin; this value is annotated as ‘shift’ and is subtracted from all Ki to obtain Li

170 Li = Ki - K0, the interaction between (specific influence of) the KO/KD mutation on

171 doxorubicin response, at Di

172 For cultures not generating a growth curve, Yi = 0 for K and r, and the L

173 parameter was assigned Yi max, defined as the maximum observed Yi among all 7

174 cultures exhibiting a minimum carrying capacity (K) within 2 standard deviation (SD) of

175 the parental reference strain mean at Di. Yi max was also assigned to outlier values (i.e.,

176 if Yi > Yi max).

177 Interaction was calculated by the following steps:

178 1) Compute the average value of the 768 reference cultures (Ri) at each dose (Di):

179 2) Assign Yi max (defined above) if growth curve is observed at D0, but not at Di, or if

180 observed Yi is greater than Yi max.

181 3) Calculate Ki = Yi - Ri.

182 4) Calculate Li = Ki – K0

183 5) Fit data by linear regression (least squares): Li = A + B*Di

184 6) Compute the interaction value ‘INT’ at the max dose: INT = Li-max = A + B*Dmax

185 7) Calculate the mean and standard deviation of interaction scores for reference strains,

186 mean(REFINT) and SD(REFINT); mean(REFINT) is expected to be approximately zero, but

187 SD(REFINT) is useful for standardizing against variance (Additional Files 2-4).

188 8) Calculate interaction z-scores (Fig. 1D):

189 z-score(YKO/KDINT) = (YKO/KDINT – mean(REFINT ))/SD(REFINT)

190 z-score(YKO/KDINT) > 2 for L or < -2 for K are referred to as gene deletion enhancers of

191 doxorubicin cytotoxicity, and conversely, L interaction score < -2 or K interaction scores

192 >2 are considered gene deletion suppressors (Fig. 1E).

193

194 Recursive expectation-maximization clustering (REMc) and heatmap generation

195 REMc is a probability-based clustering method and was performed as previously

196 described [35]. Clusters obtained by Weka 3.5, an EM-optimized Gaussian mixture-

197 clustering module, were subjected to hierarchical clustering in R (http://www.r-

198 project.org/) to further aid visualization with heatmaps. REMc was performed using L 8

199 and K interaction z-scores (Fig. 1F). The effect of gene deletion on the CPP (in the

200 absence of drug), termed ‘shift’ (K0), was not used for REMc, but was included for

201 visualization in the final hierarchical clustering. Additional File 5 contains REMc results

202 in text files with associated data also displayed as heatmaps. In cases where a culture

203 did not grow in the absence of drug, 0.0001 was assigned as the interaction score, and

204 associated data were colored red (‘NA’) in the shift columns of the heatmaps.

205

206 Gene ontology term finder (GTF)

207 A python script was used to format REMc clusters for analysis with the command

208 line version of the GO Term Finder (GTF) tool downloaded from

209 http://search.cpan.org/dist/GO-TermFinder/ [36]. GTF reports on enrichment of Gene

210 Ontology (GO) terms by comparing the ratio of genes assigned to a term within a cluster

211 to the respective ratio involving all genes tested. Additional File 5 contains GTF

212 analysis of all REMc clusters. GO-enriched terms from REMc were investigated with

213 respect to genes representing the term and literature underlying their annotations [37].

214

215 Gene ontology term averaging (GTA)

216 In addition to using GTF to survey functional enrichment in REMc clusters, we

217 developed GTA as a complementary workflow, using the GO information on SGD at

218 https://downloads.yeastgenome.org/curation/literature/ to perform the following analysis:

219 220 1. Calculate the average and SD for interaction values of all genes in a GO term.

221 2. Filter results to obtain terms having GTA value greater than 2 or less than -2.

222 3. Obtain GTA scores defined as |GTA value| - gtaSD; filter for GTA score > 2.

223 The GTA analysis is contained in Additional File 6 as tables and interactive plots

224 created using the R plotly package https://CRAN.R-project.org/package=plotly. GTA

225 results were analyzed primarily using the L interaction scores, however GTA results with

226 K interaction scores are included in Additional File 6 (File D).

227

228 Validation of doxorubicin-gene interaction

229 We retested 364 YKO/KD strains having human homologs in the P-POD

230 database [38] and L interaction scores greater than 2 or less than -2 in at least one

231 media type. Strains were struck to obtain four single colonies and arranged on replicate

232 384 well plates along with twenty reference strain controls and reanalyzed by Q-HTCP

233 on HLD and HLEG, as in the genome-wide experiment. Results are summarized in Fig

234 2S-T, Additional File 2 (Tables S5-S8), and Additional Files 3-4 (Files C-D).

235

236 Prediction of human homologs that influence tumor response to doxorubicin

237 PharmacoDB holds pharmacogenomics data from cancer cell lines, including

238 transcriptomics and drug sensitivity [39]. The PharmacoGx R/Bioconductor package [40]

239 was used to analyze the GDSC1000 (https://pharmacodb.pmgenomics.ca/datasets/5)

240 and gCSI (https://pharmacodb.pmgenomics.ca/datasets/4) datasets, which contained

241 transcriptomic and doxorubicin sensitivity results. A p-value < 0.05 was used for

242 differential gene expression and doxorubicin sensitivity. For gene expression, the sign of

243 the standardized coefficient denotes increased (+) or decreased (-) expression. The

244 biomaRt R package [41, 42] was used with the Ensembl database [43] to match yeast

245 and human homologs from the phenomic and transcriptomic data, classifying yeast-

246 human homology as one to one, one to many, and many to many.

247 Results:

248 Phenomic characterization of doxorubicin response genes

249 The workflow for analyzing doxorubicin-gene interaction and differential buffering

250 of doxorubicin with respect to the Warburg effect is summarized in Fig. 1. Alternately, in

251 a respiratory or glycolytic (HLEG or HLD media, respectively) context (Fig. 1A), Q-HTCP

252 technology was used for high throughput kinetic imaging of 384-culture cell arrays plated

253 on agar media (Fig. 1B), image analysis (Fig. 1C), and growth curve fitting (Fig. 1D) to

254 obtain the CPPs, L (time to reach half carrying capacity), K (carrying capacity), and r

255 (maximum specific rate) [21, 32, 34], which were used to measure doxorubicin-gene

256 interaction across the entire YKO/KD library. Departure of the observed CPP from the

257 expected doxorubicin response for each YKO/KD strain was derived using distributions

258 from many replicate reference strain control cultures, and summarized across all

259 doxorubicin concentrations by linear regression (Fig. 1E). Interaction scores with

260 absolute value greater than two were considered as gene deletion enhancement (z-

261 score_L ≥ 2 or z-score_K ≤ -2) or deletion suppression (z-score_L ≤ -2 or z-score_K ≥ 2)

262 of doxorubicin cytotoxicity. Gene deletion enhancement (e.g., mms1-∆0) and

263 suppression (e.g., vps54-∆0) reveal functions that buffer or confer doxorubicin

264 cytotoxicity, respectively. Doxorubicin-gene interaction profiles (selected if they

265 contained L interaction scores with absolute value greater than two, in either HLD or

266 HLEG media) were analyzed by REMc and assessed for GO Term enrichment (Fig. 1F).

267 As a complement to clustering gene interaction profiles, functional enrichment was

268 analyzed by GTA (see methods), systematically querying all GO processes, functions,

269 and components (Fig. 1G and methods) with respect to CPPs and Warburg status.

270 Taken together, REMc and GTA reveal genetic modules that buffer doxorubicin, and

271 how they are influenced by Warburg metabolism (Fig. 1H). 11

272 Doxorubicin cytotoxicity was greater in HLEG than HLD media, evidenced by the

273 reference strain being more growth inhibited (Fig. 2A-L, Additional File 1, Fig. S1). The

274 ‘L’ parameter was the most sensitive CPP, while K reported larger phenotypic effects

275 (Fig. 2M-N) (Additional File 1, Fig. S2). We noted positive correlation between

276 doxorubicin-gene interaction in HLEG and HLD, however interaction was media-specific

277 and more abundant in the context of respiration, i.e., with HLEG media (Fig. 2O).

278 We compared our results with two prior studies of doxorubicin cytotoxicity in the

279 yeast knockout collections [44, 45]. One study was conducted in SC media with the

280 haploid (BY4741) YKO library and identified 71 deletion enhancers of cytotoxicity [44]. A

281 second study reported on the homozygous diploid (BY4743) YKO collection in YPD

282 media, identifying 376 enhancers [45]. Overlap between these studies and ours is shown

283 in Fig. 2P-2R and in Additional File 7 (Table S9-10). While many genes overlapped

284 between the studies, differing results were also observed, possibly attributable to strain

285 background, media conditions, as well as methods for scoring interactions [20, 46]. To

286 assess within-study reproducibility, we sub-cloned four colonies from glycerol stocks

287 used in the first experiment and retested doxorubicin-gene interaction, revealing higher

288 correlation and overall reproducibility within-study than between-study (Fig. 2S-T).

289

290 Identification of functional gene interaction modules

291 Gene interaction profiles were analyzed by REMc (Figs. 3,4), as described

292 previously [35]. Briefly, REMc uses an expectation-maximization algorithm to define

293 clusters probabilistically, and is applied recursively to resolve gene interaction profile

294 clusters. REMc terminates when a round of clustering reveals no new clusters. The

295 cluster naming convention is “A-B.C.D-X”, where ‘A’ = the round of clustering, ‘B’ = 0,

296 and ‘C.D-X’ indicates the cluster pedigree. For example, 1-0-0 refers to the first cluster of 12

297 the first round, 2-0.0-3 the fourth cluster derived from 1-0-0 (in round 2 of REMc), 3-

298 0.0.3-1 indicates the second cluster derived from 2-0.0-3 (in round 3), and so on [35].

299 The main effect of the gene KO or KD on cell proliferation, i.e., Ki in the absence

300 of doxorubicin (D0) is also referred to as ‘shift’ (see methods). ‘Shift’ was not subjected to

301 REMc, but was included for hierarchical clustering and visualization by heatmaps after

302 REMc (Fig. 3; Additional File 5, File B). Ki is termed ‘shift’, because this value is

303 subtracted from the data series for each YKO/KD to obtain Li values, which are fit by

304 linear regression for calculating drug-gene interaction (Fig. 1E; see methods).

305 GO TermFinder [36] was used to associate enrichment of biological functions

306 with particular patterns of doxorubicin-gene interaction identified by REMc (Figs. 3-4;

307 Table 1; Additional File 5, File C). In general, the first two rounds of REMc revealed

308 distinctive profiles of gene interaction in respiratory vs. glycolytic media (Fig. 4). Later

309 round clusters exhibited greater GO term enrichment in some cases; however, GO

310 enrichment was other times reduced by further clustering (see Additional File 8).

311 GTA score revealed 129 GO terms, 39 of which were found by REMc/GTF

312 (Table 2 and Additional File 6, Files A-C). GTA identifies functions of smaller GO

313 terms, e.g., protein complexes. GTA with K interaction scores yielded only 35 GO terms

314 (Additional File 6, File D), with only 3 being unique from GTA with L interaction; thus,

315 we focused on L interaction for GTA analysis. Interactive scatter plots (html files in which

316 points contain embedded information) were used to visualize significant GO terms from

317 both REMc and GTA (Additional File 6, File B). GO term-specific heatmaps further

318 aided visualization of relationships between genes and the GO terms (see Figs. 6-11

319 and Additional File 9) by systematically displaying, for all genes attributed to a parent

320 term and its children, uniformity vs. pleiotropy of interaction effects across different

321 conditions. 13

322 In summary, we used REMc, GTA, and GO term-specific scatterplots and

323 heatmaps to discover genetic modules that alternatively buffer (i.e., deletion enhancing)

324 or confer (i.e., deletion suppressing) doxorubicin cytotoxicity, and to determine whether

325 the Warburg-transition exerts influence on their effects (Fig. 5).

326

327 Warburg transition-dependent doxorubicin gene interaction modules

328 Respiration-specific gene deletion enhancement

329 Respiration-specific deletion-enhancing clusters (see Fig. 4; 1-0-7 and 1-0-8)

330 revealed GO Term enrichment for histone modification and chromatin organization,

331 respiratory chain complex III assembly, protein import into mitochondria, protein

332 urmylation, the NatC complex, protein folding in endoplasmic reticulum, and DNA

333 topological change (Figs. 6-8; Additional File 5, File C). Additional modules were

334 identified using GTA (Fig. 8C and Additional File 6, File A).

335

336 Chromatin organization and histone modification

337 REMc/GTF and GTA identified several chromatin-related processes that buffer

338 doxorubicin toxicity in a respiration-specific manner, including DNA replication-

339 independent nucleosome assembly, histone exchange, histone deacetylation, and

340 histone methylation (Figs. 6 and 7).

341 (i) DNA replication-independent nucleosome assembly (HIR complex)

342 REMc/GTF identified the HIR complex (HIR1-3 and HPC2), which functions as a

343 histone chaperone in chromatin assembly and disassembly, in cluster 2-0.7-2 (Fig. 4,

344 Table 1) [47]. Along with Asf1 and Rtt106, the HIR complex is involved in DNA

345 replication-independent (i.e., RNA transcriptional) histone deposition, and regulates

346 transcription of three of the four histone genes [47-49]. Furthermore, genes encoding for 14

347 HTA1/HTB1, HHT1/2, and HHF1/2 were also respiratory-specific deletion enhancers.

348 Asf1 and Rtt106 function in nucleosome assembly in both DNA replication and DNA

349 replication-independent contexts. Asf1, which functions in the Rad53-dependent DNA

350 damage response [50], enhanced doxorubicin toxicity in both respiratory and glycolytic

351 media, like other DNA repair genes (see below). In further contrast, genes associated

352 with replication-dependent nucleosome assembly (RLF2, CAC2, MSI1) by the chromatin

353 assembly factor complex, CAF-1, [51] were HLD-specific suppressors (Fig. 6A-B).

354 Prior studies have reported enhanced doxorubicin cytotoxicity due to nucleosome

355 disassembly and “chromatin trapping” by the FACT complex, referring to binding and

356 resulting damage to disassembled chromatin in the context of doxorubicin exposure [13].

357 POB3-DAmP, the only member of the FACT complex represented in the YKO/KD library,

358 resulted in suppression of doxorubicin cytotoxicity (Fig. 6B), presumably by suppressing

359 its effect of trapping and damaging disassembled chromatin.

360 (ii) Histone exchange (Swr1 complex)

361 The Swr1 complex (enriched in cluster 2-0.7-2) uses ATP hydrolysis to replace

362 the H2A nucleosome with the H2AZ variant [52]. Swr1 complex genes showing

363 respiration-specific buffering of doxorubicin toxicity included RVB1, SWC3, SWC5,

364 ARP6, SWR1, VPS71, and VPS72 (Fig. 6C). Accordingly, the H2AZ variant, Htz1, which

365 is enriched at most gene promoters in euchromatin [53-55], was also an HLEG-specific

366 deletion enhancer. The Swr1 complex is recruited for repair of dsDNA breaks, where the

367 H2AZ variant is incorporated [56]; however, the interaction profile of the Swr1 complex

368 more closely resembles other respiratory specific enhancers involved in transcriptional

369 regulation, whereas dsDNA-break repair by homologous recombination buffered

370 doxorubicin toxicity independent of Warburg context (see cluster 1-0-6 from Fig. 4,

371 Table 1, and descriptions below). The Swr1 complex can also inhibit subtelomeric 15

372 spread of heterochromatin by impeding SIR-dependent silencing [57]. Consistent with

373 knockout of Swr1 promoting silencing and having a deletion enhancing effect, deletion of

374 SIR1, SIR3 or SIR4 (which disrupts chromatin silencing) also exerted respiratory-specific

375 suppression of doxorubicin toxicity (Fig. 6C).

376 (iii) Histone deacetylation (Sin3-type and HDA1 complexes)

377 Deletion of genes functioning in the Rpd3L and Rpd3S histone deacetylase

378 complexes (HDAC) was associated with strong respiratory enhancement of doxorubicin

379 toxicity (cluster 2-0.7-2; Fig. 7A); however, genes constituting the Hda1 complex exerted

380 weaker effects, but in both respiratory and glycolytic media (Fig. 7A, Table 2). The yeast

381 Rpd3 deacetylase histone complexes are homologous to mammalian class I Rpd3-like

382 proteins (Hdac1-3,8), while the yeast Hda1 complex is homologous to mammalian class

383 II Hda1-like proteins (Hdac4-5,7,9) [58]. Hda1 and Rpd3 complexes both deacetylate

384 histones H3 and H4; however, deletion of RPD3 vs. HDA1 revealed different degrees of

385 H4 lysine 5 and K12 hyperacetylation [59], implicating this functional distinction in

386 Warburg-differential doxorubicin response.

387 Histone acetylation was GO-enriched in cluster 2-0.6-1, which displayed a

388 Warburg-independent gene interaction profile (Fig. 4, Table 1). GTA analysis confirmed

389 H3K56 acetylation (SPT10 and RTT109) and histone H3 acetylation (TAF9 and HFI1) as

390 media-independent, but also histone H4 acetylation (EAF3, ESA1, NGG1, and ELP4),

391 which was relatively respiratory-specific in its deletion enhancement (Fig. 7B, Table 2).

392 Rtt109 promotes H3K56 acetylation, which is associated with elongating RNA

393 polymerase II [60], and can be persistent in the setting of DNA damage [61]. Warburg-

394 independent deletion enhancement suggests its role in DNA repair becomes invoked.

395 The SAS acetyltransferase complex was deletion suppressing; SAS2 and SAS5

396 were HLEG-specific, and SAS4 was HLD-specific (Fig. 7B). The Sas2 acetyltransferase 16

397 complex creates a barrier against spread of heterochromatin at telomeres by opposing

398 Sir protein deacetylation via effects on histone H4K16 [62]. The deacetylating SIR

399 proteins (SIR1, SIR3, SIR4) were also HLEG-specific suppressors (Fig. 6C), suggesting

400 dynamic regulation of telomeric histones (not simply acetylation or deacetylation), or

401 perhaps a function of Sas2 acetyltransferase that is independent of SIR protein

402 functions, confers doxorubicin cytotoxicity in respiring cells.

403 (iv) Histone methylation (Set1C/COMPASS complex)

404 Histone methylation differentially influences gene transcription, depending on the

405 histone residues modified and the number of methyl groups added [63]. The Set1C/

406 COMPASS complex, which catalyzes mono-, di-, and trimethylation of H3K4 [64-67],

407 was enriched in cluster 1-0-7 (Fig. 4; Table 1). All genes tested from the Set1C/

408 COMPASS complex (SPP1, SDS1, SWD1, SWD3, BRE2, SHG1; SET1 not in YKO/KD)

409 were EG-specific deletion enhancers (Fig. 7C). The Set1C/COMPASS complex and

410 H3K4 trimethylation localize at transcription start sites of actively transcribed genes [68,

411 69]. Furthermore, the Rad6-Bre1 complex, which mono-ubiquitinates histone H2B before

412 Set1C/COMPASS methylates histone H3K4 [70-72], shared the same interaction profile,

413 cross-implicating the Set1C/COMPASS and Rad6-Bre1 functions (Fig. 7C). The Rad6-

414 Bre1 complex is additionally involved in the DNA damage response checkpoint to

415 activate Rad53 [73], however, its HLEG-specific enhancing profile was more closely

416 shared with transcriptional regulation modules, indicating its latter role is better related.

417 JHD1 and JHD2 are JmjC domain family histone demethylases that act on H3-K36 and

418 H3-K4 respectively, and their deletion suppression interactions are further evidence that

419 histone methylation buffers doxorubicin cytotoxicity, especially in a respiratory context

420 (Fig. 7C).

421 Taken together, the data suggest transcription-associated chromatin regulation

422 buffers doxorubicin-mediated cellular toxicity, which is alleviated by the transition from

423 respiratory to glycolytic metabolism. In contrast, Warburg-independent buffering by

424 histone modifiers appears to be associated with functions related to DNA repair.

425

426 Mitochondrial functions

427 The abundance of deletion-enhancing doxorubicin-gene interactions in HLEG media

428 (Figure 2O) caused us to closely examine genes annotated to mitochondrial function.

429 Many mitochondrial gene deletion strains grew poorly on HLEG media, with petite-like

430 proliferation defects on HLD media, as respiration is required to reach carrying capacity.

431 Completely respiratory-deficient mutants clustered together in 1-0-0, however, many

432 mitochondrial mutants maintained some or all respiratory capacity. For example, the

433 respiratory chain complex III assembly and protein import into mitochondrial matrix terms

434 were enriched in deletion enhancing clusters, 1-0-7 and 1-0-8 (Table 1, Figure 4,

435 Additional File 1, Fig. S3). Some of these strains appeared respiratory sufficient yet the

436 genes were required to buffer doxorubicin cytotoxicity under respiratory conditions. For

437 example, evolutionarily conserved genes functioning in complex IV assembly

438 (RCF1/YML030W and COA6) reached carrying capacity on HLEG media, yet exerted

439 strong deletion enhancement of doxorubicin growth inhibition (Additional File 1, Fig.

440 S3A). In contrast, other HLEG-specific deletion enhancing complex IV assembly

441 components (COA2, CMC1, RCF2) and complex III assembly genes (FMP25, FMP36,

442 QCR9, CBP4) were either not conserved in humans or exhibited strong respiratory

443 defects (in absence of doxorubicin) (Additional File 1, Fig. S3A-B). Interactions specific

444 to assembly of respiratory chain complexes may be informative for studies in

445 cardiomyocytes regarding doxorubicin inhibition, depletion of cytochrome c and 18

446 cardiolipin, reduced workload capacity, and accelerated aging [74, 75]. Functionally

447 conserved (TOM70, TIM10, TIM17, TIM23, and MGR2) and yeast-specific (TOM6 and

448 TOM7) genes in protein import into mitochondrial matrix buffered doxorubicin cytotoxicity

449 (Additional File 1, Fig. S3C-E), possibly due to increased oxidative stress [76], which

450 enhances doxorubicin toxicity [1, 4].

451 Systematic examination of the GO annotation mitochondrion (Additional File 1,

452 Fig. S4) revealed several additional respiratory-competent gene-deletion strains

453 exhibiting HLEG-specific enhancing interactions. COX13 encodes subunit VIa of

454 cytochrome c oxidase, which functions with Rcf1 in the formation of respirasomes (also

455 called ‘supercomplexes’) [77, 78]. Others included COX8, encoding subunit VIII of

456 cytochrome c oxidase [79]; MPC1, encoding a mitochondrial pyruvate carrier [80, 81];

457 MME1, encoding an inner mitochondrial membrane magnesium exporter [82]; OMS1, an

458 inner membrane protein predicted to have methyltransferase activity [83]; GUF1, a

459 matrix-localized GTPase that binds mitochondrial ribosomes and influences cytochrome

460 oxidase assembly [84]; and MIC10 (YCL057C-A), encoding a component of the MICOS

461 complex, functioning in inner membrane organization and membrane contact site

462 formation [85].

463

464 Protein folding, localization, and modification pathways

465 Protein biogenesis and modification pathways enriched in HLEG-specific

466 enhancement clusters included the endoplasmic reticulum membrane complex (EMC)

467 (2-0.7-1), protein urmylation (2-0.2-1), and N-terminal acetylation by the NatC complex

468 (2-0.8-1) (Figure 4, Table 1).

469

470 19

471 (i) Protein folding in endoplasmic reticulum (ER membrane protein complex)

472 The ER membrane complex (EMC1-6, Fig. 8A) functions in protein folding in the

473 ER [86] and together with the ER-mitochondria encounter structure (ERMES), the EMC

474 enables ER-mitochondria phosphatidylserine transfer and tethering [87]. The EMC

475 physically interacts with the mitochondrial translocase of the outer membrane (e.g.,

476 TOM5, 6, 7, 22, 70; described above) for the process of ER-mitochondria

477 phosphatidylserine transfer [87]. The shared respiratory-specific, deletion-enhancing

478 profiles suggest cooperative functions of the EMC and mitochondrial outer membrane

479 translocase (Additional File 1, Fig. S3D) in buffering doxorubicin cytotoxicity. In

480 contrast to the EMC, genes involved in the ERMES complex (1-0-0; Additional File 5,

481 File B-C) were essential for respiration, and thus their influence on doxorubicin

482 cytotoxicity could not be addressed with knockout mutants in HLEG media.

483 (ii) Protein urmylation, Elongator complex, and tRNA wobble uridine thiolation

484 ELP2, UBA4, URM1, and URE2 clustered together in 2-0.2-1, constituting GO-

485 enrichment in protein urmylation, the covalent modification of lysine residues with the

486 ubiquitin-related modifier, Urm1 [88]. Other protein urmylation genes, ELP6, NCS2, and

487 NCS6/YGL211W, displayed similar interaction profiles and clustered together in 1-0-7

488 (Figure 8A). ELP2 and ELP6 also function in the Elongator holoenzyme complex (IKI1,

489 IKI3, ELP2, ELP3, ELP4, and ELP6) [89-91], associated with similar interaction profiles

490 (Additional File 1, Fig. S5). URM1, UBA4, NCS2, and NCS6 further function in tRNA

491 wobble position uridine thiolation, where Urm1 functions as a sulfur carrier. Genes

492 uniquely annotated to these terms (IKI1, IKI3, ELP3, ELP4, TUM1, URE2) also displayed

493 related profiles (Additional File 1, Fig. S5). Thus, protein urmylation, Elongator complex

494 function, and tRNA wobble thiolation appear to be distinct modules, comprised of shared

495 genes, each buffering doxorubicin toxicity in a respiratory-specific way. 20

496 (iii) N-terminal acetylation by the NatC complex

497 The NatC complex (Mak3, Mak10, and Mak31) specifically acetylates

498 methionine-starting hydrophobic N-terminal proteins (Met-Leu, Met-Phe, Met-Ile, Met-

499 Tyr) [92], neutralizing positive charge on the alpha-amino group and impeding turnover

500 by ubiquitination or other modifications [93]. N-acetylation occurs on around half of the

501 soluble yeast proteome and over 80% in humans [94]. NatC-mediated N-terminal

502 acetylation facilitates Golgi or inner nuclear membrane localization of some [95-98], but

503 not most proteins [99]. The three genes encoding the NatC complex clustered together

504 (Fig. 8A), however, NatC substrates were not enriched among doxorubicin-gene

505 interactions (Additional File 7, Table S11). Perhaps a select few NatC targets or a

506 novel function for NatC underlie its compensatory effects.

507

508 DNA topological change

509 DNA topological change, which refers to remodeling the turns of a double

510 stranded DNA helix, was enriched in cluster 2-0.8-0 (Figure 4, Table 1). Representative

511 genes were SGS1, TOP1, RFA1, RMI1, TOP3, MMS4, and MUS81 (Fig. 8B). Types I

512 and II topoisomerases resolve supercoiling during replication and transcription [100,

513 101]. Top1 is a type IB topoisomerase, which relaxes positive and negative supercoils

514 [102, 103], compared to Top3, a type IA topoisomerase that specifically acts on negative

515 supercoiling [104]. The Mms4-Mus81 endonuclease has overlapping functions with Top3

516 and Sgs1 in DNA repair [105]; however, their respective influences on doxorubicin

517 toxicity were quantitatively distinct in both respiratory and glycolytic contexts, with a

518 greater requirement for the MMS4/MUS81 than SGS1, TOP3, RFA1, and RMI1; the

519 latter four, functioning together for decatenation and unknotting of dsDNA [106].

520 21

521 GTA reveals additional biological functions that buffer doxorubicin toxicity

522 GTA scores revealed 71 respiratory-specific deletion enhancing GO terms, 24 of

523 which were also found by REMc/GTF (see Additional File 6, File A). Strong enhancing

524 terms (GTA value > 10) with functions relatively distinct from those identified above by

525 REMc were tRNA (m1A) methyltransferase complex, MUB1-RAD6-UBR2 ubiquitin

526 ligase complex, malonyl-CoA biosynthetic process, pyridoxal 5'-phosphate salvage,

527 maintenance of transcriptional fidelity during DNA-templated transcription elongation

528 from RNA polymerase II promoter, RNA polymerase II transcription corepressor activity,

529 pyruvate dehydrogenase activity, and eukaryotic translation initiation factor 2 complex

530 (Fig. 8C). Most terms identified by GTA consisted of 2-3 genes, and did not necessarily

531 cluster together by REMc.

532

533 Respiration-specific gene deletion suppression of doxorubicin cytotoxicity

534 REMc clusters exhibiting respiration-dependent gene deletion suppression revealed GO

535 Term enrichment for regulation of fatty acid beta-oxidation, (cluster 2-0.3-1) and

536 translation reinitiation (cluster 2-0.3-5) (Fig. 4, Table 1). By GTA analysis, the

537 EKC/KEOPS complex and spermine biosynthetic process were additionally found to

538 confer HLEG-specific deletion suppression (Fig. 8D, Table 2).

539

540 Regulation of fatty acid beta-oxidation

541 ADR1, OAF1, and PIP2 were grouped together in cluster 2-0.3-1 (Fig. 4, Table 1),

542 displaying HLEG-specific gene deletion suppression (Fig. 8D). The Pip2-Oaf1 complex

543 binds to oleate response elements, and along with ADR1, regulates transcription of

544 peroxisomal genes [107, 108]. Doxorubicin inhibits beta-oxidation of long chain fatty

545 acids in cardiac tissues, which is reversed by supplementing with propionyl-L-carnitine, 22

546 and alleviates effects of doxorubicin cardiotoxicity [109]. Thus, the yeast model may be

547 informative for investigating related gene networks in greater depth.

548

549 Translation reinitiation

550 In the respiratory-specific deletion suppressing cluster 2-0.3-5 (Fig. 4), TMA20, TMA22,

551 and TIF34 represented enrichment for translation reinitiation, which is necessary after

552 termination of short upstream open reading frames (uORFs) [110] (Fig. 8D). Some

553 uORFs function in translational regulation of a downstream protein; for example GCN4

554 expression is regulated in response to amino acid starvation [110]. However, using the

555 Welsh two sample t-test we found no significant difference in means of interaction

556 scores between the distribution of proteins regulated or not by uORFs [111] (p-value =

557 0.8357) (Additional File 7, Table S12).

558

559 Spermine biosynthetic process

560 Loss of spermine biosynthesis, specifically SPE2 (S-adenosylmethionine decarboxylase)

561 and SPE4 (spermine synthase), suppressed doxorubicin toxicity in HLEG media (Fig.

562 8D). The pathways of polyamine metabolism and their physiologic effects on cancer are

563 complex [112, 113], and although our data suggest spermine metabolism contributes to

564 doxorubicin cytotoxicity, how this occurs mechanistically and specifically in respiring

565 cells awaits further study [114].

566

567 EKC/KEOPS complex

568 GTA revealed the EKC/KEOPS complex (CGI121, GON7, and BUD32) as HLEG-

569 specific deletion suppressing (Fig. 8D). The EKC/KEOPS complex is involved in threonyl

570 carbamoyl adenosine (t6A) tRNA modification [115], which strengthens the A-U codon– 23

571 anticodon interaction [116]. EKC/KEOPS has also been characterized with respect to

572 telomere maintenance [117] and transcription [118]. Deletion of GON7, BUD32, or to a

573 lesser extent, CGI121, inhibited cell proliferation in the absence of doxorubicin

574 treatment, indicating that translational and/or transcriptional activity of the EKC/KEOPS

575 complex function contributes to doxorubicin sensitivity.

576

577 Glycolysis-specific gene deletion enhancement of doxorubicin cytotoxicity:

578 HLD-specific deletion enhancement of doxorubicin cytotoxicity could represent lethal

579 vulnerabilities that emerge when a tumor undergoes the Warburg transition. In this

580 regard, several genes, but few enriched GO terms were identified by REMc (Fig. 4,

581 clusters 1-0-5, 2-0.3-0, and 2-0.2-2; Additional File 5, File A). Ribonucleoprotein

582 complex subunit organization was suggested (Table 1), however, the term-specific

583 heatmap revealed doxorubicin-gene interaction within this cellular process to be

584 pleiotropic (Additional File 1, Fig. S6).

585

586 Glycolysis-specific deletion enhancing terms identified by GTA

587 GTA analysis revealed HLD-specific deletion-enhancing genes encoding the Cul4-RING

588 E3 ubiquitin ligase, the Dom34-Hbs1 complex, and the Ubp3-Bre5 deubiquitinase. GDP-

589 Mannose Transport and dTTP biosynthesis were also revealed (Fig. 9A; Supplemental

590 File 6, File A). SOF1, HRT1, and PRP46 were computationally inferred to form the Cul4-

591 RING E3 ubiquitin ligase complex [119]. Yeast Sof1 is an essential protein that is

592 required for 40s ribosomal biogenesis, and overexpression of its human ortholog,

593 DCAF13/WDSOF1, is associated with aggressive tumors and poorer survival in

594 hepatocellular carcinoma [120]. DOM34/PELO and HBS1/HBS1L facilitate recycling of

595 stalled ribosomes by promoting dissociation of large and small subunits through a 24

596 process called no-go decay [121-123]. Knockdown by siRNA of either WDSOF1 or

597 HBS1L was synthetic lethal in a KRAS-driven tumor model [124]. The Ubp3-Bre5

598 deubiquitination complex regulates anterograde and retrograde transport between the

599 ER and Golgi [125, 126]. Vrg4 and Hvg1 transport GDP-mannose into the Golgi lumen

600 for protein glycosylation [127, 128]. Reduced dTTP pools, evidenced by CDC8/DTYMK

601 and CDC21/TYMS, can increase doxorubicin cytotoxicity in cancer cell lines [129]. The

602 human homologs of UBP3, CDC8, and CDC21 were identified in genome-wide siRNA

603 synthetic interaction studies in cancer cell line models [130-132].

604 For several examples above, like SOF1/DCAF13, genes could be targeted as

605 both a driver of the tumor and as a sensitizer to doxorubicin. To systematically identify all

606 candidate vulnerabilities specific to glycolytic tumor cells (not constrained by GO

607 enrichment), we filtered the overall data set, limiting the list to genes with human

608 homologs and to YKO/KD strains that were growth sufficient (low shift on HLD)

609 (Additional File 1, Fig. S7). The human homologs, along with functional descriptions,

610 are provided in Additional File 10, Table S13.

611

612 Glycolysis-specific gene deletion suppression of doxorubicin cytotoxicity

613 HLD-specific deletion suppression clusters (Fig. 4, clusters 2-0.1-0, 2-0.4-0, 2-0.4-2, and

614 3-0.3.3-1) had GO Term enrichment for terms related to mRNA processing and meiotic

615 chromosome condensation. GTA also identified histone deubiquitination (Table 2).

616 Deletion suppression points to genes that could potentially increase doxorubicin toxicity

617 if overexpressed.

618

619

620 25

621 RNA processing

622 HLD-specific deletion suppression clusters (2-0.4-0, 2-0.4-2; Fig. 4) were enriched for

623 mRNA processing-related terms including mRNA 3’ end processing, mRNA cleavage,

624 and 7-methylguanosine cap hypermethylation (Table 1), but the term-specific heatmaps

625 revealed pleiotropic gene interaction profiles (Additional File 1, Fig. S8).

626 SWM2/YNR004W and TGS1 function in 7-methylguanosine (m7G) cap trimethylation

627 (cluster 2-0.4-0), however, the tgs1-∆0 allele also exerted deletion suppression in a

628 respiratory context (Fig. 9B). m7G cap trimethylation protects small nuclear RNAs

629 (snRNAs), and small nucleolar RNAs (snoRNAs) from degradation by exonucleases

630 [133, 134], and promotes efficient pre-rRNA processing and ribosome biogenesis [135].

631

632 Meiotic chromosome condensation

633 SMC2, SMC4, YCG1, and YCS4 constitute the nuclear condensin complex, which

634 functions in chromosome condensation and segregation. The condensin complex

635 associates with chromosomal sites bound by TFIIIC and the RNA Pol III transcription

636 machinery [136], where it facilitates clustering of tRNA genes at the nucleolus [137] (Fig.

637 9B). The condensin complex has been suggested as a potential therapeutic target for

638 cancer [138], and human homologs YCG1/NCAPG2, YCS4/NCAPD2, and SMC4/SMC4

639 are synthetic lethal with the Ras oncogene [124].

640

641 Histone deubiquitination

642 Histone deubiquitination was identified by GTA and includes SUS1, SGF11, SGF73,

643 UBP8, and SEM1 (Fig. 9B); all except SEM1 are part of the DUBm complex, which

644 mediates histone H2B deubiquitination and mRNA export [139]. Loss of histone H2B

645 ubiquitination resulting in HLEG-specific enhancement (Fig. 7C) is consistent with loss 26

646 of the DUBm deubiquitinase being suppressing. Together, they implicate regulation by

647 histone ubiquitination as a mechanism of doxorubicin response. The human homologs of

648 UBP8, USP22 and USP51, were identified in an RNAi screen for resistance to ionizing

649 radiation [140].

650

651 Warburg transition-independent doxorubicin gene-interaction modules:

652 Since many tumors have both respiratory and glycolytic cell populations, targeting

653 Warburg-independent interactions could be especially efficacious, as described below.

654 Deletion enhancement:

655 Cluster 1-0-6 (Fig. 4) had a strong deletion-enhancing profile in both metabolic contexts

656 with GO Term enrichment for DNA repair (Fig. 10), as well as histone acetylation

657 (discussed above, Fig. 7B). GTA analysis additionally revealed the Lst4-Lst7, the Cul8-

658 RING ubiquitin ligase, and MCM complexes (Fig. 10B).

659

660 DNA repair

661 Warburg-independent, deletion-enhancing pathways included homologous

662 recombination and break-induced replication repair (Fig. 10A), along with the Ino80

663 complex (Fig. 10B), the latter explained by its role of histone acetylation in recruitment of

664 DNA repair machinery to dsDNA break sites [52]. The Ino80 complex influences

665 doxorubicin response in fission yeast [141, 142], further suggesting evolutionary

666 conservation of this interaction, and thus potential relevance to mammalian systems

667 [143]. DNA repair pathways, such as those involving RAD52 and INO80, are

668 evolutionarily conserved, involved in genome instability and tumorigenesis [144], and

669 predictive of therapeutic response in some cancers [145], thus representing potential

670 tumor-specific biomarkers for chemotherapeutic efficacy. 27

671 Complexes identified by GTA

672 Warburg-independent deletion enhancing modules identified by GTA were

673 weaker, in many cases, than the dsDNA break repair pathways found by REMc, some of

674 which had strong K parameter interactions (Fig. 10, Additional File 9). GTA-identified

675 terms included: (1) The Cul8-RING ubiquitin ligase complex, which is encoded by

676 RTT101, RTT107, MMS1, MMS22, and HRT1, and functions in replication-associated

677 DNA repair [146]. Cul8/Rtt101, in fact, contributes to multiple complexes that regulate

678 DNA damage responses, including Rtt101‐Mms1‐Mms22, which is required for Eco1-

679 catalyzed Smc3 acetylation for normal sister chromatid cohesion establishment during S

680 phase [147]; (2) The Lst4-Lst7 complex, which functions in general amino acid

681 permease (GAP1) trafficking [148], threonine uptake, and maintenance of

682 deoxyribonucleotide (dNTP) pools [19], clustered with thr1-∆0 (threonine biosynthesis) in

683 2-0.2-1 (Additional File 5, File B); and (3) the mini-chromosome maintenance (MCM)

684 complex, which licenses and initiates DNA replication [149], was evidenced by the

685 mcm2-DAmP, mcm3-DAmP, and mcm5-DAmP YKD strains (Fig. 10B). Work in pea

686 plants showed that doxorubicin inhibits the MCM6 DNA helicase activity [150]. Prior

687 genome-wide experiments with doxorubicin did not analyze YKD mutants, thus the MCM

688 complex highlights the utility of the DAmP collection in drug-gene interaction studies.

689

690 Media-independent deletion suppression

691 Genes functioning in processes that augment doxorubicin toxicity, when lost

692 (e.g., by deletion), can result in suppression. This was suggested in both respiratory and

693 glycolytic contexts for sphingolipid homeostasis, telomere tethering at nuclear periphery,

694 and actin cortical patch localization (Fig. 4, clusters 2-0.4-1 and 2-0.3-3). Conversely,

695 their overexpression in cancer could potentiate toxicity and thereby therapeutic efficacy. 28

696 Sphingolipid homeostasis and metabolism

697 From cluster 2-0.4-1, VPS51, VPS52, VPS53, and VPS54 (Fig. 11A) form the

698 Golgi-associated retrograde protein (GARP) complex, which is required for endosome-

699 to-Golgi retrograde vesicular transport. GARP deficiency results in accumulation of

700 sphingolipid synthesis intermediates [151]. Also from this cluster came fatty acid

701 elongase activity (FEN1/ELO2 and SUR4/ELO3), which when deficient leads to reduced

702 ceramide production and phytosphingosine accumulation [152, 153].

703 Since the GARP genes and fatty acid elongase activity genes function together in

704 sphingolipid metabolism, we searched all genes annotated to this term and found other

705 media-independent suppressors to include TSC3, LIP1, SUR1, SUR2, IPT1, and SKN1

706 (Fig. 11A). Doxorubicin treatment induces accumulation of ceramide [5, 6], which

707 mediates anti-proliferative responses and apoptosis in yeast and human and appears to

708 mechanistically underlie the influence of this gene group [154] (Additional File 1, Fig.

709 S9). These findings were further supported by the deletion enhancer, SCH9, which

710 negatively regulates ceramide production by inducing ceramidases and negatively

711 regulating ISC1 (Fig. 11A) [155]. Multidrug-resistant HL-60/MX2 human promyelocytic

712 leukemia cells are sensitized to doxorubicin by N,N-Dimethyl phytosphingosine [156].

713 Taken together, the model provides genetic detail regarding how disruption of

714 sphingolipid metabolism increases resistance to doxorubicin, and that this occurs in a

715 Warburg-independent manner, seemingly by reducing apoptosis associated with

716 doxorubicin-induced ceramide overproduction [5, 157, 158].

717

718 Telomere tethering at nuclear periphery

719 Enrichment for telomere tethering at nuclear periphery in cluster 2-0.4-1 was

720 comprised of NUP60, NUP170, MLP1, and ESC1. Paradoxically growth deficient on 29

721 HLD media, NUP84, NUP120, and NUP133 also exerted deletion suppression in HLEG

722 (Fig. 11B). Nuclear pore functions include coordinating nuclear-cytoplasmic transport

723 and localizing proteins and/or chromosomes at the nuclear periphery, which contributes

724 to DNA repair, transcription, and chromatin silencing [159]. Thus, deletion of nuclear

725 pore genes could influence doxorubicin resistance by multiple potential mechanisms

726 involving altering chromatin states, transcriptional regulation, maintenance of telomeric

727 regions, and DNA repair. Doxorubicin-gene interaction profiles for all nuclear pore-

728 related genes are provided in Additional File 1, Fig. S10 A.

729

730 Actin cortical patch localization

731 Cluster 2-0.4-1 was enriched for actin cortical patch localization, including

732 RVS167, LSB3, RVS161, and VRP1 (Fig. 11B). Related terms (Arp2/3 protein complex

733 and actin cortical patch) exhibited similar doxorubicin-gene interaction profiles, including

734 ARC15, ARC18, ARC35, INP52, INP53, ARP2, ARP3, GTS1, RSP5, and FKS1 (see

735 Additional File 1, Fig. S10 B-C). This result corroborates studies in mouse embryonic

736 fibroblasts where deletion of ROCK1 increased doxorubicin resistance by altering the

737 actin cytoskeleton and protecting against apoptosis [160, 161]. Additional literature

738 indicates an importance of actin-related processes for doxorubicin cytotoxicity [162-164],

739 highlighting the utility of yeast phenomics to understand these effects in greater depth.

740

741 Respiratory-deficient doxorubicin-gene interaction modules

742 From cluster 1-0-0, we noted that respiratory deficient YKO/KD strains (those not

743 generating a growth curve on HLEG) also had low K and/or increased L ‘shift’ values on

744 HLD, as would be expected of petite strains [165]. Among strains in this category, those

745 displaying doxorubicin-gene interaction tended to show deletion enhancement (Fig. 4). 30

746 Respiratory deficient deletion enhancers on HLD functioned primarily in

747 mitochondrial processes (Additional File 5, File C; see GO enrichment for cluster 1-0-0

748 and derivative clusters), including mitochondrial translation, mitochondrion-ER tethering,

749 protein localization into mitochondria, mitochondrial genome maintenance, respiratory

750 chain complex assembly, and proton transport. Compromise of mitochondrial respiration

751 leading to sensitization of cells to doxorubicin is of interest given recent findings that

752 some glycolytic cancers are respiratory deficient [166, 167].

753

754 Phenomics-based predictions of doxorubicin-gene interaction in cancer cell lines

755 Differential gene expression, by itself, is a poor predictor of whether protein

756 function affects proliferative response to a particular drug [168]. Thus, yeast phenomic

757 data, which precisely measures enhancing and suppressing interactions with respect to

758 growth phenotypes, could provide a systems model to prioritize candidate effectors of

759 cancer cell line sensitivity and transcriptomic data [169, 170]. To investigate this

760 possibility, yeast doxorubicin-gene interaction was matched by homology to differential

761 gene expression in doxorubicin-sensitive cancer cell lines, using PharmacoGx [40] and

762 biomaRt [41, 42]) in conjunction with the GDSC1000 [171, 172] or gCSI [173, 174]

763 databases (Fig. 12). Differential gene expression was analyzed for individual tissues and

764 also aggregated across all tissues. Yeast gene deletion enhancers were matched to

765 human homologs underexpressed in doxorubicin-sensitive cancer cell lines, termed

766 ‘UES’. Conversely, yeast gene deletion suppressors were matched to human homologs

767 overexpressed in doxorubicin sensitive cells, termed ‘OES’ (Additional File 11).

768 There was greater overlap in differential gene expression between the gCSI and

769 GDSC databases for aggregated data (compared to data for individual tissues), of which

770 agreement was greater for OES than UES. Among individual tissues, there was highest 31

771 agreement between hematopoietic/lymphoid and lung. Differences could be partially

772 explained by the two studies using different platforms for measuring gene expression

773 and cell cytotoxicity (https://pharmacodb.pmgenomics.ca/drugs/273). The gCSI

774 database also reported more UES and OES genes than GDSC (Additional File 11,

775 Files B and C). Differentially expressed genes were mined with respect to p-value and

776 matched to homologous yeast gene interactions (Additional File 11, Files D-I).

777 Warburg status was not available for the cancer cell lines, so we first matched Warburg-

778 independent yeast gene interactions to differentially expressed genes from aggregated

779 data in both the gCSI and GDSC datasets, predicting eight UES (ARP4/ACTL6B,

780 ERG13/HMGCS2, PTC1/PPM1L, SCH9/RPS6KB2, SEC11/SEC11C, SEC7/ARFGEF2,

781 SEC7/IQSEC3, and SIS2/PPCDC) and 18 OES genes (ARP2/ACTR2, CDC3/SEPT6,

782 CKA2/CSNK2A2, DBR1/DBR1, DOA1/PLAA, EFT2/EEF2, HTS1/HARS, KIN28/CDK7,

783 MAP1/METAP1, RPL16B/RPL13A, RPL32/RPL32, RPL34A/RPL34, RPL40B/ZFAND4,

784 RPS6A/RPS6, SSE1/HSPA4, STO1/NCBP1, TRZ1/ELAC2, and UBC4/UBE2D1) to

785 have causal influences on the doxorubicin sensitivity phenotype (Fig. 12C-D).

786 As detailed in Tables 3-4 and described below, we expanded the analysis to

787 genes representative of GO Term enrichments revealed by the yeast phenomic model,

788 restricting to human genes differentially expressed across all cancer tissues, but without

789 restricting by Warburg-independence or gCSI/GDSC co-evidence. Results for individual

790 tissues are also provided in Additional File 11, File A.

791

792 Deletion enhancers with UES homologs

793 Concordance between deletion-enhancing doxorubicin-gene interaction in yeast

794 and UES observed for the corresponding human homologs in cancer cells suggests

795 synergistic targets and biomarkers to increase therapeutic efficacy for doxorubicin, as

796 summarized in Table 3 and Fig. 12C, and briefly discussed below.

797 Doxorubicin-enhancing interactions that were UES in both gCSI and GDSC

798 included: ACTL6B, identified as a candidate tumor suppressor gene in primary

799 hepatocellular carcinoma tissue [175]; PPM1L, which regulates ceramide trafficking at

800 ER-Golgi membrane contact sites [176], and exhibits reduced expression in familial

801 adenomatous polyposis [177]; RPS6KB2, which was UES in breast, ovarian and bone in

802 gCSI, while RPS6KA1, A2, A5 and A6 were UES in select tissues in both databases

803 (Additional File 11, File A); SEC11/SEC11C, which is upregulated in response to

804 hypoxia in non-small cell lung cancer tissue [178], and for which deletion enhancement

805 was stronger in HLD media (Additional File 1, Fig. S7); SEC7/ARFGEF2 (alias BIG2)

806 exhibits increased gene and protein expression in pancreatic cancer [179], and shRNA

807 knockdown of ARFGEF2 can reduce Burkitt’s lymphoma cell survival [180].

808 We expanded the analysis above by matching yeast gene deletion enhancers to

809 human UES genes in either database, i.e., not requiring that genes be significant in both

810 datasets (Figs 12E-F). The result highlighted chromatin-related buffering processes,

811 including nucleosome assembly (HTA1, HTB1, HHF1, HHF2, HHT1, HHF1), histone

812 exchange (SET2/SETBP1 and SWR1/SRCAP), and histone modifiers (BRE1, HDA1,

813 RCO1) (Fig. 12E, Table 3). Other functions predicted by the yeast model to buffer

814 doxorubicin toxicity in cancer cells included DNA topological change (MUS81, SGS1),

815 mitochondrial maintenance (MGR2, TOM70), protein acetylation (MAK3), and

816 metabolism (SFA1, ERG13, SOD1).

817 MUS81 knockdown enhances sensitivity of colon cancer lines to cisplatin and

818 other chemotherapy agents by activating the CHK1 pathway [181]. MGR2/ROMO1 is

819 involved in protein import into the mitochondrial matrix and overexpression of ROMO1 33

820 has been associated with poor prognosis in colorectal [182] and non-small cell lung

821 cancer patients [183]. MAK3/NAA30, a component of the NatC complex (Fig 8A),

822 induces p53-dependent apoptosis when knocked down in cancer cell lines [184]. The

823 HLD-specific deletion enhancer, SFA1, has seven human homologs, of which three

824 (ADH4, ADH1A, and ADH6) were UES in gCSI data (Additional File 1, Figure S7)..

825 High expression of ADH1A or ADH6 was predictive of improved prognosis for pancreatic

826 adenocarcinoma [185] and high expression of ADH1A or ADH4 had improved prognosis

827 for non-small cell lung cancer [186]. The ERG13 homolog, HMGCS1, has been

828 suggested as a synthetic lethal target for BRAFV600E-positive human cancers [187], and

829 HMGCS2 plays a role in invasion and metastasis in colorectal and oral cancer [188].

830 Thus, doxorubicin treatment may have anti-tumor efficacy specifically in glycolytic

831 tumors with reduced expression of SFA1 and ERG13 homologs.

832

833 Deletion suppressors with OES homologs

834 Choosing chemotherapeutic agents for patients based on their tumors exhibiting

835 high expression of genes known to increase sensitivity represents a targeted strategy to

836 increase therapeutic efficacy and could be particularly effective if the sensitizing

837 overexpressed genes happen to also be drivers [189]. Human genes that are OES,

838 homologous to yeast genes that are deletion suppressors, are highlighted in Table 4 and

839 Fig. 12D. ARP2/ACTR2 is a member of the Arp2/3 protein complex (see Additional File

840 1, Figure S10C), and silencing of the Arp2/3 protein complex reduces migration of

841 pancreatic cancer cell lines [190]. EEF2 protein is overexpressed in multiple cancer

842 types, where shRNA knockdown inhibits growth [191]. CDK7 overexpression in breast

843 [192, 193] and gastric [194] cancer is predictive of poor prognosis. RPL34

844 overexpression promotes proliferation, invasion, and metastasis in pancreatic [195], non- 34

845 small cell lung [196], and squamous cell carcinoma [197], while RPL32 was also

846 overexpressed in a prostate cell cancer model [198]. In contrast to Rps6k family

847 members being UES/deletion enhancing, Rps6 was OES/deletion suppressing in

848 ovarian tissue. RPS6 overexpression portends reduced survival for patients with renal

849 carcinoma [199] and hyperphosphosphorylation of Rps6 confers poor prognosis in non-

850 small cell lung cancer [200]. Overexpression of UBE2D1 is associated with decreased

851 survival in lung squamous cell carcinoma tissue [201], and numerous additional

852 ubiquitin-conjugating enzyme family members were OES in analysis of individual tissues

853 (Additional File 11, File A).

854 We expanded the analysis, similar to the way described above for the deletion

855 enhancers, by relaxing the matching criteria in order to identify additional deletion

856 suppressing pathways revealed by the yeast model (Additional File 11). The extended

857 analysis identified yeast-human conserved functions in metabolism (SPE2, SPE4,

858 VPS53, ELO2, ELO4), histone demethylation (JHD1, JHD2), translation reinitiation

859 (TMA22, TIF32), the condensin complex (YCG1, YCS4, SMC2), and telomere tethering

860 at the nuclear periphery (NUP170) (Table 4, Fig. 12F). SPE2/AMD1 is required for

861 spermidine and spermine biosynthesis, and up-regulation of AMD1 by mTORC1 rewires

862 polyamine metabolism in prostate cancer cell lines and mouse models [202]. VPS53, a

863 component of the GARP complex involved in sphingolipid homeostasis, is a tumor

864 suppressor in hepatocellular carcinoma [203-205]. Inhibition of ELOVL6 (homologous to

865 yeast ELO2 and ELO3) in mice reduces tumor growth and increases survival [206]. The

866 histone demethylase, JHD1/KDM2B, is overexpressed in pancreatic cancer [207] and is

867 associated with poor prognosis in glioma [208] and triple negative breast cancer [209]. A

868 second homolog, JHD2/JARID2, is required for tumor initiation in bladder cancer [210].

869 The yeast model also predicts causality underlying OES associated with genes involved 35

870 in translation reinitiation, TMA22/DENR (translation machinery associated) and

871 TIF32/EIF31. DENR-MCT-1 regulates a class of mRNAs encoding oncogenic kinases

872 [211-213], and its overexpression in hepatocellular carcinoma is associated with

873 metastasis [214]. TMA22/DENR also exerts evolutionarily conserved influence on

874 telomeric function and cell proliferation [215]. YCG1/NCAPG and SMC2/SMC2 are

875 components of the condensin complex, which are overexpressed in cancer [138].

876 NUP170/NUP155, which functions in telomere tethering at the nuclear periphery (Fig.

877 11B), is hyper-methylated in association with breast cancer [216, 217], where its

878 reduced expression contributes to a signature for bone metastasis [218].

879

880 Discussion:

881 Many genes are implicated in tumorigenesis and in chemotherapeutic response,

882 with varying degrees of tissue-specific influence and yeast-human homology. The ability

883 to assess mutation, differential gene expression, and other molecular correlates of

884 cancer and chemotherapeutic efficacy is growing, but the direct assessment of drug-

885 gene interaction (i.e., phenotypic/cell proliferative responses) remains a challenge due to

886 the complex genetics and tissue-specific aspects of cancer. In stark contrast, yeast is a

887 single-cell eukaryotic organism that is uniquely amenable to precise and genome-wide

888 measures of drug-gene interaction, for which fundamental contributions to our

889 understanding of human disease are well established [219-223]. Thus, we wondered

890 whether phenomic analysis, using the yeast YKO/KD resource, might be informative

891 about the potential of the Warburg effect to influence the anti-cancer efficacy of

892 doxorubicin, and potentially other chemotherapeutic agents [25, 224]. From this

893 unbiased systems perspective, we observed that a less extensive genetic network is

894 required to buffer doxorubicin in glycolytic vs. respiring cells. The HLEG-specific 36

895 doxorubicin-gene interaction network points to genetic vulnerabilities that respiratory

896 tumors have, but that can be relieved of by the Warburg transition to glycolytic

897 metabolism. Thus, the yeast phenomic model could be applied in the context of Warburg

898 status and analysis of somatic mutations in an individual patient’s cancer, to aid in

899 predicting doxorubicin therapeutic efficacy (Fig. 13, Tables 3-4). Cells can buffer the

900 cytotoxic effects of doxorubicin in a glycolytic context with less reliance on pathways that

901 can go awry in cancer and that influence doxorubicin cytotoxicity more in a respiratory

902 context; examples include pathways of chromatin regulation, protein folding and

903 modification, mitochondrial function, and DNA topological change. The yeast model also

904 predicts that respiring tumors can better survive doxorubicin if functions for fatty acid

905 beta-oxidation, spermine metabolism, and translation reinitiation are compromised by

906 mutation (Fig. 13, Table 3). On the other hand, cells that transition to glycolytic

907 metabolism need dTTP biosynthesis and protein complexes including the Cul4-RING E3

908 ubiquitin ligase, and the Ubp3-Bre5 deubuiquitinase, as well as Dom34-Hbs1, which

909 functions in ‘no-go’ mRNA decay, in order to buffer doxorubicin. However, glycolytic cells

910 become even more resistant if losing histone deubiquitination or the nuclear condensin

911 complex (Fig. 13, Table 3). The yeast model also highlighted Warburg-independent

912 pathways for which loss of function enhanced doxorubicin cytotoxicity, such as DNA

913 repair and histone H3-K56 acetylation, along with deletion suppressing pathways,

914 including sphingolipid homeostasis, actin cortical patch localization, and telomere

915 tethering at the nuclear periphery (Fig. 13, Table 3).

916 Studies in cancer cell lines, mice, and acute myeloid leukemia blast cells from

917 patients were highlighted by the yeast phenomic model, suggesting histone eviction,

918 increased mutation rates at active promoter sites [11, 12, 225], and accumulation of

919 damage from chromatin trapping by the FACT complex as mechanisms of doxorubicin 37

920 toxicity [13]. Further support of the importance of chromatin regulation was suggested by

921 transcriptional control and assembly of histones, as well as histone modifications, which

922 are all particularly important in a respiratory context. From a precision medicine

923 perspective, tumors that are promoted by genetic compromise in chromatin regulation

924 [226, 227] would be potentially more susceptible to treatment, but only if they have not

925 undergone the Warburg transition to glycolysis. Analogously, patients with germline

926 variation resulting in functional compromise of chromatin regulation may have normal

927 tissue (e.g., cardiac muscle) that is susceptible to doxorubicin and thus may suffer

928 greater toxic side effects of cancer treatment.

929 The examples of integrating yeast phenomic data with cancer cell line

930 pharmacogenomics data to predict therapeutic efficacy are not limited to doxorubicin

931 and/or the Warburg phenomenon. Analogous phenomic models could be generated for

932 many cytotoxic agents and/or metabolic states. We found the global correlation of

933 human UES and OES with yeast deletion suppressors and enhancers to be very low,

934 consistent with yeast studies examining this expectation [168]. However, there were

935 many examples of differential expression of individual genes, which were potentially

936 explained biologically by the yeast phenomic model. These observations suggest that

937 yeast phenomic models can be helpful and may even be necessary to associate

938 differential gene expression and sensitivity of cancer cells to chemotherapy. We hope

939 and anticipate that future integrative studies and ultimately clinical trials can further

940 demonstrate whether yeast phenomic studies contribute useful information for

941 personalizing clinical guidance and increasing therapeutic efficacy for patients.

942 The HDAC inhibitor, Abexinostat, enhanced doxorubicin cytotoxicity in cancer

943 cell lines [228, 229], and a phase I clinical trial combining the agents in metastatic

944 sarcomas showed some tumor responses [230]. Enhanced doxorubicin cardiotoxicity 38

945 was observed with co-administration of HDAC inhibitors in mice [231]. The yeast

946 phenomic model suggests it could be informative to monitor the Warburg status of

947 cancer cells in such studies (Fig. 7A). The Sin3-type histone deacetylase complexes

948 (Class I) exhibit respiration-specific deletion enhancement, however the influence of

949 HDA1/HDAC6 (Class II) is Warburg-independent, with HDAC6 being UES (Fig. 12E,

950 Table 3). HDAC6 has a unique structure among histone deacetylases, increasing the

951 ability to target it pharmacologically [232]. A clinical trial using Vorinostat in combination

952 with paclitaxel and doxorubicin-cyclophosphamide to treat advanced breast cancer

953 showed a positive response and reduced expression of HDAC6 in the primary tumor

954 [233]. Ricolinostat is a clinically safe HDAC6-specific inhibitor [234] that could enhance

955 doxorubicin toxicity to cancer driven by epigenetic plasticity [226, 227], if the cancer

956 undergoes the Warburg transition, as HDA1 complex mutants are less protected by

957 glycolysis. Yet in a respiratory context, Sin3-type complexes exhibit stronger interaction

958 (Fig. 7A). While speculative, these examples are intended to illustrate the potential

959 power of yeast phenomic models to generate novel, testable hypotheses through

960 integration of existing knowledge and new, unbiased experimental results.

961 In summary, we envision yeast phenomic drug-gene interaction models as a

962 complement to existing cancer pharmacogenomics, providing an experimental platform

963 to quantitatively derive drug-gene interaction network knowledge that can be integrated

964 with DNA, RNA, protein, epigenetic, metabolite profiling, and/or cell proliferation data

965 collected from tumors. Such predictions, applied to individual patients’ tumors, could be

966 further used in conjunction with evaluation of tumor drug response; for example, before

967 and after treatment to understand how recurrent cancer buffers the drug’s toxicities.

968 Analyses of patient-derived tumor organoids, for example, could include predictive

969 modeling and experimental validation for development of treatment strategies, both 39

970 initially and with recurrence [235-237]. The influence of Warburg status could also be

971 integrated into such personalized models if monitoring its influence on responsiveness of

972 cancer to chemotherapy proves useful for selectively killing tumors [238]. Moreover,

973 yeast phenomic models could be tailored to individual patients to examine more complex

974 interactions: for example, in the background of homologous recombination deficiency

975 [145]. Yeast phenomics provides the experimental capabilities and genetic tractability to

976 model genetic buffering networks relevant to human disease at high precision and

977 resolution, and the biological relevance of yeast genetics to human disease is

978 established; however, the extent to which yeast phenomics is predictive of human

979 disease biology and complexity remains to be determined.

980 A major premise of precision medicine should be to systematically account for

981 the contribution of genetic variance to phenotypes as well as influential interacting

982 factors such as cell energy metabolism, age, drugs, or other environmental factors.

983 However, functional genetic variation in human populations, and particularly for cancer,

984 is essentially too abundant to resolve at a systems level with respect to drug-gene

985 interaction. Thus, yeast phenomics, which can define gene interaction networks and

986 genetic buffering in a highly tractable way [21, 239, 240], offers the potential to help

987 resolve disease complexity [17, 241]. Although, the example of doxorubicin is a small

988 sliver of biology, it exemplifies the potential of yeast phenomic modeling of human

989 disease complexity. Lastly, doxorubicin and other cytotoxic agents are typically used in

990 combination cocktails, and a future direction should be to develop yeast phenomic drug-

991 gene interaction network models for buffering combination therapies.

992

993

994 40

995 Conclusions:

996 A yeast phenomic model for the influence of Warburg metabolism on doxorubicin

997 cytotoxicity revealed that glycolysis reduces the cellular reliance on genetic buffering

998 networks. The model reports gene deletion-enhancing and deletion-suppression

999 pathways, and leverages yeast phenomic results to predict differentially expressed

1000 human genes that are causal in their association with doxorubicin killing from cancer cell

1001 line pharmacogenomics data. As such, this yeast model provides systems level

1002 information about gene networks that buffer doxorubicin, serving as example of how the

1003 YKO/KD enables experimental designs to quantify gene interaction globally at high

1004 resolution. In the case of doxorubicin, gene networks buffer cytotoxicity differentially with

1005 respect to Warburg metabolic status. Understanding cytotoxicity in terms of differential

1006 gene interaction networks has the potential to inform systems medicine by increasing the

1007 precision and rationale for personalizing the choice of cytotoxic agents, improving anti-

1008 tumor efficacy and thereby reducing host toxicity. Yeast phenomics is a scalable

1009 experimental platform that can, in principle, be expanded to other cytotoxic

1010 chemotherapeutic agents, singly or in combination, thus providing versatile, tractable

1011 models to map drug-gene interaction networks and understand their complex influence

1012 on cell proliferation.

1013

1014 List of abbreviations

1015 CPP – Cell proliferation parameter; DAmP – Decreased Abundance of mRNA

1016 Production; DE – Deletion enhancer; dNTP – deoxyribonucleotide triphosphate; DS –

1017 Deletion suppressor; dsDNA – double-stranded DNA; EMC – Endoplasmic reticulum

1018 membrane complex; ER – Endoplasmic reticulum; ERMES – ER-mitochondria

1019 encounter structure; GARP - Golgi-associated retrograde protein; GO – Gene ontology; 41

1020 GTF – Gene ontology term finder; GTA – Gene ontology term averaging; GTA value –

1021 Gene ontology term average value; gtaSD – standard deviation of GTA value; GTA

1022 score – (GTA value - gtaSD); HDAC – Histone deacetylase complex; HLD – Human-like

1023 media with dextrose; HLEG – Human-like media with ethanol and glycerol; INT –

1024 Interaction score; m7G - 7-methylguanosine; MCM – Mini-chromosome maintenance;

1025 OES – Overexpressed in doxorubicin sensitive cells; Q-HTCP – Quantitative high

1026 throughput cell array phenotyping; Ref – Reference; REMc – Recursive expectation

1027 maximization clustering; ROS – Reactive oxygen species; RPA - Replication Protein A;

1028 SD – Standard deviation; SGD – Saccharomyces cerevisiae genome database;

1029 snoRNAs - Small nucleolar RNA; snRNA - Small nuclear RNA; t6A - Threonyl

1030 carbamoyl adenosine; UES – Underexpressed in doxorubicin sensitive cells; uORF -

1031 Upstream open reading frames; YKO – Yeast knockout; YKD = Yeast knockdown

1032 Declarations

1033 Ethics approval and consent to participate

1034 -Not Applicable

1035 Consent for Publication

1036 -Not Applicable

1037 Availability of data and materials

1038 All data generated or analyzed during this study are either included in this

1039 published article and supplementary files or will be freely supplied upon request.

1040 Competing Interests

1041 JLH has ownership in Spectrum PhenomX, LLC, a shell company that was formed to

1042 commercialize Q-HTCP technology. The authors declare no other competing interests.

1043

1044 42

1045 Funding

1046 The authors thank the following funding agencies for their support: American Cancer

1047 Society (RSG-10-066-01-TBE), Howard Hughes Medical Institute (P/S ECA 57005927),

1048 NIH/NCI (P30 CA013148), NIH/NIA (R01 AG043076), and Cystic Fibrosis Foundation

1049 (HARTMA16G0).

1050 Author’s contributions

1051 SMS and JLH designed and conducted the experiments and analysis techniques, and

1052 wrote the manuscript.

1053 Acknowledgements

1054 The authors thank Jingyu Guo and Brett McKinney for development of REMc tools, John

1055 Rodgers for help with Q-HTCP analysis, and Mary-Ann Bjornsti and Alex Stepanov for

1056 helpful discussions.

1057 Endnotes

1058 -Not Applicable

1059

1060 Reference List 1061 1062 1. Thorn CF, Oshiro C, Marsh S, Hernandez-Boussard T, McLeod H, Klein TE, Altman RB: 1063 Doxorubicin pathways: pharmacodynamics and adverse effects. Pharmacogenet 1064 Genomics 2011, 21:440-446. 1065 2. Yang F, Teves SS, Kemp CJ, Henikoff S: Doxorubicin, DNA torsion, and chromatin 1066 dynamics. Biochim Biophys Acta 2014, 1845:84-89. 1067 3. Swift LP, Rephaeli A, Nudelman A, Phillips DR, Cutts SM: Doxorubicin-DNA adducts 1068 induce a non-topoisomerase II-mediated form of cell death. Cancer Res 2006, 66:4863- 1069 4871. 1070 4. Angsutararux P, Luanpitpong S, Issaragrisil S: Chemotherapy-Induced Cardiotoxicity: 1071 Overview of the Roles of Oxidative Stress. Oxid Med Cell Longev 2015, 2015:795602. 1072 5. Delpy E, Hatem SN, Andrieu N, de Vaumas C, Henaff M, Rucker-Martin C, Jaffrezou JP, 1073 Laurent G, Levade T, Mercadier JJ: Doxorubicin induces slow ceramide accumulation 1074 and late apoptosis in cultured adult rat ventricular myocytes. Cardiovasc Res 1999, 1075 43:398-407.

1076 6. Kawase M, Watanabe M, Kondo T, Yabu T, Taguchi Y, Umehara H, Uchiyama T, Mizuno 1077 K, Okazaki T: Increase of ceramide in adriamycin-induced HL-60 cell apoptosis: 1078 detection by a novel anti-ceramide antibody. Biochim Biophys Acta 2002, 1584:104- 1079 114. 1080 7. Chen SH, Chan NL, Hsieh TS: New mechanistic and functional insights into DNA 1081 topoisomerases. Annu Rev Biochem 2013, 82:139-170. 1082 8. Tewey KM, Rowe TC, Yang L, Halligan BD, Liu LF: Adriamycin-induced DNA damage 1083 mediated by mammalian DNA topoisomerase II. Science 1984, 226:466-468. 1084 9. Nitiss JL: Targeting DNA topoisomerase II in cancer chemotherapy. Nat Rev Cancer 1085 2009, 9:338-350. 1086 10. Coldwell KE, Cutts SM, Ognibene TJ, Henderson PT, Phillips DR: Detection of 1087 Adriamycin-DNA adducts by accelerator mass spectrometry at clinically relevant 1088 Adriamycin concentrations. Nucleic Acids Res 2008, 36:e100. 1089 11. Pang B, Qiao X, Janssen L, Velds A, Groothuis T, Kerkhoven R, Nieuwland M, Ovaa H, 1090 Rottenberg S, van Tellingen O, et al: Drug-induced histone eviction from open 1091 chromatin contributes to the chemotherapeutic effects of doxorubicin. Nat Commun 1092 2013, 4:1908. 1093 12. Yang F, Kemp CJ, Henikoff S: Doxorubicin enhances nucleosome turnover around 1094 promoters. Curr Biol 2013, 23:782-787. 1095 13. Nesher E, Safina A, Aljahdali I, Portwood S, Wang ES, Koman I, Wang J, Gurova KV: Role 1096 of Chromatin Damage and Chromatin Trapping of FACT in Mediating the Anticancer 1097 Cytotoxicity of DNA-Binding Small-Molecule Drugs. Cancer Res 2018, 78:1431-1443. 1098 14. Singal PK, Iliskovic N: Doxorubicin-induced cardiomyopathy. N Engl J Med 1998, 1099 339:900-905. 1100 15. Zhang S, Liu X, Bawa-Khalfe T, Lu LS, Lyu YL, Liu LF, Yeh ET: Identification of the 1101 molecular basis of doxorubicin-induced cardiotoxicity. Nat Med 2012, 18:1639-1642. 1102 16. Visscher H, Ross CJ, Rassekh SR, Barhdadi A, Dube MP, Al-Saloos H, Sandor GS, Caron 1103 HN, van Dalen EC, Kremer LC, et al: Pharmacogenomic prediction of anthracycline- 1104 induced cardiotoxicity in children. J Clin Oncol 2012, 30:1422-1428. 1105 17. Hartman IV JL, Garvik B, Hartwell L: Principles for the buffering of genetic variation. 1106 Science 2001, 291:1001-1004. 1107 18. Hartman IV JL, Tippery NP: Systematic quantification of gene interactions by 1108 phenotypic array analysis. Genome Biol 2004, 5:R49. 1109 19. Hartman IV JL: Buffering of deoxyribonucleotide pool homeostasis by threonine 1110 metabolism. Proc Natl Acad Sci U S A 2007, 104:11700-11705. 1111 20. Hartman IV JL, Stisher C, Outlaw DA, Guo J, Shah NA, Tian D, Santos SM, Rodgers JW, 1112 White RA: Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction 1113 Networks that Buffer Disease. Genes (Basel) 2015, 6:24-45. 1114 21. Louie RJ, Guo J, Rodgers JW, White R, Shah N, Pagant S, Kim P, Livstone M, Dolinski K, 1115 McKinney BA, et al: A yeast phenomic model for the gene interaction network 1116 modulating CFTR-∆F508 protein biogenesis. Genome Med 2012, 4:103. 1117 22. Veit G, Oliver K, Apaja PM, Perdomo D, Bidaud-Meynard A, Lin ST, Guo J, Icyuz M, 1118 Sorscher EJ, Hartman JI, Lukacs GL: Ribosomal Stalk Protein Silencing Partially Corrects 1119 the DeltaF508-CFTR Functional Expression Defect. PLoS Biol 2016, 14:e1002462.

1120 23. Hartman IV JL: Genetic and Molecular Buffering of Phenotypes. In Nutritional 1121 Genomics: Discovering the Path to Personalized Nutrition. Volume 1. 1 edition. Edited by 1122 Rodriguez R, Kaput J. Hoboken, NJ: John Wiley & Sons; 2006: 496 1123 24. Warburg O: On the origin of cancer cells. Science 1956, 123:309-314. 1124 25. Liberti MV, Locasale JW: The Warburg Effect: How Does it Benefit Cancer Cells? Trends 1125 Biochem Sci 2016, 41:211-218. 1126 26. Schwartz L, Supuran CT, Alfarouk KO: The Warburg Effect and the Hallmarks of Cancer. 1127 Anticancer Agents Med Chem 2017, 17:164-170. 1128 27. Xu XD, Shao SX, Jiang HP, Cao YW, Wang YH, Yang XC, Wang YL, Wang XS, Niu HT: 1129 Warburg effect or reverse Warburg effect? A review of cancer metabolism. Oncol Res 1130 Treat 2015, 38:117-122. 1131 28. Diaz-Ruiz R, Rigoulet M, Devin A: The Warburg and Crabtree effects: On the origin of 1132 cancer cell energy metabolism and of yeast glucose repression. Biochim Biophys Acta 1133 2011, 1807:568-576. 1134 29. Diaz-Ruiz R, Uribe-Carvajal S, Devin A, Rigoulet M: Tumor cell energy metabolism and 1135 its common features with yeast metabolism. Biochim Biophys Acta 2009, 1796:252- 1136 265. 1137 30. Takeda M: Glucose-induced inactivation of mitochondrial enzymes in the yeast 1138 Saccharomyces cerevisiae. Biochem J 1981, 198:281-287. 1139 31. Ventura-Clapier R, Garnier A, Veksler V, Joubert F: Bioenergetics of the failing heart. 1140 Biochim Biophys Acta 2011, 1813:1360-1372. 1141 32. Shah NA, Laws RJ, Wardman B, Zhao LP, Hartman IV JL: Accurate, precise modeling of 1142 cell proliferation kinetics from time-lapse imaging and automated image analysis of 1143 agar yeast culture arrays. BMC Syst Biol 2007, 1:3. 1144 33. Mani R, St Onge RP, Hartman IV JL, Giaever G, Roth FP: Defining genetic interaction. 1145 Proc Natl Acad Sci U S A 2008, 105:3461-3466. 1146 34. Rodgers J, Guo J, Hartman IV JL: Phenomic assessment of genetic buffering by kinetic 1147 analysis of cell arrays. Methods Mol Biol 2014, 1205:187-208. 1148 35. Guo J, Tian D, McKinney BA, Hartman IV JL: Recursive expectation-maximization 1149 clustering: a method for identifying buffering mechanisms composed of phenomic 1150 modules. Chaos 2010, 20:026103. 1151 36. Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G: GO::TermFinder-- 1152 open source software for accessing Gene Ontology information and finding 1153 significantly enriched Gene Ontology terms associated with a list of genes. 1154 Bioinformatics 2004, 20:3710-3715. 1155 37. Cherry JM, Hong EL, Amundsen C, Balakrishnan R, Binkley G, Chan ET, Christie KR, 1156 Costanzo MC, Dwight SS, Engel SR, et al: Saccharomyces Genome Database: the 1157 genomics resource of budding yeast. Nucleic Acids Res 2012, 40:D700-705. 1158 38. Heinicke S, Livstone MS, Lu C, Oughtred R, Kang F, Angiuoli SV, White O, Botstein D, 1159 Dolinski K: The Princeton Protein Orthology Database (P-POD): a comparative 1160 genomics analysis tool for biologists. PLoS One 2007, 2:e766. 1161 39. Smirnov P, Kofia V, Maru A, Freeman M, Ho C, El-Hachem N, Adam GA, Ba-Alawi W, 1162 Safikhani Z, Haibe-Kains B: PharmacoDB: an integrative database for mining in vitro 1163 anticancer drug screening studies. Nucleic Acids Res 2018, 46:D994-D1002.

1164 40. Smirnov P, Safikhani Z, El-Hachem N, Wang D, She A, Olsen C, Freeman M, Selby H, 1165 Gendoo DM, Grossmann P, et al: PharmacoGx: an R package for analysis of large 1166 pharmacogenomic datasets. Bioinformatics 2016, 32:1244-1246. 1167 41. Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W: BioMart and 1168 Bioconductor: a powerful link between biological databases and microarray data 1169 analysis. Bioinformatics 2005, 21:3439-3440. 1170 42. Durinck S, Spellman PT, Birney E, Huber W: Mapping identifiers for the integration of 1171 genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc 2009, 4:1184- 1172 1191. 1173 43. Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, Billis K, Cummins C, 1174 Gall A, Giron CG, et al: Ensembl 2018. Nucleic Acids Res 2018, 46:D754-D761. 1175 44. Xia L, Jaafar L, Cashikar A, Flores-Rozas H: Identification of genes required for 1176 protection from doxorubicin by a genome-wide screen in Saccharomyces cerevisiae. 1177 Cancer Res 2007, 67:11411-11418. 1178 45. Westmoreland TJ, Wickramasekara SM, Guo AY, Selim AL, Winsor TS, Greenleaf AL, 1179 Blackwell KL, Olson JA, Jr., Marks JR, Bennett CB: Comparative genome-wide screening 1180 identifies a conserved doxorubicin repair network that is diploid specific in 1181 Saccharomyces cerevisiae. PLoS One 2009, 4:e5830. 1182 46. Smith DL, Jr., Maharrey CH, Carey CR, White RA, Hartman IV JL: Gene-nutrient 1183 interaction markedly influences yeast chronological lifespan. Exp Gerontol 2016, 1184 86:113-123. 1185 47. Amin AD, Vishnoi N, Prochasson P: A global requirement for the HIR complex in the 1186 assembly of chromatin. Biochim Biophys Acta 2013, 1819:264-276. 1187 48. Green EM, Antczak AJ, Bailey AO, Franco AA, Wu KJ, Yates JR, 3rd, Kaufman PD: 1188 Replication-independent histone deposition by the HIR complex and Asf1. Curr Biol 1189 2005, 15:2044-2049. 1190 49. Imbeault D, Gamar L, Rufiange A, Paquet E, Nourani A: The Rtt106 histone chaperone is 1191 functionally linked to transcription elongation and is involved in the regulation of 1192 spurious transcription from cryptic promoters in yeast. J Biol Chem 2008, 283:27350- 1193 27354. 1194 50. Emili A, Schieltz DM, Yates JR, 3rd, Hartwell LH: Dynamic interaction of DNA damage 1195 checkpoint protein Rad53 with chromatin assembly factor Asf1. Mol Cell 2001, 7:13- 1196 20. 1197 51. Kaufman PD, Kobayashi R, Stillman B: Ultraviolet radiation sensitivity and reduction of 1198 telomeric silencing in Saccharomyces cerevisiae cells lacking chromatin assembly 1199 factor-I. Genes Dev 1997, 11:345-357. 1200 52. Bao Y, Shen X: SnapShot: Chromatin remodeling: INO80 and SWR1. Cell 2011, 144:158- 1201 158 e152. 1202 53. Zhang H, Roberts DN, Cairns BR: Genome-wide dynamics of Htz1, a histone H2A variant 1203 that poises repressed/basal promoters for activation through histone loss. Cell 2005, 1204 123:219-231. 1205 54. Raisner RM, Hartley PD, Meneghini MD, Bao MZ, Liu CL, Schreiber SL, Rando OJ, 1206 Madhani HD: Histone variant H2A.Z marks the 5' ends of both active and inactive 1207 genes in euchromatin. Cell 2005, 123:233-248.

1208 55. Guillemette B, Bataille AR, Gevry N, Adam M, Blanchette M, Robert F, Gaudreau L: 1209 Variant histone H2A.Z is globally localized to the promoters of inactive yeast genes 1210 and regulates nucleosome positioning. PLoS Biol 2005, 3:e384. 1211 56. Seeber A, Hauer M, Gasser SM: Nucleosome remodelers in double-strand break repair. 1212 Curr Opin Genet Dev 2013, 23:174-184. 1213 57. Meneghini MD, Wu M, Madhani HD: Conserved histone variant H2A.Z protects 1214 euchromatin from the ectopic spread of silent heterochromatin. Cell 2003, 112:725- 1215 736. 1216 58. Seto E, Yoshida M: Erasers of histone acetylation: the histone deacetylase enzymes. 1217 Cold Spring Harb Perspect Biol 2014, 6:a018713. 1218 59. Rundlett SE, Carmen AA, Kobayashi R, Bavykin S, Turner BM, Grunstein M: HDA1 and 1219 RPD3 are members of distinct yeast histone deacetylase complexes that regulate 1220 silencing and transcription. Proc Natl Acad Sci U S A 1996, 93:14503-14508. 1221 60. Schneider J, Bajwa P, Johnson FC, Bhaumik SR, Shilatifard A: Rtt109 is required for 1222 proper H3K56 acetylation: a chromatin mark associated with the elongating RNA 1223 polymerase II. J Biol Chem 2006, 281:37270-37274. 1224 61. Masumoto H, Hawke D, Kobayashi R, Verreault A: A role for cell-cycle-regulated histone 1225 H3 lysine 56 acetylation in the DNA damage response. Nature 2005, 436:294-298. 1226 62. Gartenberg MR, Smith JS: The Nuts and Bolts of Transcriptionally Silent Chromatin in 1227 Saccharomyces cerevisiae. Genetics 2016, 203:1563-1599. 1228 63. Greer EL, Shi Y: Histone methylation: a dynamic mark in health, disease and 1229 inheritance. Nat Rev Genet 2012, 13:343-357. 1230 64. Miller T, Krogan NJ, Dover J, Erdjument-Bromage H, Tempst P, Johnston M, Greenblatt 1231 JF, Shilatifard A: COMPASS: a complex of proteins associated with a trithorax-related 1232 SET domain protein. Proc Natl Acad Sci U S A 2001, 98:12902-12907. 1233 65. Krogan NJ, Dover J, Khorrami S, Greenblatt JF, Schneider J, Johnston M, Shilatifard A: 1234 COMPASS, a histone H3 (Lysine 4) methyltransferase required for telomeric silencing 1235 of gene expression. J Biol Chem 2002, 277:10753-10755. 1236 66. Roguev A, Schaft D, Shevchenko A, Pijnappel WW, Wilm M, Aasland R, Stewart AF: The 1237 Saccharomyces cerevisiae Set1 complex includes an Ash2 homologue and methylates 1238 histone 3 lysine 4. EMBO J 2001, 20:7137-7148. 1239 67. Shilatifard A: The COMPASS family of histone H3K4 methylases: mechanisms of 1240 regulation in development and disease pathogenesis. Annu Rev Biochem 2012, 81:65- 1241 95. 1242 68. Krogan NJ, Dover J, Wood A, Schneider J, Heidt J, Boateng MA, Dean K, Ryan OW, 1243 Golshani A, Johnston M, et al: The Paf1 complex is required for histone H3 methylation 1244 by COMPASS and Dot1p: linking transcriptional elongation to histone methylation. 1245 Mol Cell 2003, 11:721-729. 1246 69. Ng HH, Dole S, Struhl K: The Rtf1 component of the Paf1 transcriptional elongation 1247 complex is required for ubiquitination of histone H2B. J Biol Chem 2003, 278:33625- 1248 33628. 1249 70. Dover J, Schneider J, Tawiah-Boateng MA, Wood A, Dean K, Johnston M, Shilatifard A: 1250 Methylation of histone H3 by COMPASS requires ubiquitination of histone H2B by 1251 Rad6. J Biol Chem 2002, 277:28368-28371. 1252 71. Sun ZW, Allis CD: Ubiquitination of histone H2B regulates H3 methylation and gene 1253 silencing in yeast. Nature 2002, 418:104-108. 47

1254 72. Wood A, Schneider J, Dover J, Johnston M, Shilatifard A: The Paf1 complex is essential 1255 for histone monoubiquitination by the Rad6-Bre1 complex, which signals for histone 1256 methylation by COMPASS and Dot1p. J Biol Chem 2003, 278:34739-34742. 1257 73. Giannattasio M, Lazzaro F, Plevani P, Muzi-Falconi M: The DNA damage checkpoint 1258 response requires histone H2B ubiquitination by Rad6-Bre1 and H3 methylation by 1259 Dot1. J Biol Chem 2005, 280:9879-9886. 1260 74. Nicolay K, de Kruijff B: Effects of adriamycin on respiratory chain activities in 1261 mitochondria from rat liver, rat heart and bovine heart. Evidence for a preferential 1262 inhibition of complex III and IV. Biochim Biophys Acta 1987, 892:320-330. 1263 75. Pereira GC, Pereira SP, Tavares LC, Carvalho FS, Magalhaes-Novais S, Barbosa IA, Santos 1264 MS, Bjork J, Moreno AJ, Wallace KB, Oliveira PJ: Cardiac cytochrome c and cardiolipin 1265 depletion during anthracycline-induced chronic depression of mitochondrial function. 1266 Mitochondrion 2016, 30:95-104. 1267 76. MacKenzie JA, Payne RM: Mitochondrial protein import and human health and 1268 disease. Biochim Biophys Acta 2007, 1772:509-523. 1269 77. Taanman JW, Capaldi RA: Subunit VIa of yeast cytochrome c oxidase is not necessary 1270 for assembly of the enzyme complex but modulates the enzyme activity. Isolation and 1271 characterization of the nuclear-coded gene. J Biol Chem 1993, 268:18754-18761. 1272 78. Vukotic M, Oeljeklaus S, Wiese S, Vogtle FN, Meisinger C, Meyer HE, Zieseniss A, 1273 Katschinski DM, Jans DC, Jakobs S, et al: Rcf1 mediates cytochrome oxidase assembly 1274 and respirasome formation, revealing heterogeneity of the enzyme complex. Cell 1275 Metab 2012, 15:336-347. 1276 79. Patterson TE, Poyton RO: COX8, the structural gene for yeast cytochrome c oxidase 1277 subunit VIII. DNA sequence and gene disruption indicate that subunit VIII is required 1278 for maximal levels of cellular respiration and is derived from a precursor which is 1279 extended at both its NH2 and COOH termini. J Biol Chem 1986, 261:17192-17197. 1280 80. Herzig S, Raemy E, Montessuit S, Veuthey JL, Zamboni N, Westermann B, Kunji ER, 1281 Martinou JC: Identification and functional expression of the mitochondrial pyruvate 1282 carrier. Science 2012, 337:93-96. 1283 81. Bricker DK, Taylor EB, Schell JC, Orsak T, Boutron A, Chen YC, Cox JE, Cardon CM, Van 1284 Vranken JG, Dephoure N, et al: A mitochondrial pyruvate carrier required for pyruvate 1285 uptake in yeast, Drosophila, and humans. Science 2012, 337:96-100. 1286 82. Cui Y, Zhao S, Wang J, Wang X, Gao B, Fan Q, Sun F, Zhou B: A novel mitochondrial 1287 carrier protein Mme1 acts as a yeast mitochondrial magnesium exporter. Biochim 1288 Biophys Acta 2015, 1853:724-732. 1289 83. Lemaire C, Guibet-Grandmougin F, Angles D, Dujardin G, Bonnefoy N: A yeast 1290 mitochondrial membrane methyltransferase-like protein can compensate for oxa1 1291 mutations. J Biol Chem 2004, 279:47464-47472. 1292 84. Bauerschmitt H, Funes S, Herrmann JM: The membrane-bound GTPase Guf1 promotes 1293 mitochondrial protein synthesis under suboptimal conditions. J Biol Chem 2008, 1294 283:17139-17146. 1295 85. Pfanner N, van der Laan M, Amati P, Capaldi RA, Caudy AA, Chacinska A, Darshi M, 1296 Deckers M, Hoppins S, Icho T, et al: Uniform nomenclature for the mitochondrial 1297 contact site and cristae organizing system. J Cell Biol 2014, 204:1083-1086. 1298 86. Jonikas MC, Collins SR, Denic V, Oh E, Quan EM, Schmid V, Weibezahn J, Schwappach B, 1299 Walter P, Weissman JS, Schuldiner M: Comprehensive characterization of genes 48

1300 required for protein folding in the endoplasmic reticulum. Science 2009, 323:1693- 1301 1697. 1302 87. Lahiri S, Chao JT, Tavassoli S, Wong AK, Choudhary V, Young BP, Loewen CJ, Prinz WA: A 1303 conserved endoplasmic reticulum membrane protein complex (EMC) facilitates 1304 phospholipid transfer from the ER to mitochondria. PLoS Biol 2014, 12:e1001969. 1305 88. van der Veen AG, Ploegh HL: Ubiquitin-like proteins. Annu Rev Biochem 2012, 81:323- 1306 357. 1307 89. Judes A, Bruch A, Klassen R, Helm M, Schaffrath R: Sulfur transfer and activation by 1308 ubiquitin-like modifier system Uba4*Urm1 link protein urmylation and tRNA 1309 thiolation in yeast. Microb Cell 2016, 3:554-564. 1310 90. Nakai Y, Nakai M, Hayashi H: Thio-modification of yeast cytosolic tRNA requires a 1311 ubiquitin-related system that resembles bacterial sulfur transfer systems. J Biol Chem 1312 2008, 283:27469-27476. 1313 91. Noma A, Sakaguchi Y, Suzuki T: Mechanistic characterization of the sulfur-relay system 1314 for eukaryotic 2-thiouridine biogenesis at tRNA wobble positions. Nucleic Acids Res 1315 2009, 37:1335-1352. 1316 92. Polevoda B, Norbeck J, Takakura H, Blomberg A, Sherman F: Identification and 1317 specificities of N-terminal acetyltransferases from Saccharomyces cerevisiae. EMBO J 1318 1999, 18:6155-6168. 1319 93. Varland S, Osberg C, Arnesen T: N-terminal modifications of cellular proteins: The 1320 enzymes involved, their substrate specificities and biological effects. Proteomics 2015, 1321 15:2385-2401. 1322 94. Arnesen T, Van Damme P, Polevoda B, Helsens K, Evjenth R, Colaert N, Varhaug JE, 1323 Vandekerckhove J, Lillehaug JR, Sherman F, Gevaert K: Proteomics analyses reveal the 1324 evolutionary conservation and divergence of N-terminal acetyltransferases from yeast 1325 and humans. Proc Natl Acad Sci U S A 2009, 106:8157-8162. 1326 95. Setty SR, Strochlic TI, Tong AH, Boone C, Burd CG: Golgi targeting of ARF-like GTPase 1327 Arl3p requires its Nalpha-acetylation and the integral membrane protein Sys1p. Nat 1328 Cell Biol 2004, 6:414-419. 1329 96. Behnia R, Panic B, Whyte JR, Munro S: Targeting of the Arf-like GTPase Arl3p to the 1330 Golgi requires N-terminal acetylation and the membrane protein Sys1p. Nat Cell Biol 1331 2004, 6:405-413. 1332 97. Behnia R, Barr FA, Flanagan JJ, Barlowe C, Munro S: The yeast orthologue of GRASP65 1333 forms a complex with a coiled-coil protein that contributes to ER to Golgi traffic. J Cell 1334 Biol 2007, 176:255-261. 1335 98. Murthi A, Hopper AK: Genome-wide screen for inner nuclear membrane protein 1336 targeting in Saccharomyces cerevisiae: roles for N-acetylation and an integral 1337 membrane protein. Genetics 2005, 170:1553-1560. 1338 99. Aksnes H, Osberg C, Arnesen T: N-terminal acetylation by NatC is not a general 1339 determinant for substrate subcellular localization in Saccharomyces cerevisiae. PLoS 1340 One 2013, 8:e61012. 1341 100. Garinther WI, Schultz MC: Topoisomerase function during replication-independent 1342 chromatin assembly in yeast. Mol Cell Biol 1997, 17:3520-3526. 1343 101. Champoux JJ: DNA topoisomerases: structure, function, and mechanism. Annu Rev 1344 Biochem 2001, 70:369-413.

1345 102. Goto T, Wang JC: Cloning of yeast TOP1, the gene encoding DNA topoisomerase I, and 1346 construction of mutants defective in both DNA topoisomerase I and DNA 1347 topoisomerase II. Proc Natl Acad Sci U S A 1985, 82:7178-7182. 1348 103. Thrash C, Bankier AT, Barrell BG, Sternglanz R: Cloning, characterization, and sequence 1349 of the yeast DNA topoisomerase I gene. Proc Natl Acad Sci U S A 1985, 82:4374-4378. 1350 104. Wang JC: DNA topoisomerases. Annu Rev Biochem 1996, 65:635-692. 1351 105. Kaliraman V, Mullen JR, Fricke WM, Bastin-Shanower SA, Brill SJ: Functional overlap 1352 between Sgs1-Top3 and the Mms4-Mus81 endonuclease. Genes Dev 2001, 15:2730- 1353 2740. 1354 106. Cejka P, Plank JL, Dombrowski CC, Kowalczykowski SC: Decatenation of DNA by the S. 1355 cerevisiae Sgs1-Top3-Rmi1 and RPA complex: a mechanism for disentangling 1356 chromosomes. Mol Cell 2012, 47:886-896. 1357 107. Rottensteiner H, Kal AJ, Hamilton B, Ruis H, Tabak HF: A heterodimer of the Zn2Cys6 1358 transcription factors Pip2p and Oaf1p controls induction of genes encoding 1359 peroxisomal proteins in Saccharomyces cerevisiae. Eur J Biochem 1997, 247:776-783. 1360 108. Karpichev IV, Luo Y, Marians RC, Small GM: A complex containing two transcription 1361 factors regulates peroxisome proliferation and the coordinate induction of beta- 1362 oxidation enzymes in Saccharomyces cerevisiae. Mol Cell Biol 1997, 17:69-80. 1363 109. Sayed-Ahmed MM, Salman TM, Gaballah HE, Abou El-Naga SA, Nicolai R, Calvani M: 1364 Propionyl-L-carnitine as protector against adriamycin-induced cardiomyopathy. 1365 Pharmacol Res 2001, 43:513-520. 1366 110. Gunisova S, Hronova V, Mohammad MP, Hinnebusch AG, Valasek LS: Please do not 1367 recycle! Translation reinitiation in microbes and higher eukaryotes. FEMS Microbiol 1368 Rev 2018, 42:165-192. 1369 111. Cvijovic M, Dalevi D, Bilsland E, Kemp GJ, Sunnerhagen P: Identification of putative 1370 regulatory upstream ORFs in the yeast genome using heuristics and evolutionary 1371 conservation. BMC Bioinformatics 2007, 8:295. 1372 112. Murray-Stewart TR, Woster PM, Casero RA, Jr.: Targeting polyamine metabolism for 1373 cancer therapy and prevention. Biochem J 2016, 473:2937-2953. 1374 113. Casero RA, Jr., Celano P, Ervin SJ, Porter CW, Bergeron RJ, Libby PR: Differential 1375 induction of spermidine/spermine N1-acetyltransferase in human lung cancer cells by 1376 the bis(ethyl)polyamine analogues. Cancer Res 1989, 49:3829-3833. 1377 114. Ulrich S, Huwiler A, Loitsch S, Schmidt H, Stein JM: De novo ceramide biosynthesis is 1378 associated with resveratrol-induced inhibition of ornithine decarboxylase activity. 1379 Biochem Pharmacol 2007, 74:281-289. 1380 115. Srinivasan M, Mehta P, Yu Y, Prugar E, Koonin EV, Karzai AW, Sternglanz R: The highly 1381 conserved KEOPS/EKC complex is essential for a universal tRNA modification, t6A. 1382 EMBO J 2011, 30:873-881. 1383 116. Murphy FVt, Ramakrishnan V, Malkiewicz A, Agris PF: The role of modifications in 1384 codon discrimination by tRNA(Lys)UUU. Nat Struct Mol Biol 2004, 11:1186-1191. 1385 117. Downey M, Houlsworth R, Maringele L, Rollie A, Brehme M, Galicia S, Guillard S, 1386 Partington M, Zubko MK, Krogan NJ, et al: A genome-wide screen identifies the 1387 evolutionarily conserved KEOPS complex as a telomere regulator. Cell 2006, 124:1155- 1388 1168.

1389 118. Kisseleva-Romanova E, Lopreiato R, Baudin-Baillieu A, Rousselle JC, Ilan L, Hofmann K, 1390 Namane A, Mann C, Libri D: Yeast homolog of a cancer-testis antigen defines a new 1391 transcription complex. EMBO J 2006, 25:3576-3585. 1392 119. Gaudet P, Livstone MS, Lewis SE, Thomas PD: Phylogenetic-based propagation of 1393 functional annotations within the Gene Ontology consortium. Brief Bioinform 2011, 1394 12:449-462. 1395 120. Cao J, Hou P, Chen J, Wang P, Wang W, Liu W, Liu C, He X: The overexpression and 1396 prognostic role of DCAF13 in hepatocellular carcinoma. Tumour Biol 2017, 1397 39:1010428317705753. 1398 121. Doma MK, Parker R: Endonucleolytic cleavage of eukaryotic mRNAs with stalls in 1399 translation elongation. Nature 2006, 440:561-564. 1400 122. Tsuboi T, Kuroha K, Kudo K, Makino S, Inoue E, Kashima I, Inada T: Dom34:hbs1 plays a 1401 general role in quality-control systems by dissociation of a stalled ribosome at the 3' 1402 end of aberrant mRNA. Mol Cell 2012, 46:518-529. 1403 123. Shoemaker CJ, Eyler DE, Green R: Dom34:Hbs1 promotes subunit dissociation and 1404 peptidyl-tRNA drop-off to initiate no-go decay. Science 2010, 330:369-372. 1405 124. Luo J, Emanuele MJ, Li D, Creighton CJ, Schlabach MR, Westbrook TF, Wong KK, Elledge 1406 SJ: A genome-wide RNAi screen identifies multiple synthetic lethal interactions with 1407 the Ras oncogene. Cell 2009, 137:835-848. 1408 125. Cohen M, Stutz F, Belgareh N, Haguenauer-Tsapis R, Dargemont C: Ubp3 requires a 1409 cofactor, Bre5, to specifically de-ubiquitinate the COPII protein, Sec23. Nat Cell Biol 1410 2003, 5:661-667. 1411 126. Cohen M, Stutz F, Dargemont C: Deubiquitination, a new player in Golgi to 1412 endoplasmic reticulum retrograde transport. J Biol Chem 2003, 278:51989-51992. 1413 127. Dean N, Zhang YB, Poster JB: The VRG4 gene is required for GDP-mannose transport 1414 into the lumen of the Golgi in the yeast, Saccharomyces cerevisiae. J Biol Chem 1997, 1415 272:31908-31914. 1416 128. Ballou L, Hitzeman RA, Lewis MS, Ballou CE: Vanadate-resistant yeast mutants are 1417 defective in protein glycosylation. Proc Natl Acad Sci U S A 1991, 88:3209-3212. 1418 129. Hu CM, Chang ZF: Synthetic lethality by lentiviral short hairpin RNA silencing of 1419 thymidylate kinase and doxorubicin in colon cancer cells regardless of the p53 status. 1420 Cancer Res 2008, 68:2831-2840. 1421 130. Safran M, Dalah I, Alexander J, Rosen N, Iny Stein T, Shmoish M, Nativ N, Bahir I, Doniger 1422 T, Krug H, et al: GeneCards Version 3: the human gene integrator. Database (Oxford) 1423 2010, 2010:baq020. 1424 131. Vizeacoumar FJ, Arnold R, Vizeacoumar FS, Chandrashekhar M, Buzina A, Young JT, 1425 Kwan JH, Sayad A, Mero P, Lawo S, et al: A negative genetic interaction map in isogenic 1426 cancer cell lines reveals cancer cell vulnerabilities. Mol Syst Biol 2013, 9:696. 1427 132. Krastev DB, Slabicki M, Paszkowski-Rogacz M, Hubner NC, Junqueira M, Shevchenko A, 1428 Mann M, Neugebauer KM, Buchholz F: A systematic RNAi synthetic interaction screen 1429 reveals a link between p53 and snoRNP assembly. Nat Cell Biol 2011, 13:809-818. 1430 133. Mouaikel J, Verheggen C, Bertrand E, Tazi J, Bordonne R: Hypermethylation of the cap 1431 structure of both yeast snRNAs and snoRNAs requires a conserved methyltransferase 1432 that is localized to the nucleolus. Mol Cell 2002, 9:891-901. 1433 134. Boon KL, Kos M: Deletion of Swm2p selectively impairs trimethylation of snRNAs by 1434 trimethylguanosine synthase (Tgs1p). FEBS Lett 2010, 584:3299-3304. 51

1435 135. Colau G, Thiry M, Leduc V, Bordonne R, Lafontaine DL: The small nucle(ol)ar RNA cap 1436 trimethyltransferase is required for ribosome synthesis and intact nucleolar 1437 morphology. Mol Cell Biol 2004, 24:7976-7986. 1438 136. D'Ambrosio C, Schmidt CK, Katou Y, Kelly G, Itoh T, Shirahige K, Uhlmann F: 1439 Identification of cis-acting sites for condensin loading onto budding yeast 1440 chromosomes. Genes Dev 2008, 22:2215-2227. 1441 137. Haeusler RA, Pratt-Hyatt M, Good PD, Gipson TA, Engelke DR: Clustering of yeast tRNA 1442 genes is mediated by specific association of condensin with tRNA gene transcription 1443 complexes. Genes Dev 2008, 22:2204-2214. 1444 138. Wang HZ, Yang SH, Li GY, Cao X: Subunits of human condensins are potential 1445 therapeutic targets for cancers. Cell Div 2018, 13:2. 1446 139. Kohler A, Schneider M, Cabal GG, Nehrbass U, Hurt E: Yeast Ataxin-7 links histone 1447 deubiquitination with gene gating and mRNA export. Nat Cell Biol 2008, 10:707-715. 1448 140. Hurov KE, Cotta-Ramusino C, Elledge SJ: A genetic screen identifies the Triple T complex 1449 required for DNA damage signaling and ATM and ATR stability. Genes Dev 2010, 1450 24:1939-1950. 1451 141. Tay Z, Eng RJ, Sajiki K, Lim KK, Tang MY, Yanagida M, Chen ES: Cellular robustness 1452 conferred by genetic crosstalk underlies resistance against chemotherapeutic drug 1453 doxorubicin in fission yeast. PLoS One 2013, 8:e55041. 1454 142. Nguyen TT, Lim JS, Tang RM, Zhang L, Chen ES: Fitness profiling links topoisomerase II 1455 regulation of centromeric integrity to doxorubicin resistance in fission yeast. Sci Rep 1456 2015, 5:8400. 1457 143. Ding J, Yu C, Sui Y, Wang L, Yang Y, Wang F, Yao H, Xing F, Liu H, Li Y, et al: The 1458 chromatin remodeling protein INO80 contributes to the removal of H2A.Z at the p53- 1459 binding site of the p21 gene in response to doxorubicin. FEBS J 2018. 1460 144. Galanos P, Pappas G, Polyzos A, Kotsinas A, Svolaki I, Giakoumakis NN, Glytsou C, 1461 Pateras IS, Swain U, Souliotis VL, et al: Mutational signatures reveal the role of RAD52 1462 in p53-independent p21-driven genomic instability. Genome Biol 2018, 19:37. 1463 145. Konstantinopoulos PA, Ceccaldi R, Shapiro GI, D'Andrea AD: Homologous 1464 Recombination Deficiency: Exploiting the Fundamental Vulnerability of Ovarian 1465 Cancer. Cancer Discov 2015, 5:1137-1154. 1466 146. Mimura S, Yamaguchi T, Ishii S, Noro E, Katsura T, Obuse C, Kamura T: Cul8/Rtt101 1467 forms a variety of protein complexes that regulate DNA damage response and 1468 transcriptional silencing. J Biol Chem 2010, 285:9858-9867. 1469 147. Zhang J, Shi D, Li X, Ding L, Tang J, Liu C, Shirahige K, Cao Q, Lou H: Rtt101-Mms1- 1470 Mms22 coordinates replication-coupled sister chromatid cohesion and nucleosome 1471 assembly. EMBO Rep 2017, 18:1294-1305. 1472 148. Roberg KJ, Bickel S, Rowley N, Kaiser CA: Control of amino acid permease sorting in the 1473 late secretory pathway of Saccharomyces cerevisiae by SEC13, LST4, LST7 and LST8. 1474 Genetics 1997, 147:1569-1584. 1475 149. Symeonidou IE, Taraviras S, Lygerou Z: Control over DNA replication in time and space. 1476 FEBS Lett 2012, 586:2803-2812. 1477 150. Tran NQ, Pham XH, Tuteja R, Tuteja N: Inhibition of unwinding and ATPase activities of 1478 pea MCM6 DNA helicase by actinomycin and nogalamycin. Plant Signal Behav 2011, 1479 6:327-329.

1480 151. Frohlich F, Petit C, Kory N, Christiano R, Hannibal-Bach HK, Graham M, Liu X, Ejsing CS, 1481 Farese RV, Walther TC: The GARP complex is required for cellular sphingolipid 1482 homeostasis. Elife 2015, 4. 1483 152. Oh CS, Toke DA, Mandala S, Martin CE: ELO2 and ELO3, homologues of the 1484 Saccharomyces cerevisiae ELO1 gene, function in fatty acid elongation and are 1485 required for sphingolipid formation. J Biol Chem 1997, 272:17376-17384. 1486 153. Hwang S, Gustafsson HT, O'Sullivan C, Bisceglia G, Huang X, Klose C, Schevchenko A, 1487 Dickson RC, Cavaliere P, Dephoure N, Torres EM: Serine-Dependent Sphingolipid 1488 Synthesis Is a Metabolic Liability of Aneuploid Cells. Cell Rep 2017, 21:3807-3818. 1489 154. Ponnusamy S, Meyers-Needham M, Senkal CE, Saddoughi SA, Sentelle D, Selvam SP, 1490 Salas A, Ogretmen B: Sphingolipids and cancer: ceramide and sphingosine-1-phosphate 1491 in the regulation of cell death and drug resistance. Future Oncol 2010, 6:1603-1624. 1492 155. Swinnen E, Wilms T, Idkowiak-Baldys J, Smets B, De Snijder P, Accardo S, Ghillebert R, 1493 Thevissen K, Cammue B, De Vos D, et al: The protein kinase Sch9 is a key regulator of 1494 sphingolipid metabolism in Saccharomyces cerevisiae. Mol Biol Cell 2014, 25:196-211. 1495 156. Kim BM, Choi YJ, Lee YH, Joe YA, Hong SH: N,N-Dimethyl phytosphingosine sensitizes 1496 HL-60/MX2, a multidrug-resistant variant of HL-60 cells, to doxorubicin-induced 1497 cytotoxicity through ROS-mediated release of cytochrome c and AIF. Apoptosis 2010, 1498 15:982-993. 1499 157. Liu YY, Yu JY, Yin D, Patwardhan GA, Gupta V, Hirabayashi Y, Holleran WM, Giuliano AE, 1500 Jazwinski SM, Gouaze-Andersson V, et al: A role for ceramide in driving cancer cell 1501 resistance to doxorubicin. FASEB J 2008, 22:2541-2551. 1502 158. Martinez R, Navarro R, Lacort M, Ruiz-Sanz JI, Ruiz-Larrea MB: Doxorubicin induces 1503 ceramide and diacylglycerol accumulation in rat hepatocytes through independent 1504 routes. Toxicol Lett 2009, 190:86-90. 1505 159. Ibarra A, Hetzer MW: Nuclear pore proteins and the control of genome functions. 1506 Genes Dev 2015, 29:337-349. 1507 160. Wei L, Surma M, Gough G, Shi S, Lambert-Cheatham N, Chang J, Shi J: Dissecting the 1508 Mechanisms of Doxorubicin and Oxidative Stress-Induced Cytotoxicity: The 1509 Involvement of Actin Cytoskeleton and ROCK1. PLoS One 2015, 10:e0131763. 1510 161. Shi J, Wu X, Surma M, Vemula S, Zhang L, Yang Y, Kapur R, Wei L: Distinct roles for 1511 ROCK1 and ROCK2 in the regulation of cell detachment. Cell Death Dis 2013, 4:e483. 1512 162. Colombo R, Necco A, Vailati G, Saracco B, Milzani A, Scari G: Doxorubicin affects actin 1513 assembly in vitro. Cell Biol Int Rep 1984, 8:127-135. 1514 163. Colombo R, Necco A, Vailati G, Milzani A: Dose-dependence of doxorubicin effect on 1515 actin assembly in vitro. Exp Mol Pathol 1988, 49:297-304. 1516 164. Colombo R, Dalle Donne I, Milzani A: Metal ions modulate the effect of doxorubicin on 1517 actin assembly. Cancer Biochem Biophys 1990, 11:217-226. 1518 165. Chen XJ, Clark-Walker GD: The petite mutation in yeasts: 50 years on. Int Rev Cytol 1519 2000, 194:197-238. 1520 166. Courtney KD, Bezwada D, Mashimo T, Pichumani K, Vemireddy V, Funk AM, Wimberly J, 1521 McNeil SS, Kapur P, Lotan Y, et al: Isotope Tracing of Human Clear Cell Renal Cell 1522 Carcinomas Demonstrates Suppressed Glucose Oxidation In Vivo. Cell Metab 2018, 1523 28:793-800 e792. 1524 167. Sanderson SM, Locasale JW: Revisiting the Warburg Effect: Some Tumors Hold Their 1525 Breath. Cell Metab 2018, 28:669-670. 53

1526 168. Birrell GW, Brown JA, Wu HI, Giaever G, Chu AM, Davis RW, Brown JM: Transcriptional 1527 response of Saccharomyces cerevisiae to DNA-damaging agents does not identify the 1528 genes that protect against these agents. Proc Natl Acad Sci U S A 2002, 99:8778-8783. 1529 169. O'Neil NJ, Bailey ML, Hieter P: Synthetic lethality and cancer. Nat Rev Genet 2017, 1530 18:613-623. 1531 170. McGary KL, Park TJ, Woods JO, Cha HJ, Wallingford JB, Marcotte EM: Systematic 1532 discovery of nonobvious human disease models through orthologous phenotypes. 1533 Proc Natl Acad Sci U S A 2010, 107:6544-6549. 1534 171. Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, Greninger P, 1535 Thompson IR, Luo X, Soares J, et al: Systematic identification of genomic markers of 1536 drug sensitivity in cancer cells. Nature 2012, 483:570-575. 1537 172. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, 1538 Smith JA, Thompson IR, et al: Genomics of Drug Sensitivity in Cancer (GDSC): a resource 1539 for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 2013, 41:D955- 1540 961. 1541 173. Klijn C, Durinck S, Stawiski EW, Haverty PM, Jiang Z, Liu H, Degenhardt J, Mayba O, Gnad 1542 F, Liu J, et al: A comprehensive transcriptional portrait of human cancer cell lines. Nat 1543 Biotechnol 2015, 33:306-312. 1544 174. Haverty PM, Lin E, Tan J, Yu Y, Lam B, Lianoglou S, Neve RM, Martin S, Settleman J, 1545 Yauch RL, Bourgon R: Reproducible pharmacogenomic profiling of cancer cell line 1546 panels. Nature 2016, 533:333-337. 1547 175. Revill K, Wang T, Lachenmayer A, Kojima K, Harrington A, Li J, Hoshida Y, Llovet JM, 1548 Powers S: Genome-wide methylation analysis and epigenetic unmasking identify 1549 tumor suppressor genes in hepatocellular carcinoma. Gastroenterology 2013, 1550 145:1424-1435 e1421-1425. 1551 176. Saito S, Matsui H, Kawano M, Kumagai K, Tomishige N, Hanada K, Echigo S, Tamura S, 1552 Kobayashi T: Protein phosphatase 2Cepsilon is an endoplasmic reticulum integral 1553 membrane protein that dephosphorylates the ceramide transport protein CERT to 1554 enhance its association with organelle membranes. J Biol Chem 2008, 283:6584-6593. 1555 177. Thean LF, Loi C, Ho KS, Koh PK, Eu KW, Cheah PY: Genome-wide scan identifies a copy 1556 number variable region at 3q26 that regulates PPM1L in APC mutation-negative 1557 familial colorectal cancer patients. Genes Chromosomes Cancer 2010, 49:99-106. 1558 178. Leithner K, Wohlkoenig C, Stacher E, Lindenmann J, Hofmann NA, Galle B, Guelly C, 1559 Quehenberger F, Stiegler P, Smolle-Juttner FM, et al: Hypoxia increases membrane 1560 metallo-endopeptidase expression in a novel lung cancer ex vivo model - role of tumor 1561 stroma cells. BMC Cancer 2014, 14:40. 1562 179. Park JY, Kim SA, Chung JW, Bang S, Park SW, Paik YK, Song SY: Proteomic analysis of 1563 pancreatic juice for the identification of biomarkers of pancreatic cancer. J Cancer Res 1564 Clin Oncol 2011, 137:1229-1238. 1565 180. Corso J, Pan KT, Walter R, Doebele C, Mohr S, Bohnenberger H, Strobel P, Lenz C, 1566 Slabicki M, Hullein J, et al: Elucidation of tonic and activated B-cell receptor signaling in 1567 Burkitt's lymphoma provides insights into regulation of cell survival. Proc Natl Acad Sci 1568 U S A 2016, 113:5688-5693. 1569 181. Wu F, Su SC, Tan GQ, Yan L, Li TY, Zhang HL, Yu JS, Wang BL: Mus81 knockdown 1570 sensitizes colon cancer cells to chemotherapeutic drugs by activating CHK1 pathway. 1571 Clin Res Hepatol Gastroenterol 2017, 41:592-601. 54

1572 182. Kim HJ, Jo MJ, Kim BR, Kim JL, Jeong YA, Na YJ, Park SH, Lee SY, Lee DH, Lee HS, et al: 1573 Reactive oxygen species modulator-1 (Romo1) predicts unfavorable prognosis in 1574 colorectal cancer patients. PLoS One 2017, 12:e0176834. 1575 183. Kim HJ, Jo MJ, Kim BR, Kim JL, Jeong YA, Na YJ, Park SH, Lee SY, Lee DH, Kim BH, et al: 1576 Overexpression of Romo1 is an unfavorable prognostic biomarker and a predictor of 1577 lymphatic metastasis in non-small cell lung cancer patients. Onco Targets Ther 2018, 1578 11:4233-4246. 1579 184. Starheim KK, Gromyko D, Evjenth R, Ryningen A, Varhaug JE, Lillehaug JR, Arnesen T: 1580 Knockdown of human N alpha-terminal acetyltransferase complex C leads to p53- 1581 dependent apoptosis and aberrant human Arl8b localization. Mol Cell Biol 2009, 1582 29:3569-3581. 1583 185. Liao X, Huang R, Liu X, Han C, Yu L, Wang S, Sun N, Li B, Ning X, Peng T: Distinct 1584 prognostic values of alcohol dehydrogenase mRNA expression in pancreatic 1585 adenocarcinoma. Onco Targets Ther 2017, 10:3719-3732. 1586 186. Wang P, Zhang L, Huang C, Huang P, Zhang J: Distinct Prognostic Values of Alcohol 1587 Dehydrogenase Family Members for Non-Small Cell Lung Cancer. Med Sci Monit 2018, 1588 24:3578-3590. 1589 187. Zhao L, Fan J, Xia S, Pan Y, Liu S, Qian G, Qian Z, Kang HB, Arbiser JL, Pollack BP, et al: 1590 HMG-CoA synthase 1 is a synthetic lethal partner of BRAF(V600E) in human cancers. J 1591 Biol Chem 2017, 292:10142-10152. 1592 188. Chen SW, Chou CT, Chang CC, Li YJ, Chen ST, Lin IC, Kok SH, Cheng SJ, Lee JJ, Wu TS, et al: 1593 HMGCS2 enhances invasion and metastasis via direct interaction with PPARalpha to 1594 activate Src signaling in colorectal cancer and oral cancer. Oncotarget 2017, 8:22460- 1595 22476. 1596 189. Castro-Giner F, Ratcliffe P, Tomlinson I: The mini-driver model of polygenic cancer 1597 evolution. Nat Rev Cancer 2015, 15:680-685. 1598 190. Rauhala HE, Teppo S, Niemela S, Kallioniemi A: Silencing of the ARP2/3 complex 1599 disturbs pancreatic cancer cell migration. Anticancer Res 2013, 33:45-52. 1600 191. Oji Y, Tatsumi N, Fukuda M, Nakatsuka S, Aoyagi S, Hirata E, Nanchi I, Fujiki F, Nakajima 1601 H, Yamamoto Y, et al: The translation elongation factor eEF2 is a novel 1602 tumorassociated antigen overexpressed in various types of cancers. Int J Oncol 2014, 1603 44:1461-1469. 1604 192. Patel H, Abduljabbar R, Lai CF, Periyasamy M, Harrod A, Gemma C, Steel JH, Patel N, 1605 Busonero C, Jerjees D, et al: Expression of CDK7, Cyclin H, and MAT1 Is Elevated in 1606 Breast Cancer and Is Prognostic in Estrogen Receptor-Positive Breast Cancer. Clin 1607 Cancer Res 2016, 22:5929-5938. 1608 193. Li B, Ni Chonghaile T, Fan Y, Madden SF, Klinger R, O'Connor AE, Walsh L, O'Hurley G, 1609 Mallya Udupi G, Joseph J, et al: Therapeutic Rationale to Target Highly Expressed CDK7 1610 Conferring Poor Outcomes in Triple-Negative Breast Cancer. Cancer Res 2017, 77:3834- 1611 3845. 1612 194. Wang Q, Li M, Zhang X, Huang H, Huang J, Ke J, Ding H, Xiao J, Shan X, Liu Q, et al: 1613 Upregulation of CDK7 in gastric cancer cell promotes tumor cell proliferation and 1614 predicts poor prognosis. Exp Mol Pathol 2016, 100:514-521. 1615 195. Wei F, Ding L, Wei Z, Zhang Y, Li Y, Qinghua L, Ma Y, Guo L, Lv G, Liu Y: Ribosomal 1616 protein L34 promotes the proliferation, invasion and metastasis of pancreatic cancer 1617 cells. Oncotarget 2016, 7:85259-85272. 55

1618 196. Yang S, Cui J, Yang Y, Liu Z, Yan H, Tang C, Wang H, Qin H, Li X, Li J, et al: Over-expressed 1619 RPL34 promotes malignant proliferation of non-small cell lung cancer cells. Gene 2016, 1620 576:421-428. 1621 197. Dai J, Wei W: Influence of the RPL34 gene on the growth and metastasis of oral 1622 squamous cell carcinoma cells. Arch Oral Biol 2017, 83:40-46. 1623 198. Karan D, Kelly DL, Rizzino A, Lin MF, Batra SK: Expression profile of differentially- 1624 regulated genes during progression of androgen-independent growth in human 1625 prostate cancer cells. Carcinogenesis 2002, 23:967-975. 1626 199. Knoll M, Macher-Goeppinger S, Kopitz J, Duensing S, Pahernik S, Hohenfellner M, 1627 Schirmacher P, Roth W: The ribosomal protein S6 in renal cell carcinoma: functional 1628 relevance and potential as biomarker. Oncotarget 2016, 7:418-432. 1629 200. Chen B, Tan Z, Gao J, Wu W, Liu L, Jin W, Cao Y, Zhao S, Zhang W, Qiu Z, et al: 1630 Hyperphosphorylation of ribosomal protein S6 predicts unfavorable clinical survival in 1631 non-small cell lung cancer. J Exp Clin Cancer Res 2015, 34:126. 1632 201. Hou L, Li Y, Wang Y, Xu D, Cui H, Xu X, Cong Y, Yu C: UBE2D1 RNA Expression Was an 1633 Independent Unfavorable Prognostic Indicator in Lung Adenocarcinoma, but Not in 1634 Lung Squamous Cell Carcinoma. Dis Markers 2018, 2018:4108919. 1635 202. Zabala-Letona A, Arruabarrena-Aristorena A, Martin-Martin N, Fernandez-Ruiz S, 1636 Sutherland JD, Clasquin M, Tomas-Cortazar J, Jimenez J, Torres I, Quang P, et al: 1637 mTORC1-dependent AMD1 regulation sustains polyamine metabolism in prostate 1638 cancer. Nature 2017, 547:109-113. 1639 203. Zhao X, Li J, He Y, Lan F, Fu L, Guo J, Zhao R, Ye Y, He M, Chong W, et al: A novel growth 1640 suppressor gene on chromosome 17p13.3 with a high frequency of mutation in human 1641 hepatocellular carcinoma. Cancer Res 2001, 61:7383-7387. 1642 204. Xu HN, Huang WD, Cai Y, Ding M, Gu JF, Wei N, Sun LY, Cao X, Li HG, Zhang KJ, et al: 1643 HCCS1-armed, quadruple-regulated oncolytic adenovirus specific for liver cancer as a 1644 cancer targeting gene-viro-therapy strategy. Mol Cancer 2011, 10:133. 1645 205. Gan Y, Gu J, Cai X, Hu J, Liu XY, Zhao X: Adenovirus-mediated HCCS1 overexpression 1646 elicits a potent antitumor efficacy on human colorectal cancer and hepatoma cells 1647 both in vitro and in vivo. Cancer Gene Ther 2008, 15:808-816. 1648 206. Su YC, Feng YH, Wu HT, Huang YS, Tung CL, Wu P, Chang CJ, Shiau AL, Wu CL: Elovl6 is a 1649 negative clinical predictor for liver cancer and knockdown of Elovl6 reduces murine 1650 liver cancer progression. Sci Rep 2018, 8:6586. 1651 207. Tzatsos A, Paskaleva P, Ferrari F, Deshpande V, Stoykova S, Contino G, Wong KK, Lan F, 1652 Trojer P, Park PJ, Bardeesy N: KDM2B promotes pancreatic cancer via Polycomb- 1653 dependent and -independent transcriptional programs. J Clin Invest 2013, 123:727-739. 1654 208. Wang Y, Zang J, Zhang D, Sun Z, Qiu B, Wang X: KDM2B overexpression correlates with 1655 poor prognosis and regulates glioma cell growth. Onco Targets Ther 2018, 11:201-209. 1656 209. Zheng Q, Fan H, Meng Z, Yuan L, Liu C, Peng Y, Zhao W, Wang L, Li J, Feng J: Histone 1657 demethylase KDM2B promotes triple negative breast cancer proliferation by 1658 suppressing p15INK4B, p16INK4A, and p57KIP2 transcription. Acta Biochim Biophys Sin 1659 (Shanghai) 2018, 50:897-904. 1660 210. Zhu XX, Yan YW, Ai CZ, Jiang S, Xu SS, Niu M, Wang XZ, Zhong GS, Lu XF, Xue Y, et al: 1661 Jarid2 is essential for the maintenance of tumor initiating cells in bladder cancer. 1662 Oncotarget 2017, 8:24483-24490.

1663 211. Schleich S, Strassburger K, Janiesch PC, Koledachkina T, Miller KK, Haneke K, Cheng YS, 1664 Kuechler K, Stoecklin G, Duncan KE, Teleman AA: DENR-MCT-1 promotes translation re- 1665 initiation downstream of uORFs to control tissue growth. Nature 2014, 512:208-212. 1666 212. Wang YW, Lin KT, Chen SC, Gu DL, Chen CF, Tu PH, Jou YS: Overexpressed-eIF3I 1667 interacted and activated oncogenic Akt1 is a theranostic target in human 1668 hepatocellular carcinoma. Hepatology 2013, 58:239-250. 1669 213. Qi J, Dong Z, Liu J, Zhang JT: EIF3i promotes colon oncogenesis by regulating COX-2 1670 protein synthesis and beta-catenin activation. Oncogene 2014, 33:4156-4163. 1671 214. Wang C, Jin G, Jin H, Wang N, Luo Q, Zhang Y, Gao D, Jiang K, Gu D, Shen Q, et al: 1672 Clusterin facilitates metastasis by EIF3I/Akt/MMP13 signaling in hepatocellular 1673 carcinoma. Oncotarget 2015, 6:2903-2916. 1674 215. Torrance V, Lydall D: Overlapping open reading frames strongly reduce human and 1675 yeast STN1 gene expression and affect telomere function. PLoS Genet 2018, 1676 14:e1007523. 1677 216. Widschwendter M, Apostolidou S, Raum E, Rothenbacher D, Fiegl H, Menon U, 1678 Stegmaier C, Jacobs IJ, Brenner H: Epigenotyping in peripheral blood cell DNA and 1679 breast cancer risk: a proof of principle study. PLoS One 2008, 3:e2656. 1680 217. Khakpour G, Pooladi A, Izadi P, Noruzinia M, Tavakkoly Bazzaz J: DNA methylation as a 1681 promising landscape: A simple blood test for breast cancer prediction. Tumour Biol 1682 2015, 36:4905-4912. 1683 218. Savci-Heijink CD, Halfwerk H, Koster J, van de Vijver MJ: A novel gene expression 1684 signature for bone metastasis in breast carcinomas. Breast Cancer Res Treat 2016, 1685 156:249-259. 1686 219. Kornberg RD: The molecular basis of eucaryotic transcription. Cell Death Differ 2007, 1687 14:1989-1997. 1688 220. Hartwell LH: Nobel Lecture. Yeast and cancer. Biosci Rep 2002, 22:373-394. 1689 221. Blackburn EH: Telomeres and telomerase: the means to the end (Nobel lecture). 1690 Angew Chem Int Ed Engl 2010, 49:7405-7421. 1691 222. Schekman R, Sudhof T: An interview with Randy Schekman and Thomas Sudhof. Trends 1692 Cell Biol 2014, 24:6-8. 1693 223. Mizushima N: The exponential growth of autophagy-related research: from the 1694 humble yeast to the Nobel Prize. FEBS Lett 2017, 591:681-689. 1695 224. Bhattacharya B, Mohd Omar MF, Soong R: The Warburg effect and drug resistance. Br J 1696 Pharmacol 2016, 173:970-979. 1697 225. Yang F, Kemp CJ, Henikoff S: Anthracyclines induce double-strand DNA breaks at active 1698 gene promoters. Mutat Res 2015, 773:9-15. 1699 226. Flavahan WA, Gaskell E, Bernstein BE: Epigenetic plasticity and the hallmarks of cancer. 1700 Science 2017, 357. 1701 227. de Cubas AA, Rathmell WK: Epigenetic modifiers: activities in renal cell carcinoma. Nat 1702 Rev Urol 2018, 15:599-614. 1703 228. Lopez G, Liu J, Ren W, Wei W, Wang S, Lahat G, Zhu QS, Bornmann WG, McConkey DJ, 1704 Pollock RE, Lev DC: Combining PCI-24781, a novel histone deacetylase inhibitor, with 1705 chemotherapy for the treatment of soft tissue sarcoma. Clin Cancer Res 2009, 15:3472- 1706 3483.

1707 229. Yang C, Choy E, Hornicek FJ, Wood KB, Schwab JH, Liu X, Mankin H, Duan Z: Histone 1708 deacetylase inhibitor (HDACI) PCI-24781 potentiates cytotoxic effects of doxorubicin in 1709 bone sarcoma cells. Cancer Chemother Pharmacol 2011, 67:439-446. 1710 230. Choy E, Flamand Y, Balasubramanian S, Butrynski JE, Harmon DC, George S, Cote GM, 1711 Wagner AJ, Morgan JA, Sirisawad M, et al: Phase 1 study of oral abexinostat, a histone 1712 deacetylase inhibitor, in combination with doxorubicin in patients with metastatic 1713 sarcoma. Cancer 2015, 121:1223-1230. 1714 231. Ververis K, Rodd AL, Tang MM, El-Osta A, Karagiannis TC: Histone deacetylase inhibitors 1715 augment doxorubicin-induced DNA damage in cardiomyocytes. Cell Mol Life Sci 2011, 1716 68:4101-4114. 1717 232. Li T, Zhang C, Hassan S, Liu X, Song F, Chen K, Zhang W, Yang J: Histone deacetylase 6 in 1718 cancer. J Hematol Oncol 2018, 11:111. 1719 233. Tu Y, Hershman DL, Bhalla K, Fiskus W, Pellegrino CM, Andreopoulou E, Makower D, 1720 Kalinsky K, Fehn K, Fineberg S, et al: A phase I-II study of the histone deacetylase 1721 inhibitor vorinostat plus sequential weekly paclitaxel and doxorubicin- 1722 cyclophosphamide in locally advanced breast cancer. Breast Cancer Res Treat 2014, 1723 146:145-152. 1724 234. Vogl DT, Raje N, Jagannath S, Richardson P, Hari P, Orlowski R, Supko JG, Tamang D, 1725 Yang M, Jones SS, et al: Ricolinostat, the First Selective Histone Deacetylase 6 Inhibitor, 1726 in Combination with Bortezomib and Dexamethasone for Relapsed or Refractory 1727 Multiple Myeloma. Clin Cancer Res 2017, 23:3307-3315. 1728 235. Xu C, Nikolova O, Basom RS, Mitchell RM, Shaw R, Moser RD, Park H, Gurley KE, Kao MC, 1729 Green CL, et al: Functional Precision Medicine Identifies Novel Druggable Targets and 1730 Therapeutic Options in Head and Neck Cancer. Clin Cancer Res 2018, 24:2828-2843. 1731 236. Grandori C, Kemp CJ: Personalized Cancer Models for Target Discovery and Precision 1732 Medicine. Trends Cancer 2018, 4:634-642. 1733 237. Puca L, Bareja R, Prandi D, Shaw R, Benelli M, Karthaus WR, Hess J, Sigouros M, 1734 Donoghue A, Kossai M, et al: Patient derived organoids to model rare prostate cancer 1735 phenotypes. Nat Commun 2018, 9:2404. 1736 238. Kim MM, Parolia A, Dunphy MP, Venneti S: Non-invasive metabolic imaging of brain 1737 tumours in the era of precision medicine. Nat Rev Clin Oncol 2016, 13:725-739. 1738 239. Costanzo M, VanderSluis B, Koch EN, Baryshnikova A, Pons C, Tan G, Wang W, Usaj M, 1739 Hanchard J, Lee SD, et al: A global genetic interaction network maps a wiring diagram 1740 of cellular function. Science 2016, 353. 1741 240. Ihmels J, Collins SR, Schuldiner M, Krogan NJ, Weissman JS: Backup without 1742 redundancy: genetic interactions reveal the cost of duplicate gene loss. Mol Syst Biol 1743 2007, 3:86. 1744 241. Phillips PC: Epistasis--the essential role of gene interactions in the structure and 1745 evolution of genetic systems. Nat Rev Genet 2008, 9:855-867.

1746

1747

1748

1749 Figures, Tables, and Additional Files

1750 Figures:

1751 Figure 1. Experimental strategy to characterize differential doxorubicin-gene

1752 interaction, with respect to the Warburg metabolic transition. (A) The phenomic

1753 model incorporates treatment of individually grown cultures of the YKO/KD collection

1754 with increasing doxorubicin (0, 2.5, 5, 7.5, and 15 ug/mL) in “fermentable/glycolytic”

1755 (HLD) or “non-fermentable/respiratory” (HLEG) media. (B) Representative cell array

1756 images, treated and untreated with 15 ug/mL doxorubicin. (C) Time series of individual

1757 culture images, exemplifying gene deletion suppression (vps54-∆0) and gene deletion

1758 enhancement (mms1-∆0), relative to parental control (‘RF1’) in HLEG media with

1759 indicated concentrations (0, 5, and 15 ug/mL) of doxorubicin. (D) After image analysis,

1760 data time series are fit to a logistic growth function, G(t), to obtain the cell proliferation

1761 parameters (CPPs), K (carrying capacity), L (time at which K/2 is reached) and r

1762 (maximum specific rate) for each culture. ‘∆L’ (left panel) indicates Ki (see methods). (E)

1763 Interaction is quantified by linear regression of Li (indicated ‘Delta_L’ and ‘Delta_K’ in

1764 right panels; see methods) across the entire dose range, which is converted to a z-score

1765 by dividing with the variance of the parental reference control (see methods). (F) Gene

1766 interaction profiles were grouped by recursive expectation-maximization clustering

1767 (REMc) to reveal deletion enhancing and deletion suppressing doxorubicin-gene

1768 interaction modules and the influence of the Warburg effect. Resulting clusters were

1769 analyzed with GOTermFinder (GTF) to identify enriched biological functions. (G) Gene

1770 Ontology Term Averaging (GTA) was used as a complement to REMc/GTF. (H) The

1771 model for genetic buffering of doxorubicin cytotoxicity incorporates primary and

1772 interaction effects involving glycolysis (green), and respiration (red), to explain the

1773 influence of Warburg context (blue) on doxorubicin-gene interaction (black). 59

1774 Figure 2. Q-HTCP provides cell proliferation parameters as phenotypes to quantify

1775 gene interaction. (A, B) Average pixel intensity and standard deviation for 768

1776 reference strain cultures at indicated times after exposure to escalating doxorubicin

1777 concentrations in (A) HLD or (B) HLEG media. (C-D) Semi-log plots after fitting the data

1778 plotted above for (C) HLD or (D) HLEG to a logistic function (see Fig. 1D). (E-L) CPP

1779 distributions from data depicted in panels A-D for (E-H) HLD and (I-J) HLEG, including

1780 (E, I) L, (F, J) K, (G, K) r, and (H, L) AUC. (M, N) Comparison of doxorubicin-gene

1781 interaction scores using the L vs. K CPP in the context of either (M) HLD or (N) HLEG

1782 media. Score distributions of knockout (YKO, green), knock down / DAmP (YKD, Red),

1783 and non-mutant parental (Ref, Purple) strain cultures are indicated along with thresholds

1784 for deletion enhancement and suppression (dashed lines at +/- 2). (O) Differential

1785 doxorubicin-gene interaction (using L as the CPP) for HLD vs. HLEG, classified with

1786 respect to Warburg metabolism as non-specific (NS), respiratory-specific (R), or

1787 glycolysis-specific (G) deletion enhancement (Enh) or deletion suppression (Sup). (P-R)

1788 Comparisons between genome-wide studies of doxorubicin-gene interaction: (P) Genes

1789 reported from Westmoreland et al. (green), Xia et al. (Red) or both studies (purple) are

1790 plotted overlying L interaction scores (gray) in HLD vs. HLEG. (Q-R) L interaction scores

1791 (gray) for genes reported by Westmoreland et al. (green), Xia et al. (red), or both studies

1792 (purple) in (Q) HLD or (R) HLEG media. (S-T) Doxorubicin-gene interaction from

1793 genome wide (GWS) and validation (V) studies on (S) HLD or (T) HLEG media.

1794 Figure 3. Characterization of Warburg-differential, doxorubicin-gene interaction

1795 profiles. (A) The union of enhancers (L z-score > 2) or suppressors (L z-score < -2)

1796 from the HLD and HLEG analyses totaled 2802 gene interaction profiles that were

1797 subjected to REMc (see methods). (B-C) The column order is the same for all heatmaps;

1798 ‘+’ indicates doxorubicin-gene interaction and ‘-‘ indicates ‘shift’ (K0; see methods).

1799 Interactions by K are negative (brown) if enhancing and positive (purple) if suppressing,

1800 while the signs of interaction are reversed for L (see methods). The heatmap color scale

1801 is incremented by twos; red indicates no growth curve in the absence of doxorubicin. (B)

1802 First round cluster 1-0-7 has a gene interaction profile indicative of HLEG-specific

1803 deletion enhancement. (C) Second round clusters (2-0.7-X) are ordered left to right by

1804 strength of influence. (D) The pattern of distributions for the different doxorubicin-gene

1805 interaction scores (‘+’ columns only from panel C) summarizes respective clusters from

1806 panel C. Deletion enhancement is considered to be qualitatively stronger if observed for

1807 K in addition to L.

1808 Figure 4. A summary of the first and second rounds of REMc. First round clusters

1809 are at the left end of each row of heatmap thumbnails; second round clusters derived

1810 from each first round cluster are ordered to the right by relative strength. Rows are

1811 grouped into panels by similarity in their gene interaction profiles. The columns in each

1812 heatmap have the same order from left to right (see inset panel), with K to the left and L

1813 to the right. Within the K and L groups, HLD is to the left and HLEG to the right. Within

1814 each of the CPP-media groupings, ‘shift’ (-) is left of the doxorubicin-gene interaction (+).

1815 (A) Respiration-specific enhancement. (B) Warburg-independent enhancement. (C)

1816 Glycolysis-specific enhancement. (D) HLD and HLEG suppression modules. (E)

1817 Respiratory deficiency.

1818 Figure 5. GO annotations associated with deletion enhancement or suppression of

1819 doxorubicin cytotoxicity, with respect to Warburg-dependence. Representative GO

1820 terms are listed, which were identified by REMc/GTF (orange), GTA (purple), or both

1821 methods, for HLD (left, red), HLEG (right, blue), or both media types (black), and for 61

1822 enhancement (above dashed line) or suppression (below dashed line) of doxorubicin

1823 cytotoxicity. Distance above or below the horizontal dashed line indicates the GTA value

1824 for terms identified by REMc or the GTA score if identified by GTA (see methods). See

1825 Additional Files 5 and 6, respectively, for all REMc and GTA results.

1826 Figure 6. Respiration increases the role for chromatin organization in buffering

1827 doxorubicin toxicity. (A) GO term-specific heatmaps for chromatin organization and its

1828 child terms (indicated by arrows) clarify related but distinct biological functions that buffer

1829 doxorubicin, with respect to Warburg status. (B-C) L-based doxorubicin-gene interaction

1830 scores associated with GO terms that were enriched in cluster 2-0.7-2. Dashed lines

1831 indicate z-score thresholds for enhancers (>2) and suppressors (<-2). Sub-threshold

1832 gene interaction values are plotted, but not labeled.

1833 Figure 7. Distinct histone modifications differentially influence doxorubicin

1834 cytotoxicity. (A) Rpd3L and Rpd3S complexes exert strong HLEG-specific doxorubicin-

1835 enhancing influence relative to other Sin3-type histone deacetylases and the HDA1

1836 complex. (B) In contrast to histone deacetylation (panel A), histone acetylation exhibits

1837 deletion enhancement that is Warburg-independent. (C) Histone H3K4 methylation by

1838 the Set1C/COMPASS complex, which requires histone mono-ubiquitination of H2B by

1839 the Bre1/Rad6 complex, is opposed by Jhd2, a histone H3K4 demethylase. The

1840 respiration-specific deletion enhancing interactions suggest the Warburg transition can

1841 protect tumors promoted by certain types of chromatin deregulation from doxorubicin.

1842 Figure 8. Additional respiration-specific deletion-enhancing and -suppressing

1843 functions that influence doxorubicin cytotoxicity. Heatmaps depicting complete

1844 phenotypic profiles are inset, corresponding to plots of L-based doxorubicin-gene

1845 interaction. (A) Protein folding in endoplasmic reticulum and the N-terminal protein-

1846 acetylating NatC complex are largely respiratory-dependent in their deletion-enhancing

1847 influence. (B) DNA topological change exerts deletion-enhancing interactions in both

1848 respiratory and glycolytic contexts. (C) GTA-identified terms tend to be smaller in

1849 number and display greater variability in the Warburg dependence among genes sharing

1850 the same functional annotation. (D) Functions implicated in respiratory-dependent

1851 deletion suppression of doxorubicin toxicity.

1852 Figure 9. Glycolysis-specific enhancement and suppression of doxorubicin

1853 cytotoxicity. Doxorubicin-gene interaction profiles for HLD-specific GO terms identified

1854 by GTA are depicted for (A) deletion enhancement and (B) deletion suppression.

1855 Figure 10. Warburg-independent deletion enhancement of doxorubicin

1856 cytotoxicity. Gene interaction profiles showing deletion enhancement in both

1857 respiratory and glycolytic context included: (A) double-strand break repair via

1858 homologous recombination, and its child terms (indicated by arrows), and (B) the Cul8-

1859 RING ubiquitin ligase, Ino80 complex, Lst4-7 complex, and MCM complex.

1860 Figure 11. Warburg-independent deletion suppression of doxorubicin cytotoxicity.

1861 Doxorubicin-gene interaction profiles and L-interaction plots for genes associated with

1862 deletion suppression in HLEG or HLD media, including: (A) Cellular sphingolipid

1863 homeostasis, along with its parent term, lipid homeostasis, and related term sphingolipid

1864 metabolism; and (B) actin cortical patch localization and telomere tethering at nuclear

1865 periphery.

1866 Figure 12. Use of the yeast phenomic model to predict doxorubicin-gene

1867 interaction in cancer cells. (A) BiomaRt was used to assign yeast-human gene

1868 homology for the GDSC and gCSI datasets. (B) PharmacoGx was used to retrieve

1869 differential gene expression for doxorubicin sensitive cell lines from the gCSI and GDSC

1870 databases, searching data from individual tissues or across data aggregated from all

1871 tissues. Human genes that are underexpressed in doxorubicin sensitive cell lines (UES)

1872 with yeast homologs that are deletion enhancers are predicted to be causal in their

1873 phenotypic association. Similarly, human genes that are overexpressed in doxorubicin

1874 sensitive cancer cell lines (OES) would be predicted to be causal if the yeast homolog

1875 was a deletion suppressor in the phenomic dataset. (C-D) Boxes inside of Venn

1876 diagrams indicate the genes for which gene interaction profiles are shown in the

1877 heatmaps below. Gene names are to the right of heatmaps, with blue labels indicating

1878 genes identified in both the GDSC and gCSI databases and black labels indicating

1879 genes found only in the gCSI dataset. The category of homology (see panel A) is

1880 indicated in the left column of each heatmap. (C) Deletion enhancement by yeast genes

1881 predicts human functions that buffer doxorubicin cytotoxicity, and thus, reduced

1882 expression of homologs in cancer cell lines is predicted to increase doxorubicin

1883 sensitivity. (D) Deletion suppression by yeast genes predicts functions that mediate

1884 cytotoxicity and is shown for human homologs having significant association of

1885 overexpression in cancer cell lines with increased doxorubicin sensitivity. (E-F) Genes

1886 representing enhancing or suppressing modules from REMc or GTA that are (E) UES or

1887 (F) OES in at least one of the two databases. Red labels indicate genes found only in

1888 the GDSC database. Additional File 13 reports all results from the analysis described

1889 above, including assessment of individual tissues.

1890 Figure 13. Yeast phenomic model for the influence of Warburg metabolism on

1891 doxorubicin-gene interaction. Shaded areas indicate influences that are relatively

1892 Warburg-dependent, being red or green if their effects are relatively specific to a

1893 respiratory or glycolytic context, respectively. Processes that influence doxorubicin

1894 cytotoxicity in a more Warburg-independent manner are unshaded. Arrowheads indicate

1895 processes for which genes predominantly transduce doxorubicin toxicity, based on their

1896 loss of function suppressing its growth inhibitory effects. Conversely, a perpendicular bar

1897 at the line head indicates a process that buffers doxorubicin toxicity, as genetic

1898 compromise of its function enhances the growth inhibitory effects of doxorubicin.

1899 Additional Files:

1900 Additional File 1. Supplemental figures. Figure S1. Doxorubicin dose responses of

1901 the YKO/KD parental strains, BY4741a, BY4742alpha, and BY4743a/alpha diploid.

1902 Figure S2. Correlation between interaction scores based on L vs. other CPPs (K, r, and

1903 AUC), for both HLD and HLEG media. Figure S3. Doxorubicin-gene interaction profiles

1904 for selected mitochondrial GO terms. Figure S4. Deletion of mitochondrial genes tends

1905 to influence doxorubicin-gene interaction in a respiratory (HLEG media) more so than a

1906 glycolytic (HLD media) context. Figure S5. Heatmaps for GO terms comprised of

1907 overlapping gene sets. Figure S6. Pleiotropic phenotypic influences from genetic

1908 perturbation of ribonucleoprotein complex subunit organization. Figure S7. HLD-specific

1909 deletion enhancement of doxorubicin toxicity by evolutionarily conserved genes. See

1910 also Additional File 10 (Table S13). Figure S8. GO term-specific heatmaps for mRNA 3’

1911 end processing and mRNA cleavage gene interaction profiles. Figure S9. Suppression

1912 of doxorubicin cytotoxicity by perturbation of sphingolipid and ceramide metabolism.

1913 Figure S10. Deletion suppressing doxorubicin-gene interaction for nuclear pore and

1914 actin cortical patch functions is relatively Warburg-independent.

1915 Additional File 2. Doxorubicin-gene interaction data; Tables S1-S8. Tables S1-S4

1916 are the genome-wide experiment: Table S1. YKO/KD strains in HLEG. Table S2.

1917 Reference cultures in HLEG. Table S3. YKO/KD strains in HLD. Table S4. Reference

1918 cultures in HLD. Tables S5-S8 are the validation study: Table S5. YKO/KD strains in

1919 HLEG. Table S6. Reference cultures in HLEG. Table S7. YKO/KD strains in HLD.

1920 Table S8. Reference cultures in HLD.

1921 Additional File 3. Interaction plots for HLEG. (A, B) Genome-wide and (C, D)

1922 validation analyses for (A, C) YKO/KD and (B, D) reference strains in HLEG. See also

1923 methods and Additional File 2.

1924 Additional File 4. Interaction plots for HLD. (A, B) Genome-wide and (C, D) validation

1925 analyses. (A, C) YKO/KD and (B, D) reference strains in HLD media. See also methods

1926 and Additional File 2.

1927 Additional File 5. REMc results with doxorubicin-gene interaction profile heatmaps

1928 and Gene Ontology enrichment (GO Term Finder; GTF) results. File A contains

1929 REMc results and associated gene interaction and shift data. File B is the heatmap

1930 representation of each REMc cluster after incorporating shift values and hierarchical

1931 clustering. File C contains the GTF results obtained for REMc clusters for the three

1932 ontologies – process, function, and component.

1933 Additional File 6. Gene Ontology Term Averaging (GTA) results and interactive

1934 plots. File A contains all GTA values, cross-referenced with REMc-enriched terms. File

1935 B displays GTA values associated with above-threshold GTA scores (see note below)

1936 plotted for HLD vs. HLEG. GTA values for REMc-enriched terms are also included

1937 (regardless of whether |GTA score| >2). File C displays a subset of File B, containing

1938 only GO Terms with above-threshold GTA scores and that were enriched by REMc/GTF.

1939 File D reports GTA value using the K parameter. Files B-D should be opened in an

1940 Internet web browser so that embedded information from Additional File 6A can be

1941 viewed by scrolling over points on the graphs. Subsets in each of the plots can be

1942 toggled off and on by clicking on the respective legend label. In the embedded

1943 information, X1 represents HLEG and X2 represents HLD information. Note: The GTA

1944 score threshold (for L) indicates that GTA-gtaSD > 2 for enhancers or GTA+gtaSD < -2

1945 for suppressors, in at least one media.

1946 Additional File 7. Systematic comparisons involving genome-wide studies of

1947 doxorubicin-gene interaction. Table S9. Genes with deletion-enhancing doxorubicin-

1948 gene interaction from Xia et al. 2007 and Westmoreland et al. 2009. Table S10.

1949 Summary of experimental details associated with Table S9. Table S11. Test of

1950 enrichment for doxorubicin-gene interaction among genes encoding proteins predicted

1951 as substrates of the NatC complex. Table S12. Test of enrichment for doxorubicin-gene

1952 interaction among genes predicted to be regulated by conserved uORFs (Cvijovic et al.

1953 2007).

1954 Additional File 8. Quantitative summaries of REMc clusters. File A depicts REMc

1955 results, in terms of cluster distributions of L and K interaction (‘shift’ is not used for REMc

1956 and thus is not displayed), as a way to visualize cluster differences quantitatively. File B

1957 is organized by first round clusters and plots the change in p-value for significant terms

1958 with respect to round of clustering. Clusters derived from one another and sharing

1959 enrichment of the same GO term are connected by a line. Only GO terms with a

1960 background size of 500 or smaller are included. Scroll over a symbol to see embedded

1961 detail about each GO term. The square root of the p-value is used on the y-axis to

1962 evenly distribute data.

1963 Additional File 9. GO term-specific heatmaps for REMc/GTF-enriched clusters. GO

1964 term-specific heatmaps for significant GO process terms were generated as described in

1965 methods and Figures 3 and 4. Any related child terms are presented in subsequent

1966 pages of the parent file name. GO terms with more than 100 children, with 2 or fewer

1967 genes annotated to the term, or a file size over 300KB are not shown. All heatmaps are

1968 generated with the same layout (see Figures 3 and 4).

1969 Additional File 10 (Table S13). HLD-specific gene deletion enhancement, not

1970 associated with ‘shift’ / growth deficiency. Data were selected for yeast-human

1971 homologs if the respective YKO/KD strains generated growth curves in both HLD and

1972 HLEG media (in the absence doxorubicin), and either of the following two sets of criteria

1973 were met: (1) HLD L interaction > 2 and HLEG L interaction < 2; these data were further

1974 filtered for HLD L Interaction - HLD L Shift > 4, and are presented in Fig. S7A.; or (2)

1975 HLD L Interaction – HLEG L interaction > 4 and HLEG K interaction > - 10; these data

1976 were further filtered for HLD L Interaction - HLD L Shift > 4, and are presented in Fig.

1977 S7B. Data included in Fig. S7 are indicated in the last column.

1978 Additional File 11. Integration of yeast phenomic and cancer cell line

1979 pharmacogenomic data to predict human genes that modify doxorubicin toxicity

1980 in cancer cells. (A) Tables of UES and OES human genes and whether their yeast

1981 homologs were found to be deletion enhancing or deletion suppressing, respectively. (B-

1982 C) Overlap between the gCSI and GDSC1000 databases with regard to UES and OES

1983 human genes (B) across all tissues or (C) for individual tissues. Note: the intersection of

1984 UES with OES between gCSI and GDSC was used as a negative control for assessing

1985 UES and OES overlap. (D-E) Yeast phenomic doxorubicin-gene interaction profiles for

1986 homologs of human UES or OES genes, sub-classified according to interaction type

1987 (deletion enhancing or suppressing) and Warburg-dependence of the interaction, for the

1988 (D) gCSI or (E) GDSC1000 databases. Similar to Figure 12, yeast-human homology

1989 relationships are shown to the left of heatmaps (blue - one to one; green - one to many;

1990 red - many to many). (F-I) Interactive plots for yeast-human homologs, comparing the p-

1991 value of human genes to L interaction scores for yeast counterparts in (F, G) HLD or (H,

1992 I) HLEG from (F, H) gCSI or (G, I) GDSC1000. For the standardized coefficient

1993 (‘estimate’; color gradient), a negative value (purple) indicates UES, while a positive

1994 value (orange) indicates OES. Thus, the model would predict causality for a human gene

1995 if its yeast homolog has a positive L interaction (deletion enhancing) and is colored

1996 purple (UES), or a negative L interaction (deletion suppressing) and colored orange

1997 (OES). Genes are only plotted if the human homolog was significant (p-value < 0.05).

1998 Tables:

1999

2000 Table 1. GO Terms enriched in REMc clusters. 2001 bioRxiv preprint certified bypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailableunder GTA GTA Media INT Clust GO Term Name p-value Genes HLEG HLD VPS71|RSC2|SWR1|LDB7|HHF1|RSC4|IES1|ISW1|ARP6|RTT106|HIR3|SWC3| Resp Enh 5.0 2.8 1-0-7 nucleosome organization 1.1E-07 HPC2|YAF9|HIR1|HIR2|HTB1|NHP6A|SWC5|NHP10 Resp Enh 7.1 0.1 1-0-7 Set1C/COMPASS complex 5.5E-04 SPP1|SDC1|SWD1|SWD3|BRE2

Resp Enh 3.9 -0.6 1-0-7 histone methylation 4.1E-03 SPP1|SDC1|LGE1|NOP1|SWD3|HHF1|SWD1|BRE2 doi: Resp Enh 3.4 3.0 1-0-7 protein import into mitochondrial matrix 6.4E-03 MGR2|TOM7|YME1|TOM70|PAM17|TIM17|TIM23|TOM6 Resp Enh 0.6 0.8 2-0.7-1 ER membrane protein complex 4.6E-06 EMC6|EMC4|EMC3|EMC5 https://doi.org/10.1101/517490 Resp Enh 4.6 0.2 2-0.7-2 Sin3-type complex 1.5E-05 RCO1|RXT2|SAP30|PHO23|DEP1|UME1 Resp Enh 5.2 -0.1 2-0.7-2 Rpd3L complex 7.1E-05 RXT2|SAP30|PHO23|DEP1|UME1 Resp Enh 7.3 1.6 2-0.7-2 Swr1 complex 1.2E-06 SWC3|SWC5|VPS71|YAF9|SWR1|ARP6 Resp Enh 5.9 2.1 2-0.7-2 histone exchange 5.7E-06 SWC3|SWC5|VPS71|YAF9|SWR1|ARP6 Resp Enh 5.0 3.4 2-0.7-2 ATP-dependent chromatin remodeling 2.4E-04 SWC3|SWC5|VPS71|YAF9|SWR1|LDB7|ARP6 Resp Enh 11.9 0.2 2-0.7-2 HIR complex 6.6E-06 HPC2|HIR1|HIR3|HIR2 Resp Enh 11.4 3.2 2-0.7-2 DNA replication-independent nucleosome assembly 4.5E-04 HPC2|HIR1|HIR3|HIR2 Resp Enh 11.0 1.7 1-0-8 respiratory chain complex III assembly 4.2E-02 QCR9|CBP4|FMP25 Resp Enh 7.9 0.7 2-0.8-0 DNA topological change 2.6E-02 TOP3|MUS81 Resp Enh 14.9 -0.4 2-0.8-1 NatC complex 5.6E-03 MAK31|MAK3

Resp Sup -2.6 -1.5 2-0.3-1 regulation of fatty acid beta-oxidation 2.1E-02 ADR1|OAF1|PIP2 ; this versionpostedJanuary15,2019. a Resp Sup -0.3 6.7 2-0.3-5 translation reinitiation 2.0E-02 TMA20|TIF34|TMA22 CC-BY-NC 4.0Internationallicense RSA4|HBS1|BRR1|SDO1|RPS17A|DHH1|CLF1|RRP7|TIF6|RPS14A|RPS27B| Glyc Enh 1.1 0.5 2-0.2-2 ribonucleoprotein complex subunit organization 1.9E-02 PRP9 Glyc Sup -2.2 -3.0 2-0.4-0 7-methylguanosine cap hypermethylation 5.6E-03 SWM2|TGS1 Glyc Sup 1.5 -0.4 2-0.4-2 mRNA 3'-end processing 8.6E-04 MPE1|CDC73|YSH1|KIN28|RNA14|NRD1 Glyc Sup 1.3 0.9 2-0.4-2 mRNA cleavage 3.3E-02 MPE1|YSH1|POP8|RNA14 Glyc Sup -0.8 -2.9 2-0.4-2 meiotic chromosome condensation 3.4E-03 SMC2|YCG1|YCS4 Glyc Sup -1.0 -2.7 2-0.4-2 condensin complex 2.8E-03 SMC2|YCG1|YCS4 CTK3|SIT4|RTT109|RVB1|RAD54|MMS22|CDC1|RAD55|PSF3|RAD50|BUD25| Both Enh 2.9 2.3 1-0-6 cellular response to DNA damage stimulus 4.1E-08 RAD51|MRE11|ARP8|ARP4|RAD57|TFB1|CDC7|RAD52|NPL6 Both Enh 5.0 5.0 1-0-6 double-strand break repair via homologous recombination 2.9E-07 PSF3|RAD50|RAD51|MRE11|RAD54|MMS22|RAD57|CDC7|RAD52|RAD55 double-strand break repair via synthesis-dependent strand Both Enh 7.7 9.7 1-0-6 4.3E-06 RAD54|RAD57|RAD51|RAD52|MRE11|RAD55 annealing

Both Enh 9.2 5.0 2-0.6-1 ATP-dependent 3'-5' DNA helicase activity 1.9E-04 RVB1|ARP5|ARP8|ARP4 . The copyrightholderforthispreprint(whichwasnot Both Enh 9.0 2.5 2-0.6-1 Ino80 complex 2.1E-05 RVB1|IES6|ARP5|ARP8|ARP4 Both Enh 3.4 1.6 2-0.6-1 histone acetylation 4.1E-02 RTT109|RVB1|NGG1|SPT20|ARP4 Resp Enh 7.4 1.1 2-0.2-1 protein urmylation 1.1E-03 URM1|URE2|UBA4|ELP2 Both Enh 9.9 3.9 2-0.2-1 Lst4-Lst7 complex 3.1E-02 LST7|LST4 Both Sup -4.5 -2.3 2-0.4-1 cellular sphingolipid homeostasis 9.6E-05 VPS53|VPS52|VPS54|VPS51 Both Sup -12.2 -7.0 2-0.4-1 fatty acid elongase activity 2.9E-02 ELO3|ELO2 Both Sup -3.0 -1.3 2-0.4-1 actin cortical patch localization 8.1E-03 RVS167|LSB3|RVS161|VRP1 Both Sup -9.0 -3.5 2-0.4-1 Rvs161p-Rvs167p complex 1.7E-02 RVS167|RVS161 Both Sup -4.4 -0.6 2-0.4-1 telomere tethering at nuclear periphery 1.8E-02 NUP60|MLP1|NUP120|NUP133 2002 The table headers are defined as follows: For Column ‘Media’, ‘Resp’, ‘Glyc’, and ‘Both’ refer to whether the gene interaction 2003 type observed for the REMc cluster associated with the term was prominent in HLEG, HLD, or both media (see Fig. 4). 2004 For column ‘INT’, ‘Enh’ and ‘Sup’ indicate deletion enhancing or suppressing. Column ‘GTA’ refers to GO Term Average. 2005 Column ‘Clust’ refers to REMc ID.

2006 Table 2. GO terms identified by GTA

HLEG HLEG HLD HLD REMc GO Term Name Media INT Genes p-value GTA gtaSD GTA gtaSD related bioRxiv preprint HIR complex Resp Enh 11.9 1.5 0.2 0.9 HIR1 | HIR2 | HPC2 | HIR3 2-0.7-2 6.6E-06 certified bypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailableunder histone monoubiquitination Resp Enh 11.4 7.0 0.1 1.2 RAD6 | BRE1 NA NA RVB1 | IES6 | ARP5 | ARP8 | ARP4 | ARP7 | IES5 | IES3 | NHP10 Ino80 complex Resp Enh 9.0 6.8 2.5 7.7 3-0.6.1-1 1.5E-06 | IES2 | IES1 | RVB2 | IES4 | TAF14 histone H4 acetylation Resp Enh 8.0 4.8 -0.8 2.1 ESA1 | NGG1 | ELP4 | EAF3 | HAT1 NA NA mitochondrial respiratory chain complex III assembly Resp Enh 11.0 6.8 1.7 2.0 QCR7 | CBP6 | CBP4 | BCS1 | QCR9 | FMP25 | FMP36 | CBP3 1-0-8 4.2E-02 mitochondrial respiratory chain supercomplex assembly Resp Enh 15.9 0.6 0.8 0.1 RCF1 | COX13 1-0-8 7.0E-02 mitochondrial outer membrane translocase complex Resp Enh 9.1 6.4 0.7 3.1 TOM22 | TOM5 | TOM6 | TOM70 | TOM7 | TOM40 NA NA doi: protein urmylation Resp Enh 7.4 2.6 1.1 0.9 ELP2 | URM1 | NCS2 | UBA4 | ELP6 | URE2 2-0.2-1 1.1E-03 https://doi.org/10.1101/517490 Elongator holoenzyme complex Resp Enh 8.9 3.6 0.0 0.9 TUP1 | IKI3 | ELP4 | ELP2 | ELP3 | IKI1 | ELP6 3-0.7.2-0 1.4E-04 NatC complex Resp Enh 14.9 1.7 -0.4 0.6 MAK31 | MAK10 | MAK3 2-0.8-1 5.6E-03 RFA2 | TOP3 | MUS81 | RMI1 | TOP1 | SGS1 | RFA1 | RAD4 | DNA topological change Resp Enh 7.9 5.7 0.7 2.6 2-0.8-0 2.6E-02 TOP2 tRNA (m1A) methyltransferase complex Resp Enh 17.0 0.8 9.3 17.4 GCD10 | GCD14 NA NA MUB1-RAD6-UBR2 ubiquitin ligase complex Resp Enh 12.9 3.1 0.9 0.5 RAD6 | MUB1 | UBR2 NA NA malonyl-CoA biosynthetic process Resp Enh 11.1 7.4 1.5 0.1 HFA1 | ACC1 NA NA pyridoxal 5'-phosphate salvage Resp Enh 11.1 8.7 1.5 5.3 PDX3 | BUD16 | BUD17 NA NA maintenance of transcriptional fidelity during DNA-templated Resp Enh 11.1 7.5 -0.4 4.2 RPB9 | DST1 NA NA transcription elongation from RNA polymerase II promoter RNA polymerase II transcription corepressor activity Resp Enh 11.0 7.6 2.2 1.7 SIN3 | MED8 | SRB7 NA NA pyruvate dehydrogenase activity Resp Enh 10.6 6.4 2.8 0.9 PDA1 | LPD1 | PDB1 NA NA

eukaryotic translation initiation factor 2 complex Resp Enh 10.3 4.7 8.2 8.7 SUI2 | GCD11 NA NA ; this versionpostedJanuary15,2019. a

L-aspartate:2-oxoglutarate aminotransferase activity Resp Sup -3.9 0.5 -0.9 0.8 AAT2 | AAT1 2-0.4-3 5.9E-04 CC-BY-NC 4.0Internationallicense nuclear pore outer ring Resp Sup -6.3 3.7 1.4 7.6 NUP145 | SEH1 | NUP84 | NUP120 | NUP133 3-0.4.1-0 9.7E-02 positive regulation of fatty acid beta-oxidation Resp Sup -2.6 0.5 -1.5 0.2 OAF1 | ADR1 | PIP2 2-0.3-1 2.1E-02 EKC/KEOPS complex Resp Sup -7.9 4.6 -1.8 1.1 KAE1 | CGI121 | GON7 | BUD32 NA NA spermine biosynthetic process Resp Sup -2.6 0.3 -0.3 0.7 SPE4 | SPE2 NA NA Dom34-Hbs1 complex Glyc Enh 0.3 2.1 2.7 0.3 HBS1 | DOM34 NA NA Ubp3-Bre5 deubiquitination complex Glyc Enh -1.1 3.2 8.8 2.2 BRE5 | UBP3 NA NA Cul4-RING E3 ubiquitin ligase complex Glyc Enh 1.8 4.1 4.6 2.3 HRT1 | PRP46 | SOF1 NA NA dTTP biosynthetic process Glyc Enh -1.3 3.2 7.0 0.6 CDC21 | CDC8 NA NA GDP-mannose transport Glyc Enh 1.5 1.9 9.5 5.6 VRG4 | HVG1 NA NA 7-methylguanosine cap hypermethylation Glyc Sup -2.2 1.5 -3.0 0.9 SWM2 | TGS1 2-0.4-0 5.6E-03 meiotic chromosome condensation Glyc Sup -0.8 0.9 -2.9 0.9 SMC2 | YCG1 | SMC4 | YCS4 2-0.4-2 3.4E-03 histone deubiquitination Glyc Sup 1.8 1.9 -3.4 1.1 SEM1 | UBP8 | SGF73 | SGF11 NA NA HDA1 complex Both Enh 8.9 0.3 4.0 1.1 HDA2 | HDA1 | HDA3 NA NA CTDK-1 complex Both Enh 15.6 0.7 3.8 0.9 CTK2 | CTK3 | CTK1 1-0-8 5.3E-02

Cul8-RING ubiquitin ligase complex Both Enh 9.1 4.5 6.1 1.1 MMS22 | MMS1 | RTT101 | HRT1 | RTT107 1-0-2 1.0E-01 . The copyrightholderforthispreprint(whichwasnot Lst4-Lst7 complex Both Enh 9.9 1.4 3.9 0.3 LST7 | LST4 2-0.2-1 3.1E-02 MCM complex Both Enh 4.2 1.4 4.9 2.6 MCM7 | MCM6 | MCM5 | MCM2 | MCM3 NA NA histone H3-K56 acetylation Both Enh 10.3 7.0 8.6 4.3 RTT109 | SPT10 NA NA fatty acid elongase activity Both Sup -12.2 2.4 -7.0 1.1 SUR4 | FEN1 2-0.4-1 2.9E-02 GARP complex Both Sup -6.8 0.9 -3.5 0.8 VPS53 | VPS54 | VPS52 | VPS51 3-0.4.1-0 6.9E-07 nuclear cap binding complex Both Sup -4.7 0.5 -3.4 0.5 STO1 | CBC2 3-0.4.1-0 9.9E-03 Rvs161p-Rvs167p complex Both Sup -9.0 0.4 -3.5 0.2 RVS167 | RVS161 3-0.4.1-0 9.9E-03 2007 The table headers are defined as follows: ‘GTA SD’ refers to the standard deviation of GTA; ‘REMc cluster’ refers to an REMc 2008 cluster ID if GTA-identified term was also found by REMc/GTF; ‘p-value’ reports results from REMc/GTF. See Table 1 for other 2009 header definitions.

GDSC gCSI hGene yGene DB Fig. GO term HLD L|K HLEG L|K Ref H Description hGene pval pval ACTL6B ARP4 Both 12E Ino80 Complex 8.3|-10 16.4|-12.6 3.3E-02 3.8E-02 175 2 actin like 6B HMGCS2 ERG13 Both S7B N/A 34.8|-21.4 3.7|-3.3 2.4E-02 7.9E-04 188 2 3-hydroxy-3-methylglutaryl-CoA synthase 2 176 protein phosphatase, Mg2+/Mn2+ dependent PPM1L PTC1 Both 12C N/A 15.2|-4.7 14.7|-13 3.1E-04 1.6E-02 2 -7 1L RPS6KB2 SCH9 Both 12E Sphingolipid Metabolic Process 6|-3.7 8.8|-9.8 3.5E-02 4.2E-03 NA 3 ribosomal protein S6 kinase B2 SEC11 homolog C, signal peptidase complex SEC11C SEC11 Both S7B N/A 11.6|-1.3 2.8|0 3.5E-04 3.5E-04 178 2 subunit 179 ADP ribosylation factor guanine nucleotide ARFGEF2 SEC7 Both 12C N/A 2.9|0 2.6|0.8 7.5E-03 1.5E-08 2 -80 exchange factor 2 IQSEC3 SEC7 Both 12C N/A 2.9|0 2.6|0.8 7.5E-03 4.9E-02 NA 2 IQ motif and Sec7 domain 3 PPCDC SIS2 Both 12C N/A 7.3|-3.5 12.1|-9.8 3.9E-02 4.7E-03 NA 2 phosphopantothenoylcysteine decarboxylase CCS CCS1 gCSI NA N/A 2.4|-0.4 5.6|-3.7 3.4E-01 1.2E-02 NA 2 copper chaperone for superoxide dismutase HMGCS1 ERG13 gCSI S7B N/A 34.8|-21.4 3.7|-3.3 9.5E-01 1.4E-02 187 3 3-hydroxy-3-methylglutaryl-CoA synthase 1 HDAC6 HDA1 gCSI 7A HDA1 Complex 5.2|-1 9.1|-1.8 9.1E-01 1.7E-03 NA 2 histone deacetylase 6 MUS81 structure-specific endonuclease MUS81 MUS81 gCSI 8B DNA Topological Change 5.2|-2.4 15.9|-11.1 6.9E-02 1.9E-04 181 2 subunit SGK2 SCH9 gCSI 12E Sphingolipid Metabolic Process 6|-3.7 8.8|-9.8 4.6E-01 8.5E-04 NA 1 SGK2, serine/threonine kinase 2 CCS SOD1 gCSI 12E N/A 6.2|-0.5 8.1|-10.7 3.4E-01 1.2E-02 NA 2 copper chaperone for superoxide dismutase SOD1 SOD1 GDSC 12E N/A 6.2|-0.5 8.1|-10.7 4.3E-02 7.9E-01 NA 2 superoxide dismutase 1 pelota mRNA surveillance and ribosome PELO DOM34 gCSI 9A Dom34-Hbs1 Complex 2.5|-0.7 -1.2|1.1 NA 1.7E-02 NA 2 rescue factor 185 alcohol dehydrogenase 1A (class I), alpha ADH1A SFA1 gCSI 12E N/A 4.8|0 0.9|-0.3 1.1E-01 2.9E-02 2 -6 polypeptide alcohol dehydrogenase 4 (class II), pi ADH4 SFA1 gCSI 12E N/A 4.8|0 0.9|-0.3 3.6E-01 3.6E-03 186 3 polypeptide ADH6 SFA1 gCSI 12E N/A 4.8|0 0.9|-0.3 8.6E-01 3.3E-03 185 1 alcohol dehydrogenase 6 (class V) HIST1H3D HHT1 Both 6A-B Nucleosome Assembly 0.4|-0.2 15|-10.2 4.2E-02 2.1E-02 NA 2 histone cluster 1 H3 family member d HIST1H2BN HTB1 Both 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 4.0E-02 3.0E-06 NA 2 histone cluster 1 H2B family member n HIST2H2BE HTB1 Both 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 4.0E-02 2.3E-08 NA 2 histone cluster 2 H2B family member e SETBP1 SET2 Both 6A,C Histone exchange 1.3|-1.6 5.4|-2.5 7.3E-07 3.0E-04 NA 3 SET binding protein 1 RNF40 BRE1 gCSI 7C Histone Monoubiquitination -0.7|0.5 6.5|-4.9 7.4E-01 8.5E-03 NA 2 ring finger protein 40 HIST1H4D HHF1 gCSI 6A-B Nucleosome Assembly -0.6|0.2 13.7|-3.8 NA 8.9E-03 NA 3 histone cluster 1 H4 family member d HIST1H4H HHF1 gCSI 6A-B Nucleosome Assembly -0.6|0.2 13.7|-3.8 8.6E-02 2.8E-06 NA 3 histone cluster 1 H4 family member h HIST1H4I HHF1 gCSI 6A-B Nucleosome Assembly -0.6|0.2 13.7|-3.8 NA 3.8E-02 NA 2 histone cluster 1 H4 family member i HIST1H4K HHF1 gCSI 6A-B Nucleosome Assembly -0.6|0.2 13.7|-3.8 NA 8.0E-03 NA 3 histone cluster 1 H4 family member k HIST2H4A HHF1 gCSI 6A-B Nucleosome Assembly -0.6|0.2 13.7|-3.8 NA 4.8E-02 NA 3 histone cluster 2 H4 family member a HIST2H4B HHF1 gCSI 6A-B Nucleosome Assembly -0.6|0.2 13.7|-3.8 NA 3.7E-03 NA 3 histone cluster 2 H4 family member b HIST4H4 HHF1 gCSI 6A-B Nucleosome Assembly -0.6|0.2 13.7|-3.8 5.4E-02 2.4E-02 NA 3 histone cluster 4 H4 HIST1H4D HHF2 gCSI 12E Chromatin Assembly or Disassembly -1.6|0.4 4.3|-0.1 NA 8.9E-03 NA 3 histone cluster 1 H4 family member d HIST1H4H HHF2 gCSI 12E Chromatin Assembly or Disassembly -1.6|0.4 4.3|-0.1 8.6E-02 2.8E-06 NA 3 histone cluster 1 H4 family member h HIST1H4I HHF2 gCSI 12E Chromatin Assembly or Disassembly -1.6|0.4 4.3|-0.1 NA 3.8E-02 NA 3 histone cluster 1 H4 family member i HIST1H4K HHF2 gCSI 12E Chromatin Assembly or Disassembly -1.6|0.4 4.3|-0.1 NA 8.0E-03 NA 3 histone cluster 1 H4 family member k HIST2H4A HHF2 gCSI 12E Chromatin Assembly or Disassembly -1.6|0.4 4.3|-0.1 NA 4.8E-02 NA 3 histone cluster 2 H4 family member a HIST2H4B HHF2 gCSI 12E Chromatin Assembly or Disassembly -1.6|0.4 4.3|-0.1 NA 3.7E-03 NA 3 histone cluster 2 H4 family member b HIST4H4 HHF2 gCSI 12E Chromatin Assembly or Disassembly -1.6|0.4 4.3|-0.1 5.4E-02 2.4E-02 NA 3 histone cluster 4 H4 HIST1H2AE HHT1 gCSI 6A-B Nucleosome Assembly 0.4|-0.2 15|-10.2 NA 1.1E-02 NA 3 histone cluster 1 H2A family member e HIST1H3E HHT1 gCSI 6A-B Nucleosome Assembly 0.4|-0.2 15|-10.2 NA 1.3E-02 NA 3 histone cluster 1 H3 family member e HIST1H3H HHT1 gCSI 6A-B Nucleosome Assembly 0.4|-0.2 15|-10.2 NA 6.8E-03 NA 3 histone cluster 1 H3 family member h HIST1H2AC HTA1 gCSI 12E Chromatin Assembly or Disassembly -3.5|0.8 13.5|-5.2 7.1E-01 2.9E-05 NA 3 histone cluster 1 H2A family member c HIST1H2AD HTA1 gCSI 12E Chromatin Assembly or Disassembly -3.5|0.8 13.5|-5.2 3.7E-01 5.8E-03 NA 3 histone cluster 1 H2A family member d HIST1H2AG HTA1 gCSI 12E Chromatin Assembly or Disassembly -3.5|0.8 13.5|-5.2 5.6E-01 1.4E-02 NA 3 histone cluster 1 H2A family member g HIST1H2AK HTA1 gCSI 12E Chromatin Assembly or Disassembly -3.5|0.8 13.5|-5.2 NA 6.3E-04 NA 3 histone cluster 1 H2A family member k HIST2H2AA3 HTA1 gCSI 12E Chromatin Assembly or Disassembly -3.5|0.8 13.5|-5.2 NA 2.6E-02 NA 3 histone cluster 2 H2A family member a3 HIST2H2AA4 HTA1 gCSI 12E Chromatin Assembly or Disassembly -3.5|0.8 13.5|-5.2 NA 5.3E-03 NA 3 histone cluster 2 H2A family member a4 H2BFM HTB1 gCSI 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 NA 3.0E-02 NA 3 H2B histone family member M H2BFWT HTB1 gCSI 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 3.3E-01 6.5E-04 NA 3 H2B histone family member W, testis specific HIST1H2BC HTB1 gCSI 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 9.8E-01 5.3E-05 NA 3 histone cluster 1 H2B family member c HIST1H2BD HTB1 gCSI 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 4.7E-01 3.0E-06 NA 3 histone cluster 1 H2B family member d HIST1H2BE HTB1 gCSI 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 NA 2.5E-04 NA 3 histone cluster 1 H2B family member e HIST1H2BF HTB1 gCSI 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 NA 5.3E-03 NA 3 histone cluster 1 H2B family member f HIST1H2BG HTB1 gCSI 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 NA 5.8E-04 NA 3 histone cluster 1 H2B family member g HIST1H2BJ HTB1 gCSI 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 9.2E-02 1.5E-03 NA 3 histone cluster 1 H2B family member j HIST1H2BK HTB1 gCSI 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 NA 9.0E-04 NA 3 histone cluster 1 H2B family member k HIST1H2BO HTB1 gCSI 6A-B Nucleosome Assembly 0.2|-0.6 7.8|-5.8 NA 2.9E-02 NA 3 histone cluster 1 H2B family member o N(alpha)-acetyltransferase 30, NatC catalytic NAA30 MAK3 gCSI 8A NatC Complex 0.2|-0.5 16.6|-11.6 8.5E-01 2.9E-02 184 2 subunit Protein import into mitochondrial 182 ROMO1 MGR2 gCSI S3C 0|-0.2 10.3|0.1 7.1E-02 4.1E-02 2 reactive oxygen species modulator 1 matrix -3 AIRE RCO1 gCSI 7A Rpd3S Complex 0.9|-0.5 7.9|-4.4 5.5E-01 1.3E-03 NA 3 autoimmune regulator ASH1L SET2 gCSI 6A,C Histone exchange 1.3|-1.6 5.4|-2.5 6.6E-01 5.5E-04 NA 3 ASH1 like histone lysine methyltransferase RECQL4 SGS1 gCSI 8B DNA Topological Change -0.2|0.7 6.1|-2.5 7.3E-01 3.2E-02 NA 3 RecQ like helicase 4 RECQL5 SGS1 gCSI 8B DNA Topological Change -0.2|0.7 6.1|-2.5 2.7E-01 3.0E-04 NA 1 RecQ like helicase 5 SRCAP SWR1 gCSI 7A Swr1 complex 0.4|-0.5 7.3|-6.2 NA 5.1E-04 NA 3 Snf2 related CREBBP activator protein Protein import into mitochondrial UNC45B TOM70 gCSI S3C 0.8|-0.4 12.4|-0.3 7.4E-01 1.6E-02 NA 2 unc-45 myosin chaperone B matrix MOCS3 UBA4 gCSI 8A protein urmylation 1.5|-3.3 8.1|-3.4 8.0E-01 3.0E-02 NA 1 molybdenum cofactor synthesis 3 EMC3 EMC3 GDSC 8A ER Membrane Protein Complex 1.5|-0.8 5.6|-1.8 1.1E-02 NA NA 2 ER membrane protein complex subunit 3 EMC4 EMC4 GDSC 8A ER Membrane Protein Complex -0.1|-0.3 6.2|-1.6 2.6E-02 NA NA 2 ER membrane protein complex subunit 4 2011 For column ‘DB’: ‘gCSI’, ‘GDSC’, or ‘Both’ indicate UES in the gCSI, GDSC, or both databases. Column ‘Fig.’ refers to 2012 specific figures. Columns “HLD L|K” and “HLEG L|K” contain the L and K interaction scores for HLD and HLEG media, 2013 respectively. “GDSC pval” and “gCSI pval” refer to the significance of differential gene expression in the respective 2014 databases. ‘Ref’ refers to relevant literature citations. ‘H’ refers to homology type: ‘1’, ‘2’, and ‘3’ indicate 1:1, 1:many, and 2015 many:many, respectively.

2016 Table 4. Yeast-human homologs with deletion suppression and OES across all tissues

GDSC gCSI hGene yGene DB Fig GO term HLD L|K HLEG L|K Ref H Description hGene pval pval ACTR2 ARP2 Both 12D Arp2/3 Protein Complex -3.7|1.4 -3.3|-6.9 3.2E-02 6.0E-05 190 1 ARP2 actin related protein 2 homolog SEPT6 CDC3 Both 12D N/A -2.1|0.7 -2.6|0.3 1.7E-04 2.8E-05 NA 1 septin 6 CSNK2A CKA2 Both 12D N/A -5.5|1 -4|1.2 4.3E-03 3.6E-03 NA 2 casein kinase 2 alpha 2 2 DBR1 DBR1 Both 12D N/A -2.1|0.6 -3.5|1 4.3E-02 9.8E-04 NA 1 debranching RNA lariats 1 PLAA DOA1 Both 12D N/A -2.2|0.7 -7.7|1.9 2.6E-02 1.5E-04 NA 3 phospholipase A2 activating protein EEF2 EFT2 Both 12D N/A -2.7|0.2 -2.1|1.1 1.9E-02 9.7E-06 191 2 eukaryotic translation elongation factor 2 HARS HTS1 Both 12D N/A -2.4|0.6 -2.9|0.5 4.1E-03 1.6E-03 NA 1 histidyl-tRNA synthetase 192- CDK7 KIN28 Both 12D N/A -2.2|1.1 -2.5|-1.2 2.4E-02 2.6E-04 3 cyclin dependent kinase 7 4 METAP1 MAP1 Both 12D N/A -4.7|3.3 -4.2|-0.6 8.9E-03 2.3E-02 NA 1 methionyl aminopeptidase 1 RPL13A RPL16B Both 12D N/A -4.5|3.7 -5.8|1.4 1.5E-03 9.5E-05 NA 2 ribosomal protein L13a RPL32 RPL32 Both 12D N/A -3.9|1 -11.3|1.1 6.9E-03 3.6E-03 198 2 ribosomal protein L32 195- RPL34 RPL34A Both 12D N/A -4.8|2.3 -7.2|2.4 1.5E-02 4.4E-03 3 ribosomal protein L34 7 ZFAND4 RPL40B Both 12D N/A -4.1|1.1 -5.7|1.1 3.7E-02 1.7E-02 NA 2 zinc finger AN1-type containing 4 199- RPS6 RPS6A Both 12D N/A -5.7|1.8 -6|2.6 2.0E-04 2.5E-07 2 ribosomal protein S6 200 heat shock protein family A (Hsp70) HSPA4 SSE1 Both 12D N/A -6.3|3 -13.7|4.4 1.5E-02 4.2E-07 NA 2 member 4 NCBP1 STO1 Both 12D N/A -3|1.7 -4.3|1.3 2.3E-03 3.5E-04 NA 2 nuclear cap binding protein subunit 1 ELAC2 TRZ1 Both 12D N/A -2.3|0.6 -2.6|0.1 1.1E-05 1.5E-08 NA 3 elaC ribonuclease Z 2 UBE2D1 UBC4 Both 12D N/A -4.6|2.2 -12.3|2.6 1.0E-02 8.1E-03 201 1 ubiquitin conjugating enzyme E2 D1 -2.2|- TPRKB CGI121 gCSI 12F EKC/KEOPS Complex -7.7|2.1 1.3E-01 7.6E-04 NA 1 TP53RK binding protein 0.8 ELOVL6 ELO2 gCSI 12F Fatty Acid Elongase Activity -7.7|1.4 -13.9|4.1 5.1E-01 2.7E-02 206 2 ELOVL fatty acid elongase 6 ELOVL6 ELO3 gCSI 12F Fatty Acid Elongase Activity -6.3|1.3 -10.5|1.9 5.1E-01 2.7E-02 206 1 ELOVL fatty acid elongase 6 Telomere tethering at the 216- NUP155 NUP170 gCSI 12F -3.7|0.6 -6.5|1.3 1.0E-01 4.4E-02 1 nucleoporin 155 nuclear periphery 18 SSRP1 POB3 gCSI 12F FACT Complex -4.1|1.2 -5.1|1.4 6.0E-02 2.2E-06 13 2 structure specific recognition protein 1 7-methylguanosine cap TGS1 TGS1 gCSI 12F -2.4|2.6 -3.3|0.7 8.5E-02 2.0E-03 NA 2 trimethylguanosine synthase 1 hypermethylation Cellular sphingolipid 203- VPS53 VPS53 gCSI 12F -2.4|1.8 -5.8|1.4 2.0E-01 2.4E-02 2 VPS53, GARP complex subunit homeostasis 5 USP22 UBP8 Both 12F histone deubiquitination -2|0.8 0.3|0.3 2.3E-02 1.2E-02 NA 1 ubiquitin specific peptidase 22 meiotic chromosome SMC2 SMC2 gCSI 12F -3.5|1.2 0.4|-0.9 1.1E-01 4.3E-02 138 3 structural maintenance of chromosomes 2 condensation meiotic chromosome NCAPG YCG1 gCSI 12F -2|0.8 -0.8|-0.6 7.9E-01 9.2E-06 138 3 non-SMC condensin I complex subunit G condensation meiotic chromosome NCAPD2 YCS4 gCSI 12F -2.4|0.8 -1.7|-0.9 2.3E-01 1.7E-03 NA 2 non-SMC condensin I complex subunit D2 condensation USP44 UBP8 GDSC 12F histone deubiquitination -2|0.8 0.3|0.3 4.1E-04 6.1E-01 NA 2 ubiquitin specific peptidase 44 207- KDM2B JHD1 Both 12F Histone Demethylation 0.2|-0.2 -2.3|1.9 4.6E-02 3.5E-02 1 lysine demethylase 2B 9 spermine biosynthetic AMD1 SPE2 Both 12F 0.2|-0.2 -2.8|0.5 1.7E-02 1.5E-04 202 1 adenosylmethionine decarboxylase 1 process spermine biosynthetic SMS SPE4 gCSI 12F -0.8|0.4 -2.4|1 NA 3.9E-02 NA 1 spermine synthase process eukaryotic translation initiation factor 3 EIF3I TIF34 gCSI 12F translation reinitiation 1.2|0 -3.9|1.4 8.2E-01 7.1E-05 NA 2 subunit I serine/threonine kinase receptor STRAP TIF34 gCSI 12F translation reinitiation 1.2|0 -3.9|1.4 6.7E-01 9.1E-03 NA 2 associated protein 211- density regulated re-initiation and release DENR TMA22 gCSI 12F translation reinitiation -1.1|0.6 -6.4|1.9 4.0E-01 1.9E-02 1 15 factor PHF2 JHD1 GDSC 12F Histone Demethylation 0.2|-0.2 -2.3|1.9 1.9E-03 6.8E-02 NA 1 PHD finger protein 2 jumonji and AT-rich interaction domain JARID2 JHD2 GDSC 12F Histone Demethylation -0.2|0.1 -3.2|1 1.9E-03 2.5E-02 210 2 containing 2 2017 See Table 3 for header descriptions.

No ~6000 YKO/KD strains, 768 ref strains drug

Q-HTCP on HLD or HLEG with 0, 2.5, 5, 7.5, or 15ug/mL doxorubicin 15 ug/ mL DOX Quantify gene interaction and mine profiles

Hrs: 20 24 30 40 24 48 72 96 C Image analysis Growth curve fitting RF) D vps54-∆0) No Drug Strain RF mms1-∆0) mms1-∆0 Hours: 40 80 vps54-∆0 RF) 5 ug/mL K vps54-∆0) DOX mms1-∆0)

Hours: 50 116 K/2 RF) 15 ug/mL vps54-∆0) DOX r mms1-∆0)

Hours: 60 124 ∆L Lref E Quantification of Interactions G GTA: GO module Warburg-dependence of assessment H doxorubicin-gene interaction Gene interaction

? ? doxorubicin

glycolysis

cell proliferation

respiration

F REMc/GTF: Gene interaction modules >> GO functional association Select gene interaction profiles REMc >> Phenomic modules >> GO enrichment Media- Media- HLEG- HLEG- HLD- HLD- z-score L > 2 Deletion Enhancers independent independent specific specific specific specific enhancers suppressors enhancers suppressors enhancers suppressors

z-score L z-scoreL z-score L < -2 Deletion Suppressors bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under Reference strain growth curveaCC-BY-NC distributions 4.0 International license. Doxorubicin-gene interaction A HLD B HLEG M HLD

C HLD D HLEG N HLEG

Ref strain CPPs E F G H O

y = 0.33x – 0.19 R2 = 0.16

NS_Enh G_Enh I J K L R_Sup R_Enh

G_ glycolytic NS_Sup R_ respiratory NS_ non-specific

G_Sup Enh enhancement Sup suppression

Comparison with prior published data Q Reproducibility P HLD S

R HLEG T bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

Select gene Classify / annotate clusters based on K and L interaction A interaction profiles C

Deletion Enhancers Four 2nd round clusters derived from 1-0-7, denoted 2-0.7-x; x= : 2: strong K and L interaction HLEG HLD 3: moderate K and L interaction 0: no K with strong L interaction 1: weak K and weak L interaction 2-0.7-2 2-0.7-3 2-0.7-0 U = 2802 2-0.7-1 Deletion Suppressors

HLEG HLD

B 1-0-7 D Interaction Interaction bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. A 1-0-8 2-0.8-1 2-0.8-2 2-0.8-0 Strong, HLEG-specific enhancement

1-0-7 2-0.7-2 2-0.7-3 2-0.7-0 2-0.7-1 Strong and intermediate strength, HLEG-specific Weak, media- Weak, HLD- enhancement independent specific enhancement suppression 1-0-1 2-0.1-7 2-0.1-4 2-0.1-2 2-0.1-3 2-0.1-1 2-0.1-6 2-0.1-5 2-0.1-0 Weak EG- specific enhancement

KEY: B 1-0-6 2-0.6-0 2-0.6-1 z-score Strong, media-independent enhancement

1-0-2 2-0.2-1 2-0.2-2 2-0.2-0 Mixed, Warburg- specific and non- specific enhancement

C 1-0-5 2-0.5-1 2-0.5-0

HLD-specific enhancement

1-0-3 2-0.3-5 2-0.3-1 2-0.3-4 2-0.3-3 2-0.3-2 2-0.3-0

Media dependent Weak, HLD- and independent specific suppression enhancement

2-0.4-3 2-0.4-0 2-0.4-4 1-0-4 2-0.4-1 2-0.4-2 Media- 2-0.4-5 independent Mixed interactions and HLD- specific suppression

1-0-0 2-0.0-0 2-0.0-3 2-0.0-2 2-0.0-1 E Respiratory deficiency; D- enhancers and mixed interactions bioRxiv preprint certified bypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailableunder Identified by GTA Identified by REMc HLD – Glycolysis doi: https://doi.org/10.1101/517490 ; this versionpostedJanuary15,2019. a CC-BY-NC 4.0Internationallicense HLEG –Respiraon . The copyrightholderforthispreprint(whichwasnot

Suppression Enhancement bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under Color Key chromatin organization aCC-BY-NC 4.0 International license.

Color Key Chromatin organization Nucleosomenucleosome organization DNA replication- −10 5 A Value 0 0 0 −7 0 0 1 12 HIR2 1 0 0 −7 −1 −1 1 11 PHO4 0 −1 0 −7 0 1 0 11 SWC5 0 0 −1 −7 1 0 −1 10 HIR3 0 −1 0 −8 1 2 1 12 SWD1 organization 0 −1 −1 −9 1 1 1 11 VPS72 2 0 1 −6 1 −1 2 12 PHO23 −2 0 0 −4 3 0 1 13 ELP4 independent nucleosome −2 0 0 −4 1 −1 −1 14 HHF1 0 1 0 −5 2 −4 1 14 HTA1 −10 5 Nucleosome 0 0 0 −6 −1 −1 −1 15 SWD3 0 0 0 −7 −1 0 −2 14 HIR1 Value Color Key 1 −1 0 −5 −1 2 0 11 HPC2 0 −1 −1 −5 −2 2 −2 11 LDB7 nucleosome assembly 0 −1 −1 −6 0 2 2 7 VPS71 1 −1 −1 −6 1 0 1 7 SWR1 0 0 0 −6 0 1 2 8 SWC3 0 −1 0 −4 1 1 −1 8 RCO1 0 0 0 −7 0 0 1 12 HIR2 0 −1 1 −6 2 0 −1 8 HTB1 −1 0 0 −3 2 0 0 6 LGE1 0 0 0 −3 1 0 0 6 SDC1 0 −1 −1 −9 1 1 1 11 0 1 0 −5 3 −1 2 6 BRE1 VPS72 1 0 0 −3 −1 0 −1 9 NHP10 0 0 −1 −4 1 −1 −1 8 RTT106 1 0 0 −3 −1 −1 −1 6 ISW1 0 −1 0 −7 0 1 0 11 SWC5 Color Key 0 0 −1 −3 −1 1 0 5 BRE2 DNA replication−independent nucleosome assembly 0 0 −1 −3 0 1 −1 4 YPL184C 1 −1 −2 −4 −1 1 1 5 UME1 assembly assembly −2 0 0 −7 0 1 1 3 YAF9 0 0 −1 −7 1 0 −1 10 HIR3 −10 5 0 −2 0 −9 1 4 −1 9 RSC1 Value 0 −1 0 −9 0 1 1 7 ARP6 −10 5 −1 −1 −1 −8 2 0 0 6 VPS72 Value 1 −1 0 −12 3 4 0 14 NPT1 −2 1 −1 −7 2 −3 0 10 IES3 −2 −1 −1 −13 1 2 4 16 HTZ1 −1 −3 −3 −13 3 1 4 16 RAD6 −2 0 0 −10 1 0 0 15 HHT1 0 0 0 −7 0 0 1 12 HIR2 −1 0 0 −12 −3 1 −1 15 −2 0 0 −10 1 0 0 15 YMR247C HHT1 0 0 0 −7 0 0 1 12 HIR2 −3 3 −1 −13 4 −3 5 11 NGG1 −3 1 −4 −13 4 −3 4 16 ADA2 −1 0 −1 −1 3 0 4 9 TOP1 −1 0 −2 0 6 −3 5 7 0 0 0 −7 −1 0 −2 14 HIR2 RSC2 HIR1 −1 −1 −4 −6 5 −1 6 9 ELP3 0 0 −4 −6 1 −1 3 8 RXT2 −1 −2 −5 −3 3 1 4 5 SET2 0 0 −1 −7 1 0 −1 10 HIR3 −4 1 0 −7 5 0 2 12 SPT21 1 −1 0 −5 −1 2 0 11 HPC2 −2 1 −2 −5 6 −2 2 9 CBF1 1 1 0 −7 2 −3 3 7 RTF1 0 0 −1 −7 1 0 −1 10 HIR3 −1 1 −1 −9 3 −3 1 8 IES2 −2 1 −1 −7 2 −3 0 10 IES3 0 −1 −1 −5 −2 2 −2 11 LDB7 −4 −1 −1 −6 11 1 5 5 DIA2 −4 3 −2 −7 11 −6 5 4 CTR9 HIR3 0 0 −1 −9 2 1 5 8 SAP30 1 −1 0 −5 −1 2 0 11 HPC2 −1 −1 −2 −12 2 1 7 8 DEP1 −2 0 0 −4 1 −1 −1 14 HHF1 −1 2 −3 −8 4 −5 8 3 UME6 −2 −4 −1 −5 9 9 2 9 RNT1 −2 −8 0 −1 5 8 3 5 GAS1 1 −1 0 −5 −1 2 0 11 HPC2 −3 −9 −2 −7 4 8 2 6 UAF30 0 −1 −1 −6 0 2 2 7 VPS71 1 −3 0 −12 3 12 2 11 CDC7 −7 −1 −9 −9 10 3 7 11 SPT7 0 0 0 −7 −1 0 −2 14 HIR1 −10 −4 −3 −7 11 6 3 5 SPT10 1 −1 −1 −6 1 0 1 7 SWR1 HPC2 −3 −9 0 −13 8 12 4 15 RTT109 Gene 1 −10 1 −13 5 8 2 16 ARP4 −1 −9 −1 −13 3 7 1 15 ARP8 −4 −8 −1 −13 10 5 7 16 IES6 0 0 0 −6 0 1 2 8 SWC3 1 −1 −3 −13 4 4 14 16 SIN3 0 0 0 −7 −1 0 −2 14 HIR1 −1 −18 1 −8 8 −2 15 9 ESA1 0 −6 0 −13 17 26 11 16 ARP5 −2 0 0 −4 1 −1 −1 14 HHF1 −1 −12 −2 −8 8 20 3 13 ASF1 0 −1 1 −6 2 0 −1 8 HTB1 0 −11 0 −13 2 19 1 16 RAD54 HIR1 −8 −2 −21 −13 15 10 21 16 SPT20 −9 −4 −23 −13 17 6 19 16 HFI1 −13 2 −13 −10 17 −3 10 16 BUR2 0 −1 0 −9 0 1 1 7 ARP6 −1 −15 −9 −13 8 2 14 16 CSE4 −1 −16 −15 −13 7 1 14 16 ORC3 0 0 −1 −4 1 −1 −1 8 RTT106 0 −18 −15 −13 7 2 10 16 RVB1 −2 0 0 −10 1 0 0 15 HHT1 −6 −4 −16 −13 10 2 11 16 SNF6 −1 −1 −1 −8 2 0 0 6 VPS72 −7 −2 −13 −13 9 1 8 16 HTL1 −7 −9 −13 −13 4 6 6 16 NPL6 −6 −9 −20 −13 5 −2 12 16 ORC1 −12 11 0 14 12 0 MEC3 0 −2 0 −9 1 4 −1 9 RSC1 RTT106 −14 −2 −15 −3 16 13 14 −3 CYC8 −4 1 0 6 2 0 HTB2 −5 0 0 7 1 0 RPH1 0 −1 1 −6 2 0 −1 8 HTB1

0 0 −7 0 3 −1 −12 −2 −8 8 20 3 13 −7 1 −6 −2 7 1 6 0 ARD1 −2 1 1 YAF9 ASF1 −4 −2 −12 −2 8 3 7 4 SFH1 −7 −6 0 9 3 0 HDA2 −9 −1 0 11 1 0 DOC1 −7 2 −13 −3 13 0 6 3 NET1 −4 1 0 6 2 0 HTB2 −2 1 −13 −7 9 −4 10 4 ARP7 −3 2 −12 −3 5 −3 8 1 CDC73 ASF1

0 0 −1 −4 1 −1 −1 8 Z_lm_L_Doxo_EG RTT106 Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD −4 1 −1 2 10 1 1 −7 NUP84 Z_Shift_K_Doxo_HLD 0 0 −3 −1 7 −1 6 −4 TAF14 −4 −2 −12 −2 8 3 7 4 SFH1 −7 4 −1 2 10 −3 2 −9 NUP133 −8 5 −1 2 12 −5 2 −9 NUP120 −2 4 −22 11 3 −4 7 −5 EAF7 −2 1 −13 −7 9 −4 10 4 ARP7 1 0 0 0 0 3 −2 4 POL30 1 −1 0 1 0 3 −1 3 SUM1

0 0 −1 0 0 3 −2 4 ORC2 −4 1 0 6 2 0 HTB2 1 0 1 0 2 2 −1 4 RSC8 1 0 1 −1 1 0 −1 7 IES1 −1 0 0 0 1 4 −1 3 TAF9 0 0 −1 0 2 4 0 6 MCM5 2 0 1 0 −1 3 0 3 MCM3 1 −1 1 0 0 4 −1 5 RAP1 0 0 −1 0 −1 1 −2 8 NHP6A 0 0 0 0 −1 2 0 5 EAF3 1 0 0 1 −1 2 −1 4 TAF10 0 0 1 0 4 3 −1 8 NOP1 −1 0 −1 1 2 2 1 −2 VPS75 0 0 0 1 3 2 −1 7 ASA1 1 0 0 −3 −1 0 −1 9 NHP10 2 0 3 2 1 2 −1 7 GTR1 −1 −1 −1 −2 0 5 0 9 HDA1 0 0 0 −1 −3 4 −1 9 HDA3

0 0 1 0 1 −1 0 3 HST4 0 0 −1 −4 1 −1 −1 8 RTT106 Gene 0 0 0 0 0 −2 −1 4 HHF2 −1 0 0 0 0 −1 0 3 BUD6 0 0 0 −1 0 −1 0 3 IOC2 1 −1 0 −1 −1 2 −1 1 NAP1 1 0 0 −3 −1 −1 −1 6 ISW1 DNA replication- 1 0 0 0 0 0 −1 2 CBF1 1 0 0 0 −1 0 −1 2 YKR017C 0 1 0 0 0 0 −1 2 HOS1 0 0 1 0 −1 −1 −1 2 SPT2 0 0 0 1 0 0 −1 1 DOT6 1 0 0 2 −3 0 −2 5 RSC4 0 0 0 1 −1 0 −2 1 YDR026C 0 0 −1 1 −1 0 −1 2 SCS22 0 0 −1 0 −1 −1 −2 3 YAP5 0 0 0 0 −1 1 0 0 HHO1 0 0 −1 0 0 −1 −1 2 ADA2 1 0 1 1 −1 4 −1 −3 GCR1 0 0 −1 0 0 0 0 2 HMT1 0 0 0 0 −1 0 −1 1 FUN19 0 0 −1 0 −1 0 −1 2 GIC1 −1 0 0 0 −1 0 0 1 XBP1 −1 0 −1 1 2 2 1 −2 VPS75 −1 0 0 1 1 −1 −1 2 RPT6

−1 1 −1 1 1 −1 −1 2 MRC1 1 0 0 1 −1 1 −2 1 FPR4 0 1 −1 0 1 −2 0 2 NAM7 1 0 1 0 2 2 −1 4 RSC8 0 0 −1 0 1 −1 0 1 CTI6 0 1 0 0 0 −2 −1 2 DPB4 dependent nucleosome 0 0 0 0 0 −2 0 2 HOS4 1 0 0 0 0 0 −1 1 YCR076C 1 −1 1 1 5 −1 −1 2 RVB2 0 0 0 0 0 −1 −1 1 HST2 1 0 0 0 0 −1 −1 1 HAT1 0 1 0 0 0 −1 −1 0 SPT23 0 0 0 1 −2 1 −2 1 FPR3 0 0 0 −1 1 0 1 3 SET3 −2 1 −1 1 2 −2 −1 0 RLF2 −1 1 0 0 2 −1 1 3 SIF2 0 −1 −1 −2 0 0 1 2 SPP1 −1 0 −2 0 3 1 1 2 SPT4 1 0 1 −1 −1 1 −1 3 IOC3 0 0 −1 1 −1 −3 −1 −1 MSI1 1 0 0 0 0 1 −1 2 ACS1 1 0 0 −1 0 0 −1 4 PHO2 0 0 0 0 −1 1 0 3 HST1 1 1 1 0 0 −3 0 −3 SPT6 0 0 0 0 −1 1 −1 2 PEP5 1 −1 0 −1 −1 2 −1 1 NAP1 0 0 0 0 −1 1 0 2 ZDS1 0 0 0 0 −1 0 0 2 SHG1 0 −1 −1 −1 −1 1 0 3 YER030W 0 0 −1 0 −2 1 −1 2 SNT1 0 0 0 0 −1 1 0 0 HHO1 1 0 1 0 0 0 1 1 YNG2 1 −1 0 1 −1 1 0 0 HST3 −2 1 −1 1 2 −2 −1 0 RLF2 assembly 0 0 0 0 −1 1 0 0 HHO1 1 0 0 0 −1 1 −1 0 WTM2 0 1 0 0 0 −2 −1 2 DPB4 1 −1 0 −1 1 1 0 1 PAT1 1 −1 0 −1 −1 2 −1 1 NAP1 −1 −1 0 −1 0 2 −2 1 ORC6 2 1 2 1 2 −1 1 2 ESC2 0 1 0 1 0 −2 −1 1 CAC2 Gene 1 −1 1 1 5 −1 −1 2 RVB2 0 0 1 0 1 −1 0 6 CPR1 0 1 0 1 0 −2 −1 1 CAC2 1 0 1 0 0 −1 0 6 RIS1 1 0 1 −1 1 0 −1 7 IES1 1 −1 0 0 −2 −1 0 1 ISW2 1 0 0 0 −1 0 −1 6 HHT2 0 0 −1 0 −1 1 −2 8 NHP6A 1 0 0 2 −1 0 −2 6 CDC48 0 0 1 0 −1 −1 −1 1 CHD1 1 0 0 2 −3 0 −2 5 RSC4 0 0 0 1 −3 0 −1 4 ITC1 Gene −1 −12 −2 −8 8 20 3 13 ASF1 2 0 1 1 −2 −2 −2 3 SWC7 0 0 −1 1 −1 −3 −1 −1 MSI1 −2 −2 0 1 −2 1 −1 4 RXT3 1 0 0 1 −1 1 −2 1 FPR4 1 −1 1 −1 5 4 4 0 BDF1 1 0 0 1 3 7 2 −3 LAS1 ASF1 −1 0 −1 1 2 2 1 −2 VPS75 0 −6 −2 −2 0 4 1 1 MAF1 0 0 0 1 −2 1 −2 1 FPR3 0 −1 0 1 0 13 −2 7 UTP5 1 0 0 1 1 9 −1 6 ACS2 2 −1 2 1 2 14 0 3 POL2 −1 −12 −2 −8 8 20 3 13 ASF1 0 0 −1 −4 1 −1 −1 8 RTT106 2 −2 0 1 −8 10 −1 2 ZDS2 0 0 0 1 −1 −1 −2 2 RAD26 −1 8 0 6 14 0 NUP145 −2 −3 0 8 12 0 RSC3 −1 2 −1 −10 5 −4 5 −9 GCN5 −1 0 −1 −11 0 −1 1 −1 0 0 0 0 RTT106 SGF29 GCR1 −6 6 0 2 −2 0 SNF5 −5 6 0 1 2 −2 −1 −2 SPN1 0 3 1 1 4 −9 −1 −2 LYS20 −1 −15 −9 −13 8 2 14 16 CSE4 0 1 0 −2 1 −4 1 5 0 0 0 0 TUP1 SPT3 −2 1 −1 1 2 −2 −1 0 RLF2 0 1 0 −1 1 −4 −1 4 SEM1

0 1 0 −4 2 −4 2 4 SPT8 Gene −1 1 −2 −2 3 −5 2 2 SGF73 −2 2 −2 −2 3 −4 0 1 SUS1 −1 0 0 1 −2 −1 −3 1 RTT102 RLF2 −3 −4 −2 0 3 −8 2 1 YTA7 0 0 −1 2 0 1 0 −7 DPB3 1 1 1 1 1 −1 0 −5 MCM10 1 −1 1 −1 5 4 4 0

BDF1 Z_lm_L_Doxo_EG 1 1 0 1 1 −1 0 −5 Z_lm_K_Doxo_EG

STH1 Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD 1 1 1 1 −1 −2 −1 −6 Z_Shift_K_Doxo_HLD ACS2 0 1 0 1 0 −2 −1 1 CAC2 1 0 1 1 0 −3 0 −7 ESC1 0 1 0 2 −1 −2 −1 −9 MLP1 1 1 0 1 3 −4 1 −7 NUP170 0 0 −3 −1 7 −1 6 −4 TAF14 −1 2 0 2 3 −4 0 −7 NUP60 −1 1 0 1 1 −4 −1 −5 NUP2 1 1 1 1 1 −3 0 −5 CAC2 SCS2 −1 0 −2 0 6 −3 5 7 1 1 1 1 1 −4 −1 −5 POB3 RSC2 0 0 −4 3 1 −1 3 0 RTG2 −1 −1 −4 3 1 −2 4 −1 HOS2 0 0 −1 1 −1 −3 −1 −1 MSI1 2 0 −3 2 −1 0 4 −2 YER051W −1 −2 −5 −3 3 1 4 5 SET2 1 1 2 0 4 −3 2 −1 CDC6 0 1 0 1 2 −2 0 −2 FPR1 3 0 2 1 −1 −1 1 −3 ADR1 1 0 0 1 −1 −1 2 −3 SIR4 −6 6 0 2 −2 0 SNF5 MSI1 −1 1 −2 −2 2 −3 3 −3 LEO1 1 1 1 −1 0 −2 1 −5 RSP5 1 1 0 −3 0 −2 1 −4 TAF12 1 0 −1 −2 2 −4 0 −1 YBR111W−A_1 −5 6 0 1 2 −2 −1 −2 SPN1 2 1 1 0 0 −3 0 0 SWI1 1 1 1 0 1 −4 1 −2 ORC5 1 1 1 0 0 −3 0 −3 SPT6 1 1 0 0 0 −2 0 −1 0 3 1 1 4 −9 −1 −2

SGV1 LYS20 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD 1 1 0 −1 −1 −2 0 −2 Z_lm_K_Doxo_HLD YCS4 Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD −1 1 −1 0 1 −3 2 0 NNT1 Z_Shift_K_Doxo_HLD −2 1 −1 1 2 −2 −1 0 RLF2 0 1 0 0 0 −2 −1 0 TOF2 1 1 0 1 3 −4 1 −7 NUP170 0 1 0 1 0 −2 −1 1 CAC2 0 0 0 1 1 −3 −1 0 MGA2 −1 1 −1 0 0 −3 −1 0 SGF11 0 0 −1 0 −1 −4 −1 1 SCP160 1 1 1 0 0 −3 0 −3 SPT6 0 1 1 1 0 −3 −1 −2 SAS4 0 1 0 2 0 −2 −1 −3 EAF6 0 0 −1 1 0 −3 −1 −2 HEK2 0 0 −1 1 −1 −3 −1 −1 MSI1 1 1 1 1 1 −4 −1 −5 POB3 0 1 −1 1 0 −2 −1 −2 SCS22 −1 1 −1 0 −1 −2 −1 −1 ASH1 1 0 1 1 −1 4 −1 −3 GCR1 1 0 1 1 −1 3 −2 −6 PAF1 1 1 1 1 0 0 0 −3 RSC6 2 −2 1 0 −6 3 −2 −2 MSN4 1 0 0 1 −3 −2 −3 −5 HSL7 1 −1 1 1 −2 0 −3 −2 GRX4 1 0 0 1 −2 0 −3 −3 YJR119C 1 1 0 1 1 −1 0 −5 STH1 −1 0 0 1 −2 0 −3 −3 SIR1 −2 0 −1 1 −2 0 −1 −4 MSH2 −1 1 0 1 −1 −1 −2 −2 APC9 1 0 1 0 −1 −1 −1 −2 FUN30 −1 0 0 1 −2 −1 −2 −2 HAT2 1 0 0 −2 1 0 1 −1 DPB2 1 1 1 0 −1 −2 0 0 UBP8 1 1 0 0 0 −2 1 0 MRC1 0 0 0 1 −1 0 −1 −3 SAS2 0 0 0 1 0 −1 1 0 YOR338W 1 0 0 1 −1 −1 0 −1 RPD3 0 0 0 1 1 −1 0 0 NAT4 1 0 1 −1 1 −1 0 0 CDC26 0 −6 0 −13 17 26 11 16 ARP5 0 0 −1 −1 0 0 0 0 ELF1 1 0 −1 0 −1 −1 0 0 YFL049W 0 0 −1 1 −1 1 −1 −1 HSL7 1 −1 0 1 −1 1 −1 −2 UTH1 −1 −12 −2 −8 8 20 3 13 ASF1 0 0 0 1 −1 −1 −1 −2 YKU70 1 0 0 0 0 −1 −1 −1 TEL1 1 0 1 0 −1 −1 −1 −2 FUN30 1 0 0 0 −1 −1 −1 −2 AHC2 −2 −3 0 8 12 0 RSC3 B 0 0 0 1 −1 0 −1 0 HOS3 1 1 0 0 0 −1 −1 0 ASF2 1 0 0 1 0 −1 −1 −1 FKH1 1 0 0 1 −1 −1 −1 −1 YKU80 −14 −2 −15 −3 16 13 14 −3 CYC8 0 1 0 1 −1 −1 0 −1 NTO1 0 0 0 1 −1 0 −1 −1 SET4 0 0 −1 1 −1 −1 −1 0 LRS4 0 0 0 0 −1 −1 −1 −1 YNG1 −1 −18 1 −8 8 −2 15 9 ESA1 0 0 −1 0 −1 −1 −1 −1 GIS1 1 −1 1 0 0 0 0 −2 ESS1 1 0 0 1 0 −1 0 −3 IRS4 2 1 1 0 0 −1 −1 −2 APC5 −1 −15 −9 −13 8 2 14 16 CSE4 1 1 1 1 0 0 0 −3 RSC6 1 0 1 1 0 −1 0 −3 FKH2 1 0 1 1 −1 0 −1 −2 SIR3 2 0 1 1 −2 −1 −1 −2 TTI1 0 −18 −15 −13 7 2 10 16 RVB1 1 1 1 1 −1 −2 −1 −2 SIR2 −1 1 0 1 −1 −1 −1 −3 OAF1 0 0 1 1 −1 −1 −1 −3 BDF2 −7 −2 −13 −13 9 1 8 16 HTL1 1 0 0 1 −1 −2 0 −3 AHC1 1 0 0 1 −2 0 −1 −3 YDR266C 1 0 0 1 −1 −1 −1 −3 SAS5 0 0 0 1 −1 0 −1 −3 SAS2 −7 −9 −13 −13 4 6 6 16 NPL6 2 0 3 1 −1 1 −1 1 IPL1 2 0 2 1 −1 0 −2 1 PUF4 2 0 2 1 −3 0 −1 0 GRX3 2 0 0 −2 0 0 QRI8 −7 −21 0 17 35 0 CKS1 0 0 0 0 TUP1 0 0 0 0 MCM7 0 0 0 0 ABF1 0 0 0 0 GCR1 −1 −19 0 8 29 0 CDC28 1 0 0 1 −1 1 −1 0 MSN2 1 0 0 1 −1 0 −1 0 VID21 1 0 1 0 −2 1 −2 0 WTM1 1 0 0 1 −1 1 −2 1 FPR4 −3 −18 −10 −8 8 31 14 8 SNF2 0 0 0 0 −1 0 −2 1 PAA1 0 0 0 1 −2 1 −2 1 FPR3 0 0 0 1 0 2 −3 −1 RNA1 −1 0 0 1 −2 3 −3 0 RRN5 −15 −21 0 3 35 0 RSC58 1 1 0 2 −2 1 −3 0 TOP2 1 −1 0 1 −3 2 −2 −1 SDS3 1 0 0 1 −3 −2 −3 0 YPL216W 1 0 0 1 −3 −1 −2 −1 MOD5 −1 0 0 1 −2 0 −2 −1 HPA3 −1 0 0 1 −3 −1 −1 −1 ESC8 0 0 0 1 −2 0 0 −1 SNT2 0 1 0 0 −2 −1 0 −1 STE20 0 0 0 0 −2 −1 −2 0 PNC1 −1 0 0 −1 −2 −1 −1 −1 CDC45 1 0 0 0 −1 0 −1 1 FOB1 Z_lm_L_Doxo_EG 1 −1 0 0 −2 −1 0 1 Z_lm_K_Doxo_EG ISW2 Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG 0 0 1 0 −1 −1 −1 1 Z_Shift_K_Doxo_EG CHD1 Z_Shift_L_Doxo_HLD 1 0 0 0 −1 −1 −2 0 SAS3 Z_Shift_K_Doxo_HLD 0 0 0 1 −2 −1 −2 0 SET5 −1 0 0 1 −2 −1 −3 1 RTT102 −1 0 0 1 −2 −2 −1 2 RIF1 −2 0 0 1 −2 −1 −2 2 DLS1 0 0 0 0 −2 −1 −2 2 HTA2 0 0 0 1 −1 −1 −2 2 RAD26 0 0 −1 0 −1 −1 −1 1 HPA2 0 0 0 0 −1 −1 −2 1 DOT1 0 0 0 1 −2 0 −2 1 UBX3 0 0 0 0 −2 −1 −2 1 IOC4 1 −14 0 −3 5 18 5 4 GLC7 −1 −16 0 4 7 0 TBF1 −14 −12 −15 6 5 16 14 −11 LCD1 −7 −21 0 17 35 0 CKS1 −7 −21 0 13 35 0 YRB2 −5 −21 0 8 35 0 EPL1 −15 −21 0 19 35 0 SWI3 −15 −21 0 3 35 0 RSC58 −1 −19 0 8 29 0 CDC28 −1 −18 −1 0 3 31 −2 3 REG1 −3 −18 −10 −8 8 31 14 8 SNF2 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD

ATP- dependent chromatin

Color Key chromatin remodeling Chromatin Color Key remodelingATP−dependent chromatin remodeling

−10 5 remodeling Value

−1 0 −2 0 6 −3 5 7 RSC2

−10 5 Value −1 −2 −5 −3 3 1 4 5 SET2

1 0 0 2 −3 0 −2 5 RSC4 0 −6 0 −13 17 26 11 16 ARP5

−1 −12 −2 −8 8 20 3 13 ASF1 1 0 0 −3 −1 −1 −1 6 ISW1

0 −11 0 −13 2 19 1 16 RAD54

−6 6 0 2 −2 0 SNF5

−3 −18 −10 −8 8 31 14 8 SNF2

1 −1 1 −1 5 4 4 0 BDF1 −15 −21 0 19 35 0 SWI3

−15 −21 0 3 35 0 RSC58 1 0 1 0 2 2 −1 4 RSC8

−1 0 −1 −1 3 0 4 9 TOP1

1 −1 1 1 5 −1 −1 2 RVB2 −1 0 −2 0 6 −3 5 7 RSC2

−1 −2 −5 −3 3 1 4 5 SET2 2 1 1 0 0 −3 0 0 SWI1

1 −1 1 −1 5 4 4 0 BDF1 0 1 0 0 0 −2 −1 2 DPB4

1 −1 1 1 5 −1 −1 2 RVB2

0 0 0 0 TUP1 −6 6 0 2 −2 0 SNF5

−1 2 −3 −8 4 −5 8 3 UME6 1 −1 0 0 −2 −1 0 1 ISW2

1 0 1 −1 1 0 −1 7 IES1

0 0 1 0 −1 −1 −1 1 CHD1

0 0 −1 0 −1 1 −2 8 NHP6A

1 1 1 1 0 0 0 −3 RSC6 1 0 0 −3 −1 0 −1 9 NHP10

0 0 −1 −4 1 −1 −1 8 RTT106 1 1 0 1 1 −1 0 −5 STH1

1 0 0 −3 −1 −1 −1 6 ISW1

1 0 1 0 −1 −1 −1 −2 FUN30

0 −1 −1 −5 −2 2 −2 11 LDB7

0 −1 −1 −6 0 2 2 7 VPS71 0 0 0 1 −1 0 −1 −3 SAS2

1 −1 −1 −6 1 0 1 7 SWR1 0 −1 −1 −6 0 2 2 7 VPS71 Gene 0 0 0 −6 0 1 2 8 SWC3

1 −1 −1 −6 1 0 1 7 SWR1 0 −1 0 −9 0 1 1 7 ARP6

−1 −1 −1 −8 2 0 0 6 VPS72 0 0 0 −6 0 1 2 8 SWC3

0 −2 0 −9 1 4 −1 9 RSC1

0 −1 0 −9 0 1 1 7 ARP6

1 0 0 −7 −1 −1 1 11 PHO4

−1 −1 −1 −8 2 0 0 6 VPS72 0 −1 0 −7 0 1 0 11 SWC5

0 −1 −1 −9 1 1 1 11 VPS72 0 −1 0 −7 0 1 0 11 SWC5

−2 1 −2 −5 6 −2 2 9 CBF1

0 −1 −1 −9 1 1 1 11 VPS72

−1 1 −1 −9 3 −3 1 8 IES2

0 −2 0 −9 1 4 −1 9 RSC1 −2 1 −1 −7 2 −3 0 10 IES3

−2 0 0 −7 0 1 1 3 YAF9 0 −1 −1 −5 −2 2 −2 11 LDB7

1 0 1 0 2 2 −1 4 RSC8

−2 0 0 −7 0 1 1 3 YAF9 0 0 0 0 −1 2 0 5 EAF3

1 0 1 −1 −1 1 −1 3 IOC3 −2 −3 0 8 12 0 RSC3

1 0 0 −1 0 0 −1 4 PHO2 −4 −2 −12 −2 8 3 7 4 SFH1

0 0 0 −1 0 −1 0 3 IOC2

−2 1 −13 −7 9 −4 10 4 ARP7 0 −1 −1 −1 −1 1 0 3 YER030W

0 0 −1 −3 0 1 −1 4 YPL184C −15 −21 0 19 35 0 SWI3

1 0 0 2 −3 0 −2 5 RSC4 −3 −18 −10 −8 8 31 14 8 SNF2 C 0 0 0 1 −3 0 −1 4 ITC1

−1 −12 −2 −8 8 20 3 13 ASF1 2 0 1 1 −2 −2 −2 3 SWC7

2 −2 1 0 −6 3 −2 −2 MSN4 0 −18 −15 −13 7 2 10 16 RVB1 Gene 2 1 1 0 0 −3 0 0 SWI1

−7 −9 −13 −13 4 6 6 16 NPL6 1 1 1 0 0 −3 0 −3 SPT6

1 1 1 1 0 0 0 −3 RSC6 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD 1 0 1 1 0 −1 0 −3 FKH2 Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD

1 1 0 1 1 −1 0 −5 STH1

0 0 0 1 −1 0 −1 −3 SAS2

1 0 1 1 −1 0 −1 −2 SIR3

0 0 1 1 −1 −1 −1 −3 BDF2

1 0 0 0 0 0 −1 2 CBF1

0 0 −1 0 0 −1 −1 2 ADA2

0 0 0 0 −1 0 −1 1 FUN19

−1 0 0 1 1 −1 −1 2 RPT6

0 1 0 0 0 −2 −1 2 DPB4

0 0 0 0 ABF1

0 0 0 0 TUP1

0 0 −1 −1 0 0 0 0 ELF1

0 0 0 1 0 −1 1 0 YOR338W

1 0 0 1 0 −1 −1 −1 FKH1

1 0 1 0 −1 −1 −1 −2 FUN30

1 0 0 1 −1 1 −1 0 MSN2

1 −1 0 0 −2 −1 0 1 ISW2

0 0 1 0 −1 −1 −1 1 CHD1

0 0 0 0 −2 −1 −2 1 IOC4

1 1 0 2 −2 1 −3 0 TOP2

−1 0 0 1 −2 −1 −3 1 RTT102

1 0 0 1 −3 −2 −3 0 YPL216W

−4 −2 −12 −2 8 3 7 4 SFH1

−2 1 −13 −7 9 −4 10 4 ARP7

0 0 −3 −1 7 −1 6 −4 TAF14

1 1 0 1 3 −4 1 −7 NUP170

−1 2 −1 −10 5 −4 5 −9 GCN5

−2 −3 0 8 12 0 RSC3

−14 −2 −15 −3 16 13 14 −3 CYC8 Histone

1 −10 1 −13 5 8 2 16 ARP4 Color Key −1 −9 −1 −13 3 7 1 15 ARP8 histone exchange

−4 −8 −1 −13 10 5 7 16 IES6

−2 −1 −1 −13 1 2 4 16 HTZ1 −3 1 −4 −13 4 −3 4 16 ADA2 exchange 0 −18 −15 −13 7 2 10 16 RVB1 −10 5 Value −6 −4 −16 −13 10 2 11 16 SNF6

−7 −2 −13 −13 9 1 8 16 HTL1

1 −1 1 −1 5 4 4 0 BDF1 −7 −9 −13 −13 4 6 6 16 NPL6 BDF1

1 −1 1 1 5 −1 −1 2 RVB2 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG RVB2 Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD −1 −2 −5 −3 3 1 4 5 SET2 SET2 0 0 0 0 TUP1 TUP1 0 0 1 0 −1 −1 −1 1 CHD1 CHD1 0 0 0 1 −1 0 −1 −3 SAS2 SAS2 0 −1 −1 −6 0 2 2 7 VPS71 VPS71 1 −1 −1 −6 1 0 1 7 SWR1 SWR1

0 0 0 −6 0 1 2 8 SWC3 SWC3Gene 0 −1 0 −9 0 1 1 7 ARP6 ARP6 −1 −1 −1 −8 2 0 0 6 VPS72 VPS72 0 −1 0 −7 0 1 0 11 SWC5 SWC5 0 −1 −1 −9 1 1 1 11 VPS72 VPS72 1 0 0 −3 −1 −1 −1 6 ISW1 ISW1 −2 0 0 −7 0 1 1 3 YAF9 YAF9 −1 −12 −2 −8 8 20 3 13 ASF1 ASF1 0 −18 −15 −13 7 2 10 16 RVB1 RVB1 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under A aCC-BY-NC 4.0 International license.

Color Key Rpd3L complex Rpd3L−10 5 Complex Value

Histone deacetylase 1 −1 −3 −13 4 4 14 16 SIN3 SIN3

2 0 1 −6 1 −1 2 12 PHO23 Color Key PHO23 histone deacetylase complex −10 5 complex 0 0 −4 −6 1 −1 3 8 RXT2 Value RXT2

−1 −1 −1 −2 0 5 0 9 HDA1 1 −1 −2 −4 −1 1 1 5 UME1 0 0 0 −1 −3 4 −1 9 HDA3 Sin3-type UME1 2 0 1 −6 1 −1 2 12 PHO23 0 0 −1 −9 2 1 5 8 SAP30 0 −1 0 −4 1 1 −1 8 RCO1 SAP30 1 −1 −2 −4 −1 1 1 5 UME1

0 0 −4 −6 1 −1 3 8 RXT2 Color Key −1 −1 −2 −12 2 1 7 8 DEP1 0 0 0 −1 1 0 1 3 SET3 complex Sin3−type complex DEP1 −1 1 0 0 2 −1 1 3 SIF2 −10 5 Gene 0 0 1 0 1 −1 0 6 Value CPR1 −1 2 −3 −8 4 −5 8 3 UME6 0 0 0 0 −1 2 0 5 EAF3 UME6

−1 0 0 0 3 0 2 0 STB1 1 −1 −3 −13 4 4 14 16 SIN3

0 0 0 1 0 0 −1 1 DOT6 −2 −2 0 1 −2 1 −1 4 RXT3 2 0 1 −6 1 −1 2 12 PHO23 0 1 0 0 0 0 −1 2 HOS1 RXT3

1 0 0 0 −1 0 0 1 ECM5 0 −1 0 −4 1 1 −1 8 RCO1 1 −1 0 1 −3 2 −2 −1 SDS3 1 0 0 1 −1 −1 −1 0 YBL054W SDS3 0 0 −1 0 1 −1 0 1 CTI6 1 −1 −2 −4 −1 1 1 5 UME1

0 0 0 0 0 −2 0 2 HOS4 0 0 −1 0 1 −1 0 1 0 0 −4 −6 1 −1 3 8 RXT2 CTI6 1 0 0 1 −1 −1 0 −1 RPD3 Gene CTI6 −1 1 −1 0 −1 −2 −1 −1 ASH1 0 0 −1 −9 2 1 5 8 SAP30 0 0 0 0 −1 1 0 3 HST1 1 0 0 1 −1 −1 0 −1 RPD3 0 0 −1 0 −1 0 0 3 TOS4 −1 −1 −2 −12 2 1 7 8 DEP1 RPD3 0 0 0 0 −1 2 −1 3 STB2 −1 2 −3 −8 4 −5 8 3 UME6 0 0 −1 0 −2 1 −1 2 SNT1 −1 1 −1 0 −1 −2 −1 −1 ASH1

−2 −2 0 1 −2 1 −1 4 RXT3 0 0 0 0 −1 2 0 5 EAF3 ASH1

−1 0 0 1 −2 2 −2 1 RFM1 Gene

0 0 0 0 −1 2 −1 3 1 −1 0 1 −3 2 −2 −1 SDS3 STB2

0 0 0 1 −2 0 0 −1 SNT2 −2 −2 0 1 −2 1 −1 4 RXT3 0 0 0 0 −3 0 −2 −1 STB6 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG

−1 −1 −4 3 1 −2 4 −1 Z_Shift_L_Doxo_HLD HOS2 −1 0 0 0 3 0 2 0 STB1 Z_Shift_K_Doxo_HLD

0 0 −1 −9 2 1 5 8 SAP30

0 0 −1 0 1 −1 0 1 CTI6 −1 −1 −2 −12 2 1 7 8 DEP1

−1 2 −3 −8 4 −5 8 3 UME6 1 0 0 1 −1 −1 0 −1 RPD3 −7 −6 0 9 3 0 HDA2 Rpd3S Complex 1 −1 −3 −13 4 4 14 16 SIN3 −1 1 −1 0 −1 −2 −1 −1 ASH1

Color Key

1 −1 0 1 −3 2 −2 −1 SDS3 Rpd3S complex −10 5 Value

0 0 0 0 −3 0 −2 −1 STB6 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG

Z_lm_L_Doxo_HLD 1 −1 −3 −13 4 4 14 16 Z_lm_K_Doxo_HLD SIN3 Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD SIN3 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD 0 0 0 0 −1 2 0 5 EAF3EAF3

0 −1 0 −4 1 1 −1 8 RCO1 RCO1Gene

1 −1 −2 −4 −1 1 1 5 UME1UME1

1 0 0 1 −1 −1 0 −1 RPD3RPD3 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD

Color Key HDA1 complex −10 5 HDA1Value Complex

−1 −1 −1 −2 0 5 0 9 HDA1 HDA1

0 0 0 −1 −3 4 −1 9 HDA3 HDA3Gene

−7 −6 0 9 3 0 HDA2 HDA2 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD B

Histone H4 Color Key Histone acetylationhistone acetylation acetylation −10 5 Value

Color Key histone H4 acetylation

−3 −9 0 −13 8 12 4 15 −10 5 RTT109 Value

1 −10 1 −13 5 8 2 16 ARP4 −1 −12 −2 −8 8 20 3 13 ASF1 −2 0 0 −4 3 0 1 13 ELP4ELP4 −1 −18 1 −8 8 −2 15 9 ESA1

0 −18 −15 −13 7 2 10 16 RVB1

−3 3 −1 −13 4 −3 5 11 NGG1 −8 −2 −21 −13 15 10 21 16 SPT20 NGG1 −9 −4 −23 −13 17 6 19 16 HFI1

0 −1 0 1 0 13 −2 7 UTP5

0 0 0 0 −1 2 0 5 EAF3 Gene 1 0 0 1 1 9 −1 6 ACS2 EAF3 0 0 0 0 −1 2 0 5 EAF3

1 0 0 1 −1 2 −1 4 TAF10 1 0 0 0 0 −1 −1 1 HAT1 −1 0 0 0 1 4 −1 3 TAF9 HAT1 1 0 1 0 0 0 1 1 YNG2

1 0 0 0 0 1 −1 2 ACS1 −1 −18 1 −8 8 −2 15 9 ESA1

1 −1 1 1 5 −1 −1 2 RVB2 ESA1

−1 0 −1 1 2 2 1 −2 VPS75 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD 0 1 0 −2 1 −4 1 5 SPT3

0 1 0 −4 2 −4 2 4 SPT8

−1 1 −2 −2 3 −5 2 2 SGF73

−1 0 0 1 −2 0 −2 −1 HPA3

−1 0 0 1 −2 −1 −2 −2 HAT2

0 0 0 1 0 −1 1 0 YOR338W

0 1 0 1 −1 −1 0 −1 NTO1

0 0 0 1 1 −1 0 0 NAT4

0 0 0 0 −1 −1 −1 −1 YNG1

1 0 0 0 −1 −1 −1 −2 AHC2

0 0 −1 0 0 −1 −1 2 ADA2 Histone H3

0 0 0 0 −1 0 −1 1 FUN19 Gene

0 0 −1 0 −1 −1 −1 1 HPA2

1 0 0 0 0 −1 −1 1 HAT1

0 0 1 0 −1 −1 −1 1 CHD1 Color Key acetylation 1 0 0 0 −1 −1 −2 0 SAS3 histone H3 acetylation 1 1 0 −3 0 −2 1 −4 TAF12

0 1 1 1 0 −3 −1 −2 SAS4 −10 5 Value 0 1 0 2 0 −2 −1 −3 EAF6

1 1 1 1 −1 −2 −1 −6 ACS2 1 0 0 1 −1 −2 0 −3 AHC1 −9 −4 −23 −13 17 6 19 16 HFI1HFI1 1 0 0 1 −1 −1 −1 −3 SAS5

0 0 0 1 −1 0 −1 −3 SAS2

−2 0 0 −4 3 0 1 13 ELP4 −2 0 0 −4 3 0 1 13 ELP4 ELP4 0 0 −1 −7 1 0 −1 10 HIR3

−1 −1 −4 −6 5 −1 6 9 ELP3 −3 3 −1 −13 4 −3 5 11 NGG1 −1 −2 −5 −3 3 1 4 5 SET2 NGG1 −3 3 −1 −13 4 −3 5 11 NGG1

−3 1 −4 −13 4 −3 4 16 ADA2 −3 −9 0 −13 8 12 4 15 RTT109 −7 −1 −9 −9 10 3 7 11 SPT7 RTT109 −10 −4 −3 −7 11 6 3 5 SPT10

0 0 −3 −1 7 −1 6 −4 TAF14 −10 −4 −3 −7 11 6 3 5 SPT10 −7 1 −6 −2 7 1 6 0 ARD1 SPT10 −1 2 −1 −10 5 −4 5 −9 GCN5

−1 0 −1 −11 0 −1 1 −1 SGF29 −1 0 0 0 1 4 −1 3 TAF9 Gene −2 4 −22 11 3 −4 7 −5 EAF7 TAF9

−5 −21 0 8 35 0 EPL1 −1 −2 −5 −3 3 1 4 5 SET2SET2

0 0 1 0 −1 −1 −1 1 CHD1 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG CHD1 Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD 1 1 0 −3 0 −2 1 −4 TAF12TAF12 −1 2 −1 −10 5 −4 5 −9 GCN5GCN5 −1 0 −1 −11 0 −1 1 −1 SGF29SGF29 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD

C Histone methylation by Set1/Compass Bre1/Rad6 H2B H2B-ub ub

H2B-ub Set1/COMPASS H2B-ub

Color H3K4Key H3K4-me me

−10 −5 0 5 10 Value

0 0 0 −6 −1 −1 −1 15 SWD3SWD3 0 −1 0 −8 1 2 1 12 SWD1SWD1 −1 −3 −3 −13 3 1 4 16 RAD6

0 1 0 −5 3 −1 2 6 BRE1 0 0 0 −3 1 0 0 6 SDC1SDC1 Jhd2 0 0 −1 −3 −1 1 0 5 BRE2 0 0 0 0 −1 0 0 2 SHG1SHG1 Gene Name 0 −1 −1 −2 0 0 1 2 SPP1SPP1 −5 0 0 7 1 0 RPH1 2 0 −3 2 −1 0 4 −2 JHD1JHD1 0 0 −1 0 −1 −1 −1 −1 GIS1 1 0 0 1 −2 0 −3 −3 JHD2JHD2 DOXO.HLD.L.SHIFT DOXO.HLD.K.SHIFT DOXO.HLEG.L.SHIFT DOXO.HLEG.K.SHIFT DOXO.HLD.L.INTERACTION DOXO.HLD.K.INTERACTION Type of Media DOXO.HLEG.L.INTERACTION DOXO.HLEG.K.INTERACTION ColorbioRxiv Key preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under −10 −5 0 5 10 Value aCC-BY-NC 4.0 International license.

0 −2 −1 −4 5 3 2 10 NCS6_2NCS6_2 A −2 −2 −2 −3 3 3 0 10 URM1URM1 B 0 0 −1 0 3 1 3 10 RMI1 −1 −3 −2 −3 3 1 4 8 RMI1 UBA4UBA4 −1 −2 −3 −5 3 1 4 9 ELP2ELP2 −1 0 −1 −1 3 0 4 9 TOP1 TOP1 −1 −1 −1 −1 4 1 1 10 NCS6_1 NCS6_1 −1 1 0 −2 2 0 1 6 SGS1 SGS1 −2 0 −1 −2 2 0 2 10 NCS2NCS2 0 1 −3 −10 1 −4 4 7 RFA1 RFA1 −3 −1 −2 −3 5 1 2 5 ELP6ELP6 0 −2 0 −12 0 6 −1 17 MMS4 0 −2 1 −4 3 1 1 3 URE2URE2 MMS4 0 0 0 −2 1 0 0 6 EMC4EMC4 0 −2 0 −11 0 5 −1 16 MUS81MUS81 Gene 0 −1 −1 −2 −1 1 −1 8 EMC5EMC5 0 −6 0 −13 0 6 −1 15 MMS4 MMS4 0 −1 0 −2 0 1 −2 6 EMC3EMC3Gene Name −4 −6 −7 −12 8 3 7 14 TOP3 0 −1 0 −3 −1 2 0 5 EMC6EMC6 TOP3 1 0 1 0 0 1 −2 2 EMC2EMC2 0 0 0 0 RFA2 RFA2 1 −2 −2 0 0 4 5 −2 EMC1 EMC1 0 0 1 0 −1 0 −1 1 RAD4 RAD4 1 0 1 −9 0 −1 2 13 MAK10MAK10 1 1 0 2 −2 1 −3 0 TOP2 TOP2 0 0 0 −11 −1 0 2 15 MAK31MAK31 −1 0 −3 −12 0 0 3 17 MAK3MAK3 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD DOXO HLD L DOXO DOXO HLD K DOXO DOXO HLEG L DOXO DOXO HLEG K DOXO

Type of Media

Color Key

−10 −5 0 5 10 Value

0 −2 −1 −4 2 7 0 15 BUD16BUD16 C 2 0 2 −2 −3 1 −1 12 UBR2 UBR2 D 0 −1 0 −13 −1 3 1 16 PDA1 PDA1 −1 −3 −3 −13 3 1 4 16 RAD6 RAD6 0 0 −2 −11 1 2 5 12 LPD1 LPD1 1 −1 0 −11 0 0 0 11 MUB1 MUB1 −1 −5 0 −4 8 14 1 14 SUI2 SUI2 1 −1 −3 −13 4 4 14 16 SIN3 SIN3 −4 −4 −9 −13 3 2 10 16 HFA1 HFA1 −3 0 0 −16 10 −3 8 17 PDX3 PDX3 1 0 1 2 1 1 −1 6 ACC1 ACC1 1 0 0 1 1 1 −2 6 MED8 MED8 Gene Name 0 0 0 −2 2 −3 1 6 DST1 DST1 2 0 1 −4 1 4 4 4 PDB1 PDB1 1 0 0 −5 2 2 0 7 GCD11GCD11 Color Key −1 0 0 3 1 0 SRB7 SRB7 1 0 0 0 0 0 0 1 BUD17BUD17 2 1 16 −15 0 −3 12 18 GCD14 GCD14 −10 −5 0 5 10 −8 3 −2 −13 28 22 10 16 GCD10GCD10 Value −13 2 −16 −11 17 3 21 16 RPB9 RPB9 0 −21 −1 −13 8 35 1 16 TIF35TIF35

−8 4 −10 −2 18 −3 21 −11 GON7GON7

−14 6 −16 7 15 −1 15 −11 BUD32BUD32 DOXO HL L DOXO DOXO HL K DOXO −2 −1 −1 2 8 −2 1 −8 CGI121CGI21 DOXO HLEG L DOXO DOXO HLEG K DOXO 3 0 2 1 −1 −1 1 −3 ADR1ADR1

1 1 1 0 0 −1 0 −1 KAE1KAE1

1 0 0 1 −1 −2 −1 −2 PIP2PIP2 Type of Media 1 1 0 −1 −1 −2 −1 −1 RPG1RPG1

0 0 0 0 0 0 0 −3 SPE2SPE2Gene Name 0 0 −1 1 0 −1 −1 −2 SPE4SPE4 −1 1 0 1 −1 −1 −1 −3 OAF1OAF1 −2 1 −1 2 2 −1 −2 −6 TMA22TMA22 1 0 0 1 −2 1 −3 −4 TIF34TIF34 1 0 0 1 −2 0 −1 −6 TMA20TMA20 DOXO HL L DOXO DOXO HL K DOXO DOXO HLEG L DOXO DOXO HLEG K DOXO

Type of Media bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. A B

Color Key

−10 −5 0 5 10 Value

1 −8 1 −1 −1 −4 0 −1 SMC4 SMC4 Color Key −4 3 −1 1 6 −2 2 −3 TGS1 TGS1 0 1 0 −1 1 −4 −1 4 SEM1 SEM1 −10 −5 0 5 10 Value 1 1 1 0 −1 −2 0 0 UBP8 UBP8

1 1 1 −1 1 −3 0 0 SMC2 SMC2 −1 −4 2 −1 3 7 0 1 CDC21CDC21 −1 1 −1 0 0 −3 −1 0 SGF11SGF11 1 −4 −2 −1 3 7 2 1 BRE5BRE5 1 1 0 −1 0 −2 0 −1 YCG1 YCG1 −1 −1 −1 1 5 6 0 0 VRG4 VRG4 Gene Name 1 1 0 −1 −1 −2 0 −2 YCS4 YCS4 −2 −2 1 1 2 7 0 −4 CDC8CDC8 −1 2 −1 0 4 −4 1 −1 SWM2SWM2 2 0 1 1 −1 2 −1 2 PRP46 PRP46 −1 1 −2 −2 3 −5 2 2 SGF73SGF73

0 −1 −1 0 0 3 0 2 HBS1 HBS1 −2 2 −2 −2 3 −4 0 1 YBR111WSUS1_2−A_2

0 −1 −1 1 −1 2 1 −1 DOM34 DOM34Gene Name 1 0 −1 −2 2 −4 0 −1 YBR111WSUS1_1−A_1 0 0 0 −2 1 7 1 6 HRT1HRT1

0 −6 0 0 4 10 3 −3 UBP3UBP3 DOXO HL L DOXO DOXO HL K DOXO 1 −8 1 1 2 5 2 −2 SOF1 DOXO HLEG L DOXO SOF1 HLEG K DOXO

1 −2 0 −1 −11 13 1 3 HVG1HVG1

Type of Media DOXO.HL.L.SHIFT DOXO.HL.K.SHIFT DOXO.HLEG.L.SHIFT DOXO.HLEG.K.SHIFT DOXO.HL.L.INTERACTION DOXO.HL.K.INTERACTION Type of Media DOXO.HLEG.L.INTERACTION DOXO.HLEG.K.INTERACTION bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

A ds break repair via synthesis-dependent strand ds-break repair via annealing −1 −21 0 −13 4 35 1 16 MRE11MRE11 Color Key homologous double−strand break repair via homologous recombination −4 −21 0 −12 5 35 2 15 RAD52RAD52 0 −11 0 −13 2 19 1 16 RAD54RAD54 −10 5 recombination Value −1 −12 −1 −12 0 17 0 15 RAD55RAD55

−1 −21 0 −13 4 35 1 16 MRE11 0 −17 0 −13 2 18 0 16 RAD51 0 −21 0 −13 2 35 0 16 RAD50 RAD51

−4 −21 0 −12 5 35 2 15 RAD52 −1 −15 −1 −13 4 15 1 16 RAD57 0 −11 0 −13 2 19 1 16 RAD54 RAD57 −1 −12 −1 −12 0 17 0 15 RAD55 0 −17 0 −13 2 18 0 16 RAD51 2 0 2 0 0 3 0 2 SAE2SAE2 −1 −15 −1 −13 4 15 1 16 RAD57

0 35 0 −12 −21 21 MCD1 3 0 3 1 0 −2 0 2 YLR247C Gene −1 −19 0 8 29 0 CDC28 YLR247C

2 0 1 0 −1 3 0 3 MCM3 0 0 0 0 0 −1 −1 1 MEI5 2 0 2 0 0 3 0 2 SAE2 MEI5 0 0 −1 0 2 4 0 6 MCM5 −2 −1 0 3 1 0 SHU2 −1 0 −1 0 0 −2 0 1 PSY3PSY3 −1 −3 0 −1 4 8 2 4 MCM2

−1 1 0 −2 2 0 1 6 SGS1 −1 1 −1 1 −1 0 −1 0 CSM2 0 1 0 −5 3 −1 2 6 CSM2 BRE1

0 0 0 −4 0 1 −1 10 HPR5 0 0 1 0 −2 −1 −1 −1 SHU1 0 0 −1 −2 −2 −1 −1 7 PPH3 SHU1 0 1 −3 −10 1 −4 4 7 RFA1 −1 0 −2 0 6 −3 5 7 RSC2 1 0 0 1 −3 −1 −2 −2 DMC1DMC1 −6 6 0 2 −2 0 SNF5

3 0 3 1 −1 0 −1 2 SMC5 −2 −1 0 3 1 0 SHU2 3 0 3 1 0 −2 0 2 YLR247C SHU2 2 1 2 1 2 −1 1 2 ESC2

2 1 1 4 0 −2 0 4 NSE1

1 0 0 1 0 1 −1 4 PSF1

0 0 0 0 −1 0 −1 3 PIF1 −1 0 1 0 −2 0 −3 2 TAH11 ds-break repair via 0 1 0 −1 1 −4 −1 4 SEM1 Z_lm_L_Doxo_EG 0 0 0 0 0 −1 −1 1 Z_lm_K_Doxo_EG MEI5 Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Gene Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD −1 0 −1 0 0 −2 0 1 PSY3 Z_Shift_K_Doxo_HLD 3 0 3 0 −1 −3 0 −1 DPB11 break-induced 1 0 0 1 −3 −1 −2 −2 DMC1

−1 0 0 −1 −2 −1 −1 −1 CDC45

1 0 1 1 −1 2 −1 −1 SMC3 1 0 1 1 0 −1 −1 −1 SCC2 replication 1 0 0 1 −1 −1 −1 −1 YKU80

0 0 1 0 −2 −1 −1 −1 SHU1 −1 −21 0 −13 4 35 1 16 MRE11 0 0 0 1 −1 −1 −1 −2 YKU70 MRE11

0 0 0 0 MCM6 0 −21 0 −13 2 35 0 16 RAD50 0 0 0 0 MCM7 RAD50

0 0 0 0 SLD2 −4 −21 0 −12 5 35 2 15 RAD52 0 0 0 0 RFA2 RAD52 1 0 0 0 −1 0 −2 −1 CTF18 1 −3 0 −12 3 12 2 11 CDC7CDC7 −1 1 −1 1 −1 0 −1 0 CSM2 0 0 −1 1 0 −1 0 −1 RAD1 0 −2 0 −11 0 5 −1 16 MUS81MUS81 0 0 0 1 0 −1 −1 −1 POL32 −2 0 −1 2 −3 0 2 −6 SLX4 −3 −10 −1 −9 7 7 1 10 CTF4CTF4 1 1 1 1 1 −1 0 −5 MCM10 1 1 0 1 0 −2 0 −3 PSF2 −3 −13 −7 −11 12 6 12 13 PSF3PSF3 0 1 0 1 0 −3 −2 −5 ELG1 −12 11 0 14 12 0 MEC3 0 0 −1 0 2 4 0 6 MCM5MCM5 1 −3 0 −12 3 12 2 11 CDC7 −3 −10 −1 −9 7 7 1 10 CTF4 2 0 1 0 −1 3 0 3 MCM3MCM3 0 −2 0 −11 0 5 −1 16 MUS81 −1 −3 −3 −13 3 1 4 16 RAD6 −1 −3 0 −1 4 8 2 4 MCM2MCM2 −3 −13 −7 −11 12 6 12 13 PSF3 −10 −5 −3 −12 12 6 4 14 MMS22 1 0 0 1 0 1 −1 4 PSF1PSF1 0 0 0 0 −1 0 −1 3 PIF1 PIF1 −1 0 1 0 −2 0 −3 2 TAH11TAH11Gene Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD 0 0 0 1 0 −1 −1 −1 POL32POL32 0 0 0 1 −1 −1 −1 −2 YKU70YKU70 0 0 0 0 MCM6MCM6 0 0 0 0 MCM7MCM7 0 0 0 0 SLD2SLD2 −1 0 0 −1 −2 −1 −1 −1 CDC45CDC45 3 0 3 0 −1 −3 0 −1 DPB11DBP11 1 1 1 1 1 −1 0 −5 MCM10MCM10 1 1 0 1 0 −2 0 −3 PSF2PSF2 −2 0 −1 2 −3 0 2 −6 SLX4SLX4

Color Key Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD B Z_Shift_K_Doxo_HLD −10 −5 0 5 10 Value

0 0 0 −2 1 7 1 6 HRT1HRT1 0 0 −1 0 2 4 0 6 MCM5MCM5 2 0 1 0 −1 3 0 3 MCM3MCM3 −1 −3 0 −1 4 8 2 4 MCM2MCM2 0 −2 −1 0 0 7 2 3 RTT107RTT107 0 0 −1 −4 −1 6 0 12 MMS1MMS1 −1 0 −1 −3 1 4 −1 10 RTT101RTT101 0 −3 0 −3 0 4 −2 9 LST7LST7 1 0 0 −3 −1 0 −1 9 NHP10NHP10 0 0 0 −4 1 0 −1 9 IES5IES5 1 0 1 −1 1 0 −1 7 IES1IES1 −2 −2 0 −11 1 4 −1 11 LST4LST4 −1 1 −1 −9 3 −3 1 8 IES2IES2 −2 1 −1 −7 2 −3 0 10 IES3IES3 1 −1 1 1 5 −1 −1 2 RVB2RVB2Gene Name 0 1 0 −1 0 −2 −1 0 IES4IES4 0 0 −3 −1 7 −1 6 −4 TAF14TAF14 −2 1 −13 −7 9 −4 10 4 ARP7ARP7 0 −6 0 −13 17 26 11 16 ARP5ARP5 −4 −8 −1 −13 10 5 7 16 IES6IES6 −10 −5 −3 −12 12 6 4 14 MMS22MMS22 1 −10 1 −13 5 8 2 16 ARP4ARP4 −1 −9 −1 −13 3 7 1 15 ARP8ARP8 0 −18 −15 −13 7 2 10 16 RVB1RVB1 DOXO HL L DOXO DOXO HL K DOXO DOXO HLEG L DOXO DOXO HLEG K DOXO

Telomere tethering at Actin cortical Color Key Cellular sphingolipid Sphingolipidsphingolipid metabolic process nuclear periphery patch localization Lipid homeostasis −10 5 metabolic process −1 8 0 6 14 0 NUP145NUP145 −2 0 −1 1 4 −1 1 −3 CDC50CDC50 homeostasis Value −8 −4 −6 −13 7 0 5 17 VPS66VPS66 −4 1 −1 2 10 1 1 −7 NUP84 NUP84 1 0 0 0 −1 0 −2 3 LAS17LAS17 1 −1 −1 0 1 2 −1 6 MEH1 −6 −18 0 7 24 0 PHS1 MEH1 PHS1 −7 4 −1 2 10 −3 2 −9 NUP133NUP133 1 0 0 −1 −1 0 −1 2 YBL060WYEL1 −1 1 0 −1 2 −3 −1 7 APQ12 0 −1 0 8 11 0 TSC10 APQ12 −1 1 −1 0 0 −1 −1 3 ORM1 TSC10 ORM1 −8 5 −1 2 12 −5 2 −9 NUP120 1 1 0 1 0 0 −1 0 YSC84 −2 −1 −1 −3 4 1 2 3 SPF1SPF1 0 −4 −1 −10 4 6 3 9 SCH9 NUP120 YSC84 −1 −1 0 −6 −2 2 −2 12 NPR1 −4 −2 0 4 2 0 1 0 1 0 1 −2 0 1 1 0 1 0 0 0 −1 0 MYO3 GEM1 −1 −5 −1 1 1 1 3 −3 NPR1 SRC1 GEM1 ORM2ORM2 SRC1 MYO3 −2 1 −2 −5 6 −2 2 9 CBF1 1 0 0 1 0 0 0 −5 BRL1 CBF1 BRL1 1 0 0 1 −1 −1 2 −3 SIR4 1 0 1 1 −2 0 0 −1 LSB5 1 1 0 7 4 0 KEI1 SIR4 −1 −5 −1 1 1 1 3 −3 KEI1 LSB5 ORM2 0 1 −2 3 2 −4 1 −8 ORM2 VPS51 VPS51 −4 4 −1 0 5 −1 5 −1 ARV1 2 0 1 1 0 2 −2 −2 NUP116 0 0 0 1 −1 −1 −1 0 ARF3 1 0 0 0 0 −1 0 2 YLR253WYLR253W ARV1 NUP116 ARF3 −4 −3 0 5 0 0

Gene LCB5 LCB5 1 0 0 1 −1 −1 −1 −3 MYO5 0 0 0 0 0 −1 −1 2 YPR004C 0 2 0 1 1 −2 1 −6 VPS53 1 0 1 1 −1 0 −1 −2 SIR3 YPR004C VPS52 −1 −5 −1 1 1 1 3 −3 ORM2 SIR3 MYO5 ORM2 Gene −1 1 −1 0 0 −1 −1 3 ORM1 ORM1 1 0 0 0 −1 0 0 2 TOR1 1 1 1 1 −1 −2 −1 −2 −2 0 0 1 −2 −1 −2 −1 YOR129C TOR1 SIR2 SIR2 YOR129C 1 0 0 0 −1 0 −2 1 OSH6 0 2 −1 2 1 −3 1 −7 VPS52 OSH6 VPS53 1 0 0 0 −1 0 0 1 YOR365C YOR365C 0 0 −1 1 −1 −2 0 −1 MLP2 0 0 −1 1 −2 −1 −2 −1 CRN1 0 0 0 0 −1 0 −1 1 MLP2Gene CRN1 DGA1DGA1 1 0 0 0 0 0 −1 2 CBF1 0 1 0 0 −3 −1 −2 −2 BSP1 2 0 2 1 −2 −2 0 −1 YOR059C −1 1 −1 2 2 −3 0 −6 VPS54 1 0 0 0 −1 0 −1 3 HUF2 0 0 0 0 −1 0 −1 1 SRC1 BSP1 YOR059CGene VPS54 HUF2 SRC1 1 0 0 1 −2 0 0 −1 1 −1 0 0 −1 1 0 4 KES1 YHR209W KES1 0 1 −1 2 2 −4 4 −13 VRP1 YHR209W 0 1 0 1 −1 −1 −2 0 YDR458CYDR458C VRP1 0 0 −1 0 −1 −1 1 0 POX1POX1 1 0 1 1 0 0 −1 4 LCB2 0 −1 0 2 −1 −3 −1 −7 LSB3 −1 1 −1 0 0 −1 −1 3 0 1 0 1 −3 0 −2 0 NDJ1 1 0 0 0 −1 0 0 0 FUN14 ORM1 LSB3

Z_lm_L_Doxo_EG NDJ1 FUN14 Z_lm_K_Doxo_EG

Z_lm_L_Doxo_HLD ORM1 Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD 0 0 −1 −1 1 −3 0 5 SWE1 1 0 0 0 −1 0 0 −1 YOR228CYOR228C SWE1 0 0 0 0 −3 −1 −2 −1 NUP100NUP100 1 1 −1 2 0 −3 2 −9 RVS161RVS161 1 0 0 1 −2 −3 −2 3 HUF1 0 0 −1 1 −1 −1 −1 1 YBR204CYBR204C HUF1 0 1 −1 2 −1 −4 1 −9 1 1 0 1 3 −4 1 −7 NUP170 RVS167 1 1 1 5 1 −4 2 3 LCB3 NUP170 RVS167 0 0 −1 1 −2 2 −2 −1 PHM8PHM8 0 0 0 1 −1 3 −1 −2 SCS7 −1 2 0 2 3 −4 0 −7 NUP60 −1 1 0 0 −4 −1 −2 0 YNR070W SCS7 NUP60 YNR070W 1 0 1 1 −1 −1 −2 0 CSH1 0 1 −2 3 2 −4 1 −8 VPS51 CSH1 1 0 1 1 0 −3 0 −7 ESC1 VPS51 1 0 0 1 −1 −1 −1 0 YMR110CHFD1 ESC1 0 2 0 1 1 −2 1 −6 VPS53 VPS53 1 0 0 1 0 −1 −1 −1 YPK1 0 1 0 2 −1 −2 −1 −9 MLP1 Z_lm_L_Doxo_EG YPK1 Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD

MLP1 Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG 0 2 −1 2 1 −3 1 −7 VPS52 0 1 0 1 −3 0 −2 −2 Z_Shift_K_Doxo_EG

HUF3 Z_Shift_L_Doxo_HLD VPS52 HUF3 Z_Shift_K_Doxo_HLD −1 1 −1 2 2 −3 0 −6 VPS54VPS54 1 0 0 1 0 −1 1 0 LRO1 Gene 2 0 1 0 −1 −1 0 0 LAC1 1 0 0 0 −1 −1 0 1 YSR3 1 0 0 1 0 −1 0 0 NCR1 Z_lm_L_Doxo_EG 1 0 0 1 −1 −1 −1 0 YGL010W Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG MPO1 Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD 0 0 0 1 1 −1 −1 1 YDC1 Z_Shift_K_Doxo_HLD Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG YDC1 Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD

Z_Shift_L_Doxo_EG 0 0 0 1 −2 0 −2 0 Z_Shift_K_Doxo_EG YBR159W Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD IFA38 0 0 0 0 −1 0 −1 1 DGA1 0 0 −1 1 −1 −1 −1 1 YPC1 0 1 0 −2 −2 0 TLG2 0 1 −1 0 −2 −4 1 −3 SUR1 1 0 0 1 −2 −1 −1 −2 YND1 0 0 0 1 −1 −3 −2 −3 IPT1IPT1 0 0 0 1 0 −2 1 −3 ISC1 −1 1 −1 1 −1 −1 1 −3 LAG1 −1 1 0 1 −1 −2 0 −2 SKN1 0 1 −1 1 1 −2 −1 −4 DPL1 0 0 −1 1 −1 −1 −1 −5 LCB4 −8 1 −21 2 0 −2 5 8 CSG2 1 1 −3 4 6 −8 3 −14 FEN1 0 1 −1 2 2 −6 2 −11 SUR4 1 0 1 1 2 −7 −1 −6 TSC3TSC3_2 2 0 0 1 1 −5 −2 −7 YBR058CTSC3_1−A_1 0 1 −1 2 0 −4 −1 −8 SUR2 1 0 1 1 −1 −5 −1 −7 LIP1 Z_lm_L_Doxo_EG Z_lm_K_Doxo_EG Z_lm_L_Doxo_HLD Z_lm_K_Doxo_HLD Z_Shift_L_Doxo_EG Z_Shift_K_Doxo_EG Z_Shift_L_Doxo_HLD Z_Shift_K_Doxo_HLD bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. A Yeast-Human Homology Relationship y ORF h homolog y GDSC h GDSC y gCSI h gCSI

One:One (orthologs) One to One 925 925 823 823 870 870 One:Many (human paralogs) One to Many 897 2738 828 2299 877 2489 Many:Many (yeast and human paralogs) Many to Many 363 874 350 693 359 812

Total 2165 4537 2001 3815 2106 4171 B Data across all Data from tissue types individual tissues C Yeast deletion enhancer / D Yeast deletion suppressor/ human UES human OES Significant differential gene expression with doxorubicin hypersensitivity in cancer cell lines UES OES

Color Key Enhancers across tissue with yeast enhancers only Color Key Corresponding to Corresponding to Suppressors across tissue with yeast suppressors only yeast deletion yeast deletion

enhancer suppressor −10 5 −10 5 Value Value

MYO2/MYO5B MYO2/MYO5B MAP1/METAP1MAP1/METAP1 MYO2/MYO15A MYO2/MYO15A SSE1/HSPA4SSE1/HSPA4 MYO2/MYO5C MYO2/MYO5C ELO2/ELOVL6ELO2/ELOVL6 MYO2/MYO6 MYO2/MYO6 GCN5/BAZ1BGCN5/BAZ1B MYO2/MYO7A Prediction: Prediction: MYO2/MYO7A GCN5/BAZ1AGCN5/BAZ1A MYO2/MYO7B MYO2/MYO7B TGS1/TGS1TGS1/TGS1 human genes buffer human genes confer ALA1/AARS ALA1/AARS STO1/NCBP1STO1/NCBP1 GPI14/PIGM GPI14/PIGM YNL069C_1/RPL13ARPL16B_1/RPL13A doxorubicin toxicity doxorubicin toxicity HEM2/ALAD HEM2/ALAD BUD31/BUD31BUD31/BUD31 CBK1/CDC42BPG CBK1/CDC42BPG CGI121/TPRKBCGI121/TPRKB CBK1/CDC42BPA CBK1/CDC42BPA IST3/RBMX2IST3/RBMX2 CBK1/DMPK CBK1/DMPK SMT3/SUMO2SMT3/SUMO2 CCT8/CCT8L2 Color Key CCT8/CCT8L2 RPS24A/RPS24RPS24A/RPS24 STI1/TTC31 STI1/TTC31 YNL069C_2/RPL13ARPL16B_2/RPL13A SEC7/ARFGEF2 NUP170/NUP155NUP170/NUP155 SEC7/ARFGEF1 Selected yeast and SEC7/ARFGEF1 YPT6/RAB34YPT6/RAB34 SEC7/CYTH2 RPS6A/RPS6 −10 5 SEC7/CYTH2 RPS6A/RPS6 SEC7/IQSEC1 E Value human UES SEC7/IQSEC1 PAC10/VBP1PAC10/VBP1 SEC7/IQSEC3 SEC7/IQSEC3 RPL19B/RPL19RPL19B/RPL19 HTB1/H2BFWT CDC31/CETN1 HTB1/H2BFM RPL34A/RPL34RPL34A/RPL34 ALG2/ALG2 HTB1/HIST1H2BC ALG2/ALG2 ELO3/ELOVL6ELO3/ELOVL6 HTB1/HIST1H2BD SEC62/SEC62 HTB1/HIST1H2BD SEC62/ALG62 UBC4/UBE2L3UBC4/UBE2L3 HTB1/HIST1H2BE HTB1/HIST1H2BE HEM1/ALAS2 HEM1/ALAS2 UBC4/UBE2D1 HTB1/HIST1H2BF UBC4/UBE2D1 GTR1/RRAGB HTB1/HIST1H2BG GTR1/RRAGB RPL32/RPL32RPL32/RPL32 HTB1/HIST1H2BJ PKC1/PRKCH HTB1/HIST1H2BJ PKC1/PRKCH CIN8/KIF1CCIN8/KIF1C HTB1/HIST1H2BK HTB1/HIST1H2BK PKC1/PRKCG PKC1/PRKCG CIN8/KIF14 HTB1/HIST1H2BN CIN8/KIF14 HTB1/HIST1H2BN PKC1/PRKCZ HTB1/HIST1H2BO PKC1/PRKCZ CIN8/KIF11CIN8/KIF11 HTB1/HIST2H2BE SEC15/EXOC6B HTB1/HIST2H2BE SEC15/EXOC6B SAC1/SACM1LSAC1/SACM1L RCO1/AIRE MTC5/WDR59 RCO1/AIRE MTC5/WDR59 ARP2/ACTR2 SWR1/SRCAP ARP2/ACTR2 SWR1/SRCAP MTC5/WDR24 MTC5/WDR24 YGR054W/EIF2A BRE1/RNF40 YGR054W/EIF2A TOM1/UBR5 SGS1/RECQL5 TOM1/UBR5 YBR133C_2/PRMT5HSL7_2/PRMT5 SGS1/RECQL4 YAP1801/PICALM SGS1/RECQL4 YAP1801/PICALM APC1/ANAPC1APC1/ANAPC1 EMC4/EMC4 EMC4/EMC4 YCR053W_1/THNSL2 THR4_1/THNSL2 VPS53/VPS53 HHF2/HIST1H4H VPS53/VPS53 SIS1/DNAJB2 HHF2/HIST1H4D SIS1/DNAJB2 CKA2/CSNK2A2CKA2/CSNK2A2 HHF2/HIST1H4I HHF2/HIST1H4I SIS1/DNAJB13 YFH1/FXNYFH1/FXN HHF2/HIST1H4K HHF2/HIST1H4K SIS1/DNAJB8 POB3/SSRP1 HHF2/HIST2H4A POB3/SSRP1 HHF2/HIST2H4A SIS1/DNAJC5 HHF2/HIST2H4B SIS1/DNAJC5 RPL40B/ZFAND4RPL40B/ZFAND4 HHF2/HIST4H4 SIS1/DNAJC5G RPL40B/UBA52RPL40B/UBA52 EMC3/EMC3 EMC3/EMC3 HDA1/HDAC6 PWP1/PWP1PWP1/PWP1 SET2/SETBP1 ULP2/SENP7 RPL21B/RPL21 SET2/ASH1L ULP2/SENP7 RPL21B/RPL21 UBA4/MOCS3 MVD1/MVD DOA1/PLAADOA1/PLAA TOM70/UNC45B TOM70/UNC45B THP3/LENG8 RPL38/RPL38RPL38/RPL38 MGR2/ROMO1 VPS8/VPS8 UBP12/USP16 HDA1/HDAC6 UBP12/USP16 YLR153C_1/ACSM3 HHF1/HIST1H4H ACS2_1/ACSM3 RPL36A/RPL36RPL36A/RPL36 HHF1/HIST1H4D HHF1/HIST1H4D ACS2_1/ACSM1YLR153C_1/ACSM1 FPR1/FKBP1AFPR1/FKBP1A HHF1/HIST1H4I ACS2_1/ACSM4YLR153C_1/ACSM4 DBR1/DBR1DBR1/DBR1 HHF1/HIST1H4K YLR153C_1/ACSM5 HHF1/HIST2H4A ACS2_1/ACSM5 RPL19A/RPL19RPL19A/RPL19 HHF1/HIST2H4B ACS2_1/ACSS1YLR153C_1/ACSS1 NEM1/CTDNEP1NEM1/CTDNEP1 HHF1/HIST4H4 HHF1/HIST4H4 YLR153C_1/ACSS2 ACS2_1/ACSS2 RPS16B/RPS16RPS16B/RPS16 HTA1/HIST1H2AD BEM2_1/ARHGAP27YER155C_1/ARHGAP27 TMA19/TPT1 HTA1/HIST1H2AC TMA19/TPT1 HTA1/HIST1H2AG BEM2_1/ARHGAP21YER155C_1/ARHGAP21 TAF12/TAF12TAF12/TAF12 HTA1/HIST1H2AK HTA1/HIST1H2AK BEM2_1/ARHGAP5YER155C_1/ARHGAP5 TUB2/TUBB6TUB2/TUBB6 HTA1/HIST2H2AA3 BEM2_1/OPHN1YER155C_1/OPHN1 TUB2/TUBB HTA1/HIST2H2AA4 TUB2/TUBB SFA1/ADH4 STT3/STT3A SRP1/KPNA3SRP1/KPNA3 SFA1/ADH1A BEM2_2/ARHGAP27YER155C_2/ARHGAP27 SRP1/KPNA2SRP1/KPNA2 SFA1/ADH6 BEM2_2/ARHGAP21YER155C_2/ARHGAP21 IMP4/IMP4IMP4/IMP4 DOM34/PELO YER155C_2/ARHGAP5 SEC11/SEC11C BEM2_2/ARHGAP5 SNP1/SNRNP70SNP1/SNRNP70 HHT1/HIST1H3D BEM2_2/OPHN1YER155C_2/OPHN1 EFT2/EFTUD2EFT2/EFTUD2 HHT1/HIST1H2AE HHT1/HIST1H2AE SPT5/SUPT5H EFT2/EEF2EFT2/EEF2 HHT1/HIST1H3E HHT1/HIST1H3E SEC11/SEC11C CFT2/CPSF2 HHT1/HIST1H3H SEC11/SEC11C CFT2/CPSF2 MAK3/NAA30 SCH9/SGK2 KIN28/CDKL1KIN28/CDKL1 MUS81/MUS81 MUS81/MUS81 SCH9/RPS6KB2 KIN28/CDK7KIN28/CDK7 SCH9/SGK2 SCH9/SGK2 YAL021C_1/ANGEL1 NCB2/DR1 SCH9/RPS6KB2 CCR4_1/ANGEL1 NCB2/DR1 SOD1/CCS SIS2/PPCDC CDC3/SEPT6CDC3/SEPT6 SOD1/SOD1 SOD1/CCS HTS1/HARSHTS1/HARS ARP4/ACTL6B ARP4/ACTL6B PBY1/TTLL10 RNA14/CSTF3RNA14/CSTF3 ERG13/HMGCS2 MUS81/MUS81 TRZ1/ELAC2 ERG13/HMGCS1 MUS81/MUS81 TRZ1/ELAC2 PBP1/ATXN2L MPS1/TTKMPS1/TTK CCR4_2/ANGEL1YAL021C_2/ANGEL1 RSE1/SF3B3RSE1/SF3B3 DOXO.HL.L.SHIFT DOXO.HL.K.SHIFT PTC1/PPM1L TFB4/GTF2H3TFB4/GTF2H3 DOXO.HLEG.L.SHIFT DOXO.HLEG.K.SHIFT DOXO.HL.L.INTERACTION DOXO.HL.K.INTERACTION ARP4/ACTL6B DOXO.HLEG.L.INTERACTION DOXO.HLEG.K.INTERACTION AVL9/AVL9 VPS33/VPS33B

YNL280C_2/TM7SF2 DOXO.HL.L.SHIFT ERG24_2/TM7SF2 DOXO.HL.K.SHIFT DOXO.HLEG.L.SHIFT Selected yeast and ERG24_2/DHCR7YNL280C_2/DHCR7 DOXO.HLEG.K.SHIFT DOXO.HL.L.INTERACTION DOXO.HL.K.INTERACTION ERG12/MVK DOXO.HLEG.L.INTERACTION F Color Key ERG12/MVK DOXO.HLEG.K.INTERACTION ZUO1/DNAJC1

−10 5 Value human OES ERG13/HMGCS2 ELO2/ELOVL6 ERG13/HMGCS1 ELO3/ELOVL6 TGS1/TGS1 CGI121/TPRKB TMA22/DENR POB3/SSRP1

VPS53/VPS53 DOXO.HL.L.SHIFT VPS53/VPS53 DOXO.HL.K.SHIFT DOXO.HLEG.L.SHIFT NUP170/NUP155 DOXO.HLEG.K.SHIFT

ARP2/ACTR2 DOXO.HL.L.INTERACTION ARP2/ACTR2 DOXO.HL.K.INTERACTION

JHD1/KDM2B DOXO.HLEG.L.INTERACTION JHD1/KDM2B DOXO.HLEG.K.INTERACTION JHD1/PHF2JHD1/PHF2 UBP8/USP22 UBP8/USP44 SMC2/SMC2 SPE2/AMD1 SPE4/SMS YCG1/NCAPG YCS4/NCAPD2 JHD2/JARID2JHD2/JARID2 TIF34/STRAP TIF34/EIF3ITIF34/EIF3I DOXO.HL.L.SHIFT DOXO.HL.K.SHIFT DOXO.HLEG.L.SHIFT DOXO.HLEG.K.SHIFT DOXO.HL.L.INTERACTION DOXO.HL.K.INTERACTION DOXO.HLEG.L.INTERACTION DOXO.HLEG.K.INTERACTION bioRxiv preprint doi: https://doi.org/10.1101/517490; this version posted January 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Warburg-dependent doxorubicin-gene interaction Warburg

Glycolysis Respiration LOW HIGH toxicity Doxorubicin toxicity • D o m3 4 -H b s1 Co mp l e x • dTTP bi o syn the tic • Chromatin regulation p ro ce ss • Protein folding and • C u l 4 -R ING E3 ub i q u i tin modification l i g a se comp l e x • Mitochondrial function • U b p 3 -Bre 5 • Class I topoisomerase activity deubiquitination comp l e x

• Sp h i n g o l i p i d h o me o stasi s • H i ston e deubiquitination • Actin cortica l pa tch • N u cl e a r con d e n si n comp l e x l o ca l i za tio n • DNA Repair • Fatty acid beta-oxidation, • Te l o me re tethe ri n g at • Histone H3-K56 • Spermine metabolism n u cl e a r pe ri p h e ry acetylation • Translation reinitation