Open Access

Copy-number variation of cancer-gene orthologs is sufficient to induce cancer-like symptoms in Saccharomyces cerevisiae

BMC Biology201311:24

DOI: 10.1186/1741-7007-11-24

Received: 5 October 2012

Accepted: 19 March 2013

Published: 25 March 2013

Abstract

Background

Copy-number variation (CNV), rather than complete loss of gene function, is increasingly implicated in human disease. Moreover, gene dosage is recognised as important in tumourigenesis, and there is an increasing realisation that CNVs may not be just symptomatic of the cancerous state but may, in fact, be causative. However, the identification of CNV-related phenotypes for mammalian genes is a slow process, due to the technical difficulty of constructing deletion mutants. Using the genome-wide deletion library for the model eukaryote, Saccharomyces cerevisiae, we have identified genes (termed haploproficient, HP) which, when one copy is deleted from a diploid cell, result in an increased rate of proliferation. Since haploproficiency under nutrient-sufficient conditions is a novel phenotype, we sought here to characterise a subset of the yeast haploproficient genes which seem particularly relevant to human cancers.

Results

We show that, for a subset of HP genes, heterozygous deletion is sufficient to cause aberrant cell cycling and altered rates of apoptosis, phenotypes associated with cancer in mammalian cells. A majority of these yeast genes are the orthologs of mammalian cancer genes, and hence our studies suggest that CNV of these oncogenic orthologs may be sufficient to lead to tumourigenesis in human cells. Moreover, where not already implicated, this cluster of cancer-like phenotypes in this model eukaryote may be predictive of the involvement in cancer of the mammalian orthologs of these yeast HP genes. Using the yeast set as a model, we show that the response to a range of anti-cancer drugs is strongly dependent on gene dosage, such that intermediate concentrations of the drugs can actually increase a mutant’s growth rate.

Conclusions

The exploitation of data on the phenotypic impact of heterozygosis in Saccharomyces cerevisiae has permitted the prediction of CNVs affecting tumourigenesis in humans. Our yeast data also suggest that the identification of CNVs in tumour cells may assist both the selection of anti-cancer drugs and the dosages at which they should be administered if they are to be a beneficial, rather than a deleterious, therapy.

Keywords

Copy-number variation Model organism Yeast Cancer Haploinsufficiency

Background

There is an increasing realisation that copy-number variation, and in particular the loss of one copy of a gene from a diploid cell or organism, can have a significant phenotypic impact. Moreover, the importance of gene dosage in tumourigenesis is becoming increasingly recognised [1], and the widespread aneuploidy and copy-number variations (CNVs) that are the hallmarks of cancer cells are coming to be seen as potentially causative, rather than simply symptomatic [24]. Identifying causal CNV-related phenotypes for mammalian genes, however, is hampered by the difficulty in constructing deletion mutants in the higher Eukaryotes, and the fact that homozygous knockout (or complete RNAi knockdown) phenotypes do not necessarily correlate with those elicited by the loss of a single copy of a gene [5, 6].

In the model eukaryote Saccharomyces cerevisiae, in contrast, high-throughput screens on whole-genome libraries have facilitated the identification of yeast genes for which CNV has a significant effect on cell proliferation. A screen of > 5,800 deletion mutants, each heterozygous for a different protein-encoding gene in the S.cerevisiae genome, revealed that over 18% of these heterozygotes displayed a significantly reduced growth rate [7]. This haploinsufficient phenotype was displayed even under conditions where there are no external constraints (such as nutrient limitations) on cell growth [8]. For a smaller set (ca. 600 genes, listed in Additional file 1: Table S1) of ‘haploproficient’ genes, however, heterozygous deletion of the gene elicits significantly faster growth than the wild type. Using these yeast phenotypes, we have previously made correct predictions of a gene-dosage-related phenotype for their orthologous human genes [9], and verified these predictions by controlled RNAi knockdown in human cell lines [10].

The existence of haploproficient genes in the yeast genome indicates that the organism has not evolved to maximize its rate of growth, even when sugars became abundantly available with the emergence of flowering plants approximately 80–100 million years ago [11]. The persistence of these genes in the yeast genome therefore suggests that there must be some major selective advantage that outweighs their growth-rate disadvantages. We demonstrate, here, that this advantage could be the maintenance of genome integrity, which is compromised when the dosage of these genes is reduced. For HP yeast genes, the altered gene dosage not only increases growth rate but, as we will show, copy-number reduction, as opposed to complete gene deletion, is sufficient to result in abnormal progression through the cell cycle, increased accumulation of DNA damage, and altered rates of apoptosis. This set of phenotypes is strongly reminiscent of cancer in mammalian cells. These results complement the recent conclusions from a study of aneuploid yeast strains [12] that the detriment to genome stability is driven by gene-dosage effects (e.g. stoichiometric imbalances), rather than simply by the presence of extra DNA (or additional centromeres; [13]). This emphasises the value of using model organisms to predict which human genes may impact on cancer in a dosage-dependent manner.

The screening of a library of yeast heterozygous deletion mutants for haploinsufficient or -proficient phenotypes is a high-throughput approach to determine what effects quantitative changes in the concentrations of gene products have on phenotype. A related approach is the search for drug-induced haploinsufficiency – in which heterozygous deletion strains exhibit altered sensitivity to a compound as a result of the decrease in the dosage of the target gene. In a pioneering study, [14] exposed a pool of 233 heterozygous mutants to sub-lethal compound concentrations, and later work has successfully elucidated the mode-of-action of novel compounds such as the anti-tumour agent dihydromotuporamine C [15], demonstrating the utility of such genome-wide yeast screens. In the light of our findings on haploproficiency in yeast, we combined both approaches and carried out a screen of anti-cancer agents against a set of S. cerevisiae mutants heterozygous for HP genes involved in the DNA damage response-pathway in order to search for altered sensitivities relative to both the WT and the corresponding homozygous (null) deletion mutant. This screen might inform both the appropriate treatment of tumour cells that carry CNVs of the candidate cancer-related genes, and also suggest novel combinations of specific inhibitors which may prove more effect than either drug in isolation.

Results

Yeast haploproficient genes are involved in the maintenance of genome integrity and are orthologs of human cancer genes

The existence of haploproficient genes, and the inference that the yeast genome has not been optimised for maximal growth rate, does not appear to be an accident, nor unique to S. cerevisiae. By examining orthology relationships across the Ascomycetes, we find that haploproficient genes are more highly conserved than the genome average across the lineage (Fischer’s exact test; p < 0.01) (Table 1a). Thus, selective pressure has existed toward the retention of HP genes for several hundred million years (the evolutionary distance between S. cerevisiae and Sz. pombe), including through a period of strong selective pressure toward maximizing growth rate that occurred at the time of the whole-genome duplication in the Saccharomyces lineage (approximately 100 million years ago [16]). Congruent with our hypothesis of a trade-off between genome stability and growth rate optimisation, we find that haploproficient genes are overrepresented amongst those involved in the maintenance of genome integrity. A Gene Ontology term-enrichment revealed that the HP set is enriched for genes involved in the mitotic cell cycle (p = 0.003), and, in particular, the response to and repair of DNA damage (p = 0.006) (Figure 1; Additional file 2: Table S2). Given their integral role in maintaining genome stability, it is unsurprising that yeast HP genes are very much more likely to be orthologous to cancer genes than the S. cerevisaie genome average (p < 10-10).
Table 1

a.) P- values of enrichment of haploproficient genes amongst those S.cerevisae genes having orthologs in Ascomycete species; b.) P- values for the enrichment of the subset of S.cerevisiae genes involved in the cell cycle

Species

P-value of HP gene enrichment amongst orthologs

S.paradoxus

0.02

S.mikatae

0.1

S.bayanus

0.03

S.castellii

0.2

C.glabrata

0.07

S.kluyveri

0.02

K.waltii

0.02

K.lactis

0.05

A.gossypii

0

C.albicans

0.1

C.tropicalis

0.08

C.parapsilosis

0.04

L.elongisporus

0.04

C.guilliermondii

0.05

C.lusitaniae

0.03

D.hansenii

0.07

A.nidulans

0.1

N.crassa

0.05

Sz.pombe

0.06

Sz.octosporus

0.05

Sz.japonicus

0.02

Species

P- value of HP cell cycle gene enrichment amongst orthologs

S.paradoxus

0.01

S.mikatae

0.001

S.bayanus

0.0001

S.castellii

0.01

C.glabrata

0.01

S.kluyveri

0.02

K.waltii

0.07

K.lactis

0.3

A.gossypii

0.001

C.albicans

0.9

C.tropicalis

0.2

C.parapsilosis

0.2

L.elongisporus

0.2

C.guilliermondii

0.4

C.lusitaniae

0.2

D.hansenii

0.3

A.nidulans

0.6

N.crassa

0.7

Sz.pombe

0.01

Sz.octosporus

0.01

Sz.japonicus

0.001

https://static-content.springer.com/image/art%3A10.1186%2F1741-7007-11-24/MediaObjects/12915_2012_Article_666_Fig1_HTML.jpg
Figure 1

Haploproficient genes. The incidence of genes identified in genome-wide deletion library screens [8] as haploproficient within the S.cerevisiae cell cycle (haploproficient genes highlighted in red) (pathway diagram derived from the KEGG database [45]). Abbreviations: APC = anaphase promoting complex; SCF = Sck1-Cdc53/CUL-F Box receptor E3 complex; MCM = minichromosome maintenance complex; ORC = origin recognition complex; PP2A = protein phosphatase 2 complex. Haploproficient members of the complexes are listed in Additional file 2: Table S2.

We selected the 30 HP genes (Table 2) involved in the processes of DNA damage repair and sister chromatid segregation as the most relevant candidates in which to examine the relationship between varying gene dosage and cancer-related phenotypes. This ‘HP genome integrity’ (HPGI) set is even more highly conserved than the HP set as a whole (Table 1b); and more likely (than the HP set as a whole) to be orthologous to a cancer gene (p < 10-4). Nineteen genes in the set have a unique human ortholog (Table 2) and, of these, 12 (UBX4, IDP1, IDP2, MSH2, RAD1, TOP2, NBP2, MUS81, RAD54, DBF2, STP22, PBS2) are orthologous to cancer genes as annotated in the Cancer Gene Index (CGI [17]). Table 2 lists the cancer-specific OMIM disease associations of the orthologs of members of the HPGI set.
Table 2

Yeast genes comprising the Haploproficient Genome-Integrity (HPGI) set, along with their unique human orthologs, and functional annotations

Yeast gene

Unique human ortholog

Human ortholog function & OMIM cancer association (where applicable)

Yeast gene product cell cycle-related function

BUB2

-

-

Mitotic exit network regulator; blocks cell cycle progression before anaphase in response to spindle and kinetochore damage

CCR4

CNOT6

CCR4-NOT transcription complex, subunit 6

Component of the CCR4-NOT transcriptional complex

CDC34

UBE2R2

Ubiquitin-conjugating enzyme E2R 2

Ubiquitin-conjugating enzyme (E2) and catalytic subunit of SCF ubiquitin-protein ligase complex that regulates cell cycle progression by targeting key substrates for degradation

CHK1

CHEK1

Serine/threonine kinase and DNA damage checkpoint effector

Serine/threonine kinase and DNA damage checkpoint effector

CLB1

CCNB1

Cyclin B1

B-type cyclin involved in cell cycle progression; activates Cdc28p to promote the transition from G2 to M phase

CTF8

CHTF8

Subunit of a complex with Ctf18p that shares some subunits with Replication Factor C and is required for sister chromatid cohesion

Subunit of a complex with Ctf18p that shares some subunits with Replication Factor C and is required for sister chromatid cohesion

DBF2

-

-

Ser/Thr kinase involved in transcription and stress response; functions as part of a network of genes in exit from mitosis.

DNL4

LIG4

DNA ligase required for non-homologous end-joining; OMIM: Lig4 syndrome

DNA ligase required for nonhomologous end-joining (NHEJ)

IDP1

IDH1

Isocitrate dehydrogenase 1 (NADP+); OMIM: Oligodendroglioma; piloytic astrocytoma

Mitochondrial NADP-specific isocitrate dehydrogenase, catalyzes the oxidation of isocitrate to alpha-ketoglutarate

IRR1

STAG1

Stromal antigen 1

Subunit of the cohesin complex, required for sister chromatid cohesion during mitosis and meiosis

MAD1

MAD1L1

Component of the spindle-assembly checkpoint

Coiled-coil protein involved in the spindle-assembly checkpoint; forms a complex with Mad2p

MEU1

MTAP

Methylthioadenosine phosphorylase; OMIM: Glioma

Methylthioadenosine phosphorylase

MLH1

MLH1

mutL homolog 1, colon cancer, nonpolyposis type 2

Protein required for mismatch repair in mitosis

MSH2

MSH2

mutS homolog 2, colon cancer, nonpolyposis type 1; OMIM: colorectal cancer

Protein required for mismatch repair in mitosis

MUS81

MUS81

MUS81 endonuclease homolog; OMIM: Bloom syndrome (predisposition to leukemias, lymphomas & carcinomas)

Subunit of the Mms4p-Mus81p endonuclease that cleaves branched DNA; involved in DNA repair, replication fork stability

RAD1

ERCC4

Excision repair cross-complementing rodent repair deficiency, complementation group 4; OMIM: Xeroderma pigmentosum (predisposition to basal cell carcinoma & melanoma)

Single-stranded DNA endonuclease (with Rad10p), cleaves single-stranded DNA during nucleotide excision repair and double-strand break repair

RAD54

RAD54L

RAD54-like; OMIM: Lymphoma, non-Hodgkin

DNA-dependent ATPase, stimulates strand exchange by modifying the topology of double-stranded DNA; recombinational repair of double-strand breaks in DNA

RAD61

-

-

Subunit of a complex (Scc3p, Pds5p, Rad61p) that inhibits sister chromatid cohesion

RAD9

RAD9

-

DNA damage-dependent checkpoint protein, required for cell-cycle arrest in G1/S, intra-S, and G2/M; transmits checkpoint signal by activating Rad53p and Chk1p

SMC2

SMC2

Structural maintenance of chromosomes 2

Subunit of the condensin complex

STP22

TSG101

Tumor susceptibility gene 101; OMIM: Breast cancer

Component of the ESCRT-I complex

TOP1

TOP1

Topoisomerase (DNA) I

Topoisomerase I

TOP2

TOP2B

Topoisomerase (DNA) II beta 180kDa

Topoisomerase II

TPD3

PPP2R1B

Protein phosphatase 2, regulatory subunit A, beta; OMIM: Lung cancer

Regulatory subunit A of the heterotrimeric protein phosphatase 2A (PP2A)

TUM1

TST

Thiosulfate sulfurtransferase (rhodanese); OMIM: Tumours

Rhodanese domain sulfur transferase

UBX4

ASPSCR1

Alveolar soft part sarcoma chromosome region, candidate 1; OMIM: Alveolar soft-part sarcoma

UBX (ubiquitin regulatory X) domain-containing protein that interacts with Cdc48p

YCG1

NCAPG

non-SMC condensin I complex, subunit G

Subunit of the condensin complex

RIM11

GSK3A

Glycogen synthase kinase 3 alpha

Protein kinase required for signal transduction during entry into meiosis

NBP2

SH3GL1

SH3-domain GRB2-like 1; OMIM: Acute myeloid leukemia

Protein involved in the HOG (high osmolarity glycerol) pathway, contains an SH3 domain that binds Pbs2p

PBS2

MAP2K1

MAP kinase kinase

MAP kinase kinase of the HOG signaling pathway; mediates cell cycle arrest

Orthologs of haploproficient genes exhibit CNV in tumour cells

The haploproficiency phenotype is, by definition, linked to a reduction in gene copy number. Our hypothesis posits that it is copy number variation (specifically, a reduction in gene copy number) of the orthologs of these genes that is relevant to human cancer. To determine the rate of incidence of CNV in orthologs of our HP cell cycle set, we queried our list against the NCI’s Cancer Genome Atlas database (TCGA [17]). No less than 17/19 of the human orthologs of the HPGI set have a copy number log2 ratio of magnitude >0.5 across eight types of cancer. In particular, 12/19 have a copy number reduction (i.e. a log2 ratio of < −0.5) in >25% of patients in one or more of the eight cancer types. This is a significant overrepresentation compared with 7750/~18500 of all genes in TCGA (p = 0.07), and with the set of non-HP cell-cycle/DNA repair orthologs (p = 0.05). For serous cystadenocarcinoma, kidney renal clear cell, and lung squamous cell carcinomas, there is a significant overrepresentation of orthologs of the HPGI set amongst genes exhibiting frequent copy-number reduction (p-values of 2 × 10-4, 10-4 and 10-3 respectively). Moreover, across all eight cancer types, orthologs of the HPGI set are overrepresented with a p-value of 0.003 (all corrected for multiple testing).

CNV of haploproficient genes is sufficient to elicit cancer-like phenotypic responses in yeast

Cell cycle phasing

As their growth rate is higher than the previously observed maximum, heterozygous deletion mutants of HP genes must a priori progress faster through the cell cycle. Either progress through all the phases of the cycle may be accelerated, or one or more phases must be relatively shorter than in a diploid cell with two copies of the gene. Since the products of HPGI genes are involved in checkpoint controls of the cell cycle, we reasoned that the faster cycle time in the heterozygotes could be the result of cells skipping a compromised checkpoint and therefore progressing faster through that phase of the cycle. This would manifest itself in an altered population distribution between the different cell cycle phases. This same cell cycle dysregulation, arising from compromised tumour suppressor gene function, is a mechanism of oncogenesis in mammalian cells [18]. Previously, we found strong support for the hypothesis that changes in the copy number of the high flux control (HFC) genes alter cell cycle progression in a combined modelling/experimental study of a set of genes involved in the G2/M transition in yeast [10]. This intrinsic link between haploproficiency and cell cycling is also borne out in our finding that copy-number variation of HP genes has no significant effect on yeast chronological lifespan; that is, survival in the extended non-cycling G0 state of stationary phase (see Additional file 3: Table S3).

Using flow cytometry, we analysed the cell cycle progression of heterozygous deletion mutants of the HPGI set, comparing them with the cell cycle profiles of the WT and of the non-cell cycle, non-HP controls HIS3/his3 and HO/ho, non-HP, cell-cycle HSL1/hsl1, CLB2/clb2, CLB5/clb5, CLB6/clb6, CLN2/cln2 and CLN3/cln3 and the non-cell cycle genes PNP1, MET7, HRK1 and TPO3. To distinguish between the effects of the complete absence of a given gene from those of reducing its copy number from two to one, we also compared the cycle profiles of the heterozygous deletant against that of the diploid homozygous deletants of the same gene. Complete cell cycle profile data is given in Additional file 4: Table S4.

We find that correct dosage of the HPGI genes is essential for normal cell cycling: heterozygous deletion is indeed sufficient to significantly alter the cell cycle profile relative to the WT/non-cell cycle control genes for 13 of the 30 HPGI genes (summarised in Table 3). Typically, the cell cycle perturbation for the heterozygote concurs with that previously reported for haploid null deletants of the gene (as annotated in SGD [19]), however BUB2 heterozygous deletion confers the opposite phenotype (an decrease in the relative G1 population) to that of both the null diploid and haploid.
Table 3

Cell cycle phenotypes of those deletion strains having a cycle profile significantly distinct from the wild-type

Cell cycle phenotype

Genotype

G1 Depletion (>80% of WT, p < 0.05)

 
 

BUB2/bub2

 

RAD54/rad54

 

TPD3/tpd3

 

mus81/mus81

 

rad1/rad1

 

MAD1/mad1

S Depletion (>80% of WT, p < 0.05)

 
 

RAD9/rad9

 

rad9/rad9

 

MUS81/mus81

 

SMC2/smc2

 

tpd3/tpd3

 

TUM1/tum1

 

YCG1/ycg1

G2 Depletion (>80% of WT, p < 0.05)

 
 

DBF2/dbf2

 

MLH1/mlh1

 

STP22/stp22

Apoptosis rate

In mammalian cells, compromising the link between the DNA damage response and apoptotic pathways can diminish the apoptotic response [20], which is a necessary step en route to cancer [21]. Therefore, we carried out tests to determine whether compromising the DNA damage response in yeast by heterozygous deletion of HPGI genes affected the rate of apoptosis (see [22] for a review of S.cerevisiae programmed cell death). The degree of apoptosis occurring in the deletion strain populations, in response to treatment with methyl methanesulfonate (MMS) or tert-butyl hydroperoxide (TBHP). was measured by simultaneous annexin V (cell surface phosphatidylserine [23]) and propidium iodide (cell viability) staining [24] to distinguish between apoptotic and necrotic cells. For 15 of the 30 HPGI genes, the heterozygous deletion mutant exhibited a degree of apoptosis significantly different (p < 0.05) from than the WT – that is, partial knockdown of the gene expression is sufficient to disrupt the normal apoptotic response of the cell. Whilst exploring the specific mechanisms behind this disruption for each gene is beyond the scope of this paper, it is clear that deviating from the WT dosage of these particular genes has a significant effect on programmed cell death in response to genotoxic agents. Phenotypes for the mutants are described in Table 4; apoptosis rates in HIS3/his3Δ and HO/hoΔ control strains are comparable to WT.
Table 4

Observed apoptotsis-related phenotypes of our HP yeast genes compared with those of their human orthologs

HP yeast gene

Human ortholog

Apoptotic phenotype (% dead cells) of yeast heterozygous deletion mutants treated with MMS. (Change in rate of apoptosis, relative to WT, is significant at p < 0.05 for all yeast heterozygotes.)

Mammalian apoptosis-related phenotype from literature

  

0.01% MMS

0.001% MMS

 

WT

-

79%

68%

 

CTF8

CHTF8

49%

-

-

MSH2

MSH2

56%

-

RNAi-mediated knockdown decreases apoptosis [25]

MUS81

MUS81

56%

-

-

TPD3

PPP2R1B

60%

-

RNAi-mediated knockdown decreases apoptosis [26]

TOP1

TOP1

55%

-

RNAi-mediated knockdown reduces nuclear apoptosis [27]

RAD54

RAD54L

44%

 

-

SMC3

SMC3

-

30%

RNAi-mediated knockdown increases apoptosis [28]

RAD1

ERCC4

-

22%

-

DNL4

LIG4

-

46%

Extensive apoptosis in mouse deletion homozygotes [29]

MAD1

MAD1L1

-

20%

-

MEU1

MTAP

-

94%

Pharmacological targeting of MTAP promotes apoptosis [30]

MLH1

MLH1

-

93%

Abnormal apoptosis (OMIM)

CHK1

CHEK1

-

82%

Blocking CHK1 induces apoptosis [31]

UBX4

ASPSCR1

-

80%

Regulator of apoptosis (OMIM)

The phenotypes observed for the HPGI heterozygous deletants were compared with those reported in the literature for the RNAi-mediated knockdown or pharmacological inhibition of their mammalian orthologs. The congruence between the yeast and mammalian phenotypes is summarized in Table 4. For 2/4 cases of increased apoptosis upon copy-number reduction, the phenotype is also observed in human cells, however for MLH1/MLH1 and UBX4/ASPSCR1 the relationship between gene dosage and apoptosis in human cells is acknowledged, but unclear [32].

Similarly, for 4/11 genes prompting reduced apoptosis upon copy-number reduction, knockdown of the human ortholog has been reported to cause a similar phenotype; we could find no report for the orthologs of a further 5 of the 11. For SMC3/SMC3 and DNL4/LIG4, however, copy-number reduction in yeast reduced apoptosis, whilst knockdown in mammalian systems has been reported to increase its occurrence [28, 29]. However, both of these reports involve a complete deletion of the gene, rather than the heterozygous deletion in which we observed the yeast phenotype. This suggests that further investigation in human cells of the effects of varying gene dosage of these genes, in particular, would be worthwhile.

Haploproficiency & cancer drug sensitivity

Given the strong connection that we have observed between yeast haploproficiency and human cancer, it is unsurprising that the human orthologs of many of the HPGI genes are the targets of various anti-cancer compounds (both approved drugs and those in late-stage clinical trials; see Table 5). However, in the light of the increased growth upon reduction of HP gene dosage, and the dosage-specific phenotypes reported above, it is possible that inhibitor treatment of a tumour cell could elicit the opposite to the desired response – i.e. increased proliferation rather than cell death – if complete ablation of the HP target function is not achieved. Therefore, we examined the drug sensitivity of wild-type yeast, and the heterozygous and homozygous deletion mutants for each of the non-essential HPGI genes. We also included the deletion mutants for an additional five yeast genes (HRK1, TPO3, MET7, PNP1 and GPD1), which are HP and whose products are orthologous to specific cancer-drug targets.
Table 5

Anticancer drugs and the hypertolerant phenotype of yeast strains heterozygous for the HP gene encoding their target protein

Drug

Concentration in solid medium (uM)

Concentration in liquid medium (uM)

Mode of action

Specific HP genetic target (yeast/human)

Hyper-tolerant yeast strains

Cancer in which CNV-reduction of the gene deleted

in the hypertolerant strain occurs

    

RAD54/RAD54L

  

Cycloheximide

0.36, 1.8

0.1

Inhibition of translation

RIM11/GSK3B

IRR1/irr1

-

Mitoxantrone

10, 25

-

TOPII inhibition

TOP2/TOP2A

-

-

     

NBP2/nbp2

 
     

GPD1/gpd1

Serous adenocarcinoma (NBP2)

Methotrexate

200

2, 100

Inhibition of nucleoside and purine synthesis

MET7/FPGS

RIM11/rim11

Renal cell carcinoma (GPD1)

Phleomycin

1, 2

0.1, 0.5

DNA strand break induction

-

MET7/met7

Serous adenocarcinoma

5-fluorouracil

200

2, 100

Inhibition of pyrimidine synthesis

-

IDP1/idp1

-

Tamoxifen

25

-

Estrogen receptor antagonist

-

-

-

     

NBP2/nbp2

 

Aminopterin

10

0.1,1, 5, 10, 50, 100

Inhibition of DNA, RNA & protein synthesis

-

TOP1/top1

Serous adenocarcinoma

SAHA/vorinostat

10, 20

-

Histone deacetylase inhibition

RPD3/HDAC2

-

-

Bay 11-7085

20, 40

-

NF-kB TF inhibitior; proapoptotic agent

-

-

-

Cantharidin

30, 60

6, 30

Inhibition of PP2A

TPD3

UBX4/ubx4

-

PD98059 (2'-amino-3'-methylflavone)

40, 80

4

MEK1 MAPK inhibition

PBS2/MAP2K1

NBP2/nbp2

Serous adenocarcinoma

     

HRK1/hrk1

 
     

MLH1/mlh1

 

Hydroxyurea

100000, 200000

100, 1000

Inhibition of DNA synthesis; DSB induction

RAD54/RAD54L

STP22/stp22

Renal cell carcinoma (MLH1)

Lithium chloride

 

-

Alternative RIM11 inhibitor

RIM11/GSK3B

-

 

Caffeine

1000, 5000

-

Alternative RAD54 inhibitor

RAD54/RAD54L

-

 

Methyl methanesulfonate

 

-

DNA methylation, causing DSB or stalled replication forks

-

-

 

DPI

10, 20

-

NAD(P)H oxidase inhibition

-

-

 

Tert-butyl hydroperoxide (TBHP)

 

-

Free-radical generation

-

-

 

Clotrimazole

2.5, 5, 7.5

-

Antifungal; increases cell wall permeability

-

-

 

Where the human ortholog of a yeast HPGI gene product is a cancer-drug target (Table 5) we included in our screen either the commercial drug (where available) or alternative inhibitors (for example, PD98059 for Pbs2p). In total, 18 drugs were screened (see Table 5): two of these were specific alternative inhibitors; 15 were compounds either approved for human cancer treatment or shown to be effective in cell culture; and one control treatment of clotrimazole, a fungicide not known to have anti-cancer properties.

Drug sensitivity profiles were clustered (Figure 2 left panel) by agglomerative hierarchical clustering [33]. The profile elicited by a compound can reveal its mode of action, and thus similarities between drug profiles may indicate the targeting of the same pathways in yeast. Reassuringly, known modes of action are reflected in our profiles – for example, the sensitivity of cell wall integrity/morphogenesis deletion mutants to phleomycin (Figure 2 left panel), which causes cell wall lesions [34].
https://static-content.springer.com/image/art%3A10.1186%2F1741-7007-11-24/MediaObjects/12915_2012_Article_666_Fig2_HTML.jpg
Figure 2

Interrelationships of drug-response profiles of yeast deletion mutants. Left panel: Hierarchical clustering of the response of the heterozygous and homozygous deletion mutants of the HPGI set to treatment with a panel of anti-cancer compounds. Right panel: Comparison of the drug-response profiles for the heterozygous and homozygous mutants of those HPGI genes for which cancer drug sensitivity differs markedly according to gene copy number.

Control (non-cancer drug) clotrimazole treatments are reasonably distinct from the cancer drug treatments. Aminopterin and methotrexate (which share a common, antifolate, mechanism of action) PD98059, cycloheximide and mitoxantrone cluster together, driven largely by the resistant phenotypes of deletion mutants of genes involved in chromosome condensation and segregation (SMC2, TOP1, RAD61, IRR1, MAD1). A second cluster is formed by Bay11, cantharidin and vorinostat (SAHA), all of which act as inhibitors of the NF-κB pathway in mammalian cells [3537]. This pathway is absent in S.cerevisiae[38] however, the clustering of the compounds suggests that their modes of action, though distinct from the mammalian-cell pathway, may also be related in yeast. rad54/rad54 mutants are particularly resistant to all three drugs, where, in general, these mutants are sensitive to all other treatments. Vorinostat has been demonstrated to induce DSBs in acute myeloid leukemia cells [39], suggesting that the DSB-repair function of Rad54p could potentially be a target. Indeed, the RAD54 ortholog, RAD54L, is strongly down-regulated in LNCAP (prostate adenocarcinoma) cells treated with vorinostat [40]. This is also the case for the mammalian orthologs of CHK1 and TOP2, which are also resistant (though less strongly so) to the vorinostat /Bay11/cantharidin cluster. We have since demonstrated [10] that the magnitude of these drug-response phenotypes in yeast (deviations of up to ±20% of WT growth rate) is sufficient to translate into observable phenotypes in tumour cell lines.

Targeting the CNV profile of tumour cells

Where a copy of a gene is frequently lost in tumour cells, as is the case for orthologs of the HPGI set, the phenotype of the heterozygous deletion mutant of the yeast ortholog might better reflect the tumour cell’s response to drug treatment (Figure 3b). For a significant number of homozygous/heterozygous deletant pairs (9/30, p = 0.01) the drug sensitivity phenotypes are quantitatively dissimilar (by Euclidean squared distance, i.e. diverging at the top level of branching) (Figures 2 left and right panel). For these genes, therefore, gene dosage may be highly relevant to pharmaceutical efficacy. These eight genes (CCR4, TOP1, IDP1, RAD54, BUB2, MUS81, PNP1, and MAD1) have, on average, a greater number of protein-protein interactors (as annotated in BioGrid [41]) than the average for our HPGI set as a whole (180 unique interactors versus 135). This suggests that gene dosage balance may be an important contributor to phenotype for genes which are more central in interaction networks. We have explored the relationship between gene copy number and drug response further in [8].
https://static-content.springer.com/image/art%3A10.1186%2F1741-7007-11-24/MediaObjects/12915_2012_Article_666_Fig3_HTML.jpg
Figure 3

The relevance of haploproficiency and hypertolerance to drug interventions. a) specific inhibition, but not complete protein-product ablation, of an HP target prompts increased growth; b) case where the cancer cell itself bears a CNV of an HP gene, the deletion mutant phenotype may better predict cancer cell drug response; c) case where the deletion mutant for a specific gene is hypertolerant to a compound inhibiting a distinct target, a combination therapy of the same compound plus an inhibitor of the first gene is unlikely to elicit a positive therapeutic response.

Previously, we reported the striking result that treatment of wild-type yeast with low concentrations of inhibitors targeting HP genes can induce the same increased proliferative phenotype as is observed upon reducing the copy number of the HP target, a phenomenon we termed hypertolerance[8]. We observe this phenotype again for treatment with cantharidin, which inhibits the PP2A complex, the subunits of which are strongly haploproficient (Additional file 5: Table S5), but not for any of the general cytotoxic compounds in the drug screen, reinforcing our previously-established link between the specific inhibition of a haploproficient target and drug hypertolerance. A number of heterozygous strains also exhibited hypertolerance in solid-media screens, which was subsequently confirmed by titrating across a range of drug concentrations in liquid medium (Table 5). For tumour cells bearing the orthologous CNVs, this would suggest a contraindication for treatment with the particular drug. In particular, the NBP2/nbp2 heterozygote is multiply drug-hypertolerant, of note since its ortholog SH3GL1 frequently has reduced copy-number in serous cystadenocarcinoma.

Conversely, for PNP1, the heterozygotes are mitoxantrone/methotrexate/aminopterin and vorinostat/Bay11/cantharidin sensitive, and the homozygotes resistant. This phenotype may be a instance of ‘obligate haploinsufficiency’ [42], whereby fitness is compromised as the gene dosage is reduced below some threshold value, but the phenotype is subsequently rescued through the switching on of a compensatory pathway.

Discussion

The data presented here for the S.cerevisiae HPGI set point to the striking result that copy-number variation (in this case, deletion of just one gene copy from a diploid cell), rather than complete gene loss, is sufficient to elicit deleterious phenotypes, in particular those reminiscent of cancer in mammalian cells. This concurs with the increasing appreciation of the significance of copy-number variation to the genesis and progression of disease. In particular, there is a general lack of significant correlation of phenotypes between the heterozygous and homozygous (null) deletion mutants of a given gene. Nor are the phenotypes of the null mutants necessarily more extreme than those of the heterozygous deletants, as might naively be expected from a linear ‘dosage hypothesis’ [43]. This emphasises the need to systematically investigate the phenotypic impact of gene dosage, rather than relying on knockout, or total-knockdown, studies.

A mechanism by which heterozygous deletion of a gene could be just as disruptive as a complete knockout is via the disruption of the stoichiometric ratios of the sub-units of protein complexes. This has been proposed as a mechanism for haploinsufficiency in the 'balance hypothesis' of Papp et al.[4446]. It also suggests that dosage compensation, in which the impact of the heterozygous deletion of a gene is mitigated by the increased expression of the remaining copy, does not play a significant role amongst our HPGI gene set. Indeed, [47] showed that such dosage compensation is, in general, rare in S.cerevisae, and very few HP genes are amongst those exhibiting significant compensation.

For cell cycle phasing, heterozygous gene deletion is often sufficient to perturb the cycle from the WT profile, which indicates that cell cycle proteins are required in yeast at greater than 50% of WT dosage (the quantitative relationship between gene copy number and cell cycling is further explored in [10]). Similarly, heterozygous deletion of the yeast ortholog is, in general, both sufficient to alter the degree of apoptosis occurring in response to DNA-damaging agents (for 15 out of 30 HPGI genes), and to elicit the same apoptotic phenotype (in 8 out of 15 cases) as is reported for complete protein-product knockdown in mammalian cells. This general conservation of apoptotic phenotypes suggests that the DNA damage-related pathway(s) controlling apoptosis in S.cerevisiae are similar to those in mammals. This could be investigated further by undertaking a global survey of the effect of gene deletion on apoptosis rates in yeast, particularly since the current data on the degree of apoptosis in S.cerevisiae is limited (SGD, for example, reports altered apoptotic phenotypes for only 37 null/conditional mutants and 13 overexpression strains, none of which are in the HPGI set). In four cases, an altered apoptosis rate was observed in the yeast deletion mutants but has not, so far, been reported for the RNAi knock-downs of their human orthologs. We would suggest that, based on the apparent predictive value of the yeast phenotype, these genes should become the focus of an RNAi study in mammalian cells.

HP genes and the efficacy of anti-cancer drugs

This critical importance of considering gene dosage in the context of pharmaceutical intervention in cancer is further emphasised in the copy-number-dependence of drug sensitivity phenotypes that we observed with our yeast model. The mechanisms by which copy-number variation may exert a significant effect on phenotype are summarised in Figure 3.

Firstly, products of the mammalian orthologs of several of the yeast HPGI set are the targets of specific drugs used, or proposed for use, in treating cancers. Our data suggest that, in a majority of such cases, complete inhibition of the activity of a target protein product is necessary to achieve a positive therapeutic outcome. Incomplete inhibition, analogous to heterozygous deletion of the gene encoding the target, prompts increased proliferation or hypertolerance – the opposite of the intended anti-cancer effect. Furthermore, even if a given gene is not the intended specific target of a chemotherapeutic treatment, the modes-of-action, and secondary impacts, of many cancer drugs have not been fully elucidated. Drug-induced haploinsufficiency (or haplotolerance) data from yeast can contribute to a better understanding of both drug mechanisms and the functional conservation of drug-metabolism pathways between yeast and humans. For example, the clustering of mutant phenotypes in response to the mammalian-NF-κB inhibitors in this study suggests that all three compounds act through a common pathway in S.cerevisiae, which may be mediated by Rad54p.

Secondly, we observed that varying the copy number of the yeast ortholog can significantly alter the phenotypic response to drug treatment. This is especially for genes whose human orthologs have a high likelihood of CNV in tumour cells, In particular, several deletion strains are either more resistant than the WT, or themselves hypertolerant in response to a specific drug treatment. The impact of specific CNVs on drug sensitivity is becoming increasingly appreciated, and we believe our yeast mutant approach represents a high-throughput complement to the creation of drug-CNV profile ‘fingerprints’ for tumour cells (see e.g. [48]), and a model for determining the most effective drug interventions for tumours with a particular CNV profile.

Lastly, our data on the sensitivity/resistance of deletion mutants could potentially inform the design of combination therapies. Where a particular (heterozygous) deletion mutant is sensitive to a given drug, a combination therapy consisting of that drug plus an inhibitor of the product of the heterozygous gene could have a synergistic effect in reducing cell proliferation. Conversely, considering pairs of genes/drugs for which the deletion mutant is resistant to treatment could assist in identifying those drug combinations which are unlikely to succeed, reducing the search space involved in an otherwise expensive combinatorial problem [49].

Finally, the several instances in which we observed that the heterozygous deletion mutant is more sensitive to drug treatment than the homozygote provide evidence for the ‘obligate haploinsufficiency’ hypothesis of Berger et al.[42]. Identifying such genes, and defining the protein product threshold below which their associated compensatory pathways are activated, could help to define the precise inhibitor dosage to achieve maximal therapeutic efficacy.

Conclusions

Haploproficient (HP) genes and are for which, when one copy is deleted from a diploid cell, the rate of cell proliferation is increased. We have demonstrated that, for a subset of yeast HP genes, heterozygous deletion causes aberrant cell cycling and altered rates of apoptosis, which are phenotypes associated with cancer in mammalian cells. A majority of these yeast genes are the orthologs of mammalian cancer genes, and hence our studies suggest that CNV of these oncogenic orthologs may be sufficient to lead to tumourigenesis in human cells. Moreover, where not already implicated, this cluster of cancer-like phenotypes in this model eukaryote may be predictive of the involvement in cancer of the mammalian orthologs of these yeast HP genes. Using the yeast set as a model, we have shown that the response of the heterozygotes to a range of anti-cancer drugs is strongly dependent on gene dosage, such that intermediate concentrations of the drugs can actually increase a mutant’s growth rate. This suggests such compounds may need to achieve near-total inhibition of target activity if they are to be a beneficial, rather than a deleterious, therapy.

Methods

Haploproficient genes and orthology analysis

The set of S.cerevisiae genes which are haploproficient in turbidostat culture was obtained using the growth data of [8] and an FDR cutoff of 0.02. This stringent FDR cut-off rigorously defines those genes for which heterozygosity confers a strong fitness advantage, but has no effect on the functional enrichment of genes identified as haploproficient. Genes defined as ‘haploproficient’ for the purposes of this study are listed in Additional file 1: Table S1. The set of chromosome maintenance-associated HP genes described in [8] overlaps, but is not coincident, with the HPGI set studied here, since the current set also includes DNA damage-response genes.

Orthology assignments were made using the InParanoid algorithm [50] and compared with the results of a BLAST [51] reciprocal best-hits search. GO enrichment searches were performed using the Babelomics 4 FatiGO tool [52]. To assess the significance of HP gene conservation, the number of HP genes having orthologs in a given Ascomycete species, given the number of S. cerevisiae HP genes, was compared against the whole-genome conserved proportion using a χ2 or Fisher exact test (depending on sample size), with the null hypothesis of identical distribution. All findings of significance were reiterated using a Z test for difference of proportions. Where necessary, P values were corrected for multiple testing using the Bonferroni correction. Cell cycle and DNA damage repair pathways were obtained from the KEGG pathway database [53].

Expression data for S.cerevisae genes was obtained from the Saccharomyces Genome Database [54]; and protein expression levels from [55]. A list of human cancer genes/oncogenes was obtained from the Cancer Gene Index [17]; enrichment of HP genes amongst the orthologs was determined using a χ2 test as above. CNV incidence across eight tumour types (breast invasive carcinoma, rectum adencarcinoma colon adenocarcinoma, kidney renal cell clear carcinoma, uterine corpus endometrioid carcinoma, glioblastoma multiforme, acute myeloid leukemia, lung adenocarcinoma, lung squamous cell carcinoma, serous cystadenocarcinoma) as measured by comparative genomic hybridisation, was obtained from the NCI Cancer Genome Atlas online data browser [17] with a copy number (log2 ratio) of magnitude >0.5 taken as the significance threshold. Details of the sampling and analysis of the tumour samples are described in [17]. A P-value for HP ortholog overrepresentation was calculated using a χ2 test .The TGCA database was also used to perform a pathway search for overrepresentation of HP orthologs.

Yeast strains

In total, 30 HP genes were chosen for analysis, based upon the criteria discussed in the Results above. The heterozygous deletion mutant of each gene was obtained from the heterozygous diploid deletion library (Open Biosystems), in the BY4743 (MAT a /α, his3D1/his3D1, leu2D0/leu2D0, LYS2/lys2D0, met15D0/MET15, ura3D0/ura3D0) genetic background. For non-essential genes, the homozygous deletant was retrieved from the analogous homozygous diploid deletion library (Open Biosystems).

Control strains were the BY4743 WT, along with the heterozygous deletion mutant of the non-functional his3 locus; the non-HP, non-cell cycle ho/HO heterozygous deletion strain; and the heterozygous deletion mutant of the non-HP, cell cycle gene HSL1. In addition, heterozygous deletion mutants of the G1 and G2 cyclins were included in several of the experiments. A complete list of the strains used is provided in Additional file 6: Table S6.

Cell-cycle profiling

Flow cytometric analysis of the deletion strains’ cell cycle profiles was carried about following the method of [56]. Briefly, ~107 cells in mid-exponential phase were harvested, washed, and fixed in absolute ethanol at 4C overnight. Fixed cells were then collected, washed, and boiled for 15 minutes in 2 mg/mL RNAse in 50 mM Tris-Cl (pH 8), and incubated at 37C for 2–12 hours. Cells were resuspended in protease solution (5 mg/mL pepsin, 4.5 μL/mL concentrated HCl), incubated for 15 minutes at 37C and resuspended in 50 mM Tris (pH 7.5). For analysis, 50 mL of cell suspension was added to 1 mL of 1 mM Sytox Green in 50 mM Tris pH 7.5), vortexed and analysed using a Cyan flow cytometer (Beckman Coulter). FlowJo (Tree Star) analysis software was used to fit histograms to the peaks representing 1C and 2C DNA content, and thereby calculate the number of cells in the G1 and G2 phases, and infer the number in S phase from the remaining fraction of the population.

Chronological lifespan assay

Cultures were inoculated from frozen stocks, grown overnight in YPD at 3°C, and 200mL of each was transferred into a well of a 96-well microtiter plate (Corning). Strains were present in duplicate on each plate, with a buffer of WT in the wells around the edge of the plate, so edge effects would not impact test colony measurements. A Singer Rotor HDA colony pinning robot was used to spot four replicates of each well onto a YPD + 10 μg/mL phloxine B (Sigma) plate. Phloxine B is a fluorescein derivative taken up when the cell membrane is disrupted upon cell death [57]. Plates were incubated for 48 hours at 3°C and photographed using an Epson 1240 Scanner. The colony images were analysed using a custom image-analysis code written in MatLab, with colony size measured by pixel count, and fraction of dead cells by the intensity of colony redness [10]. Since these parameters are independent, this allowed the dissection of the effect of cell viability upon colony growth from that of growth rate variation. The 96-well liquid cultures were incubated at 3°C, and, every second day over a period of three weeks, the colony-pinning onto YPD + phloxine B and image analysis repeated. For each plate, the median culture intensity for each strain was compared with the growth of the WT on that plate, and also with the strain growth and viability after the initial 48-hour period. The experiment was performed twice.

At several points throughout the 3-week period, several strains were selected at random, and viability assayed by performing serial dilutions and counting colony-forming units. These results were checked for compatibility with the microplate viability results.

Apoptosis assays

The rate of occurrence of apoptosis in the different strain populations was measured in two ways. Apoptosis was first induced by pretreating cells with 0.001%, 0.01% MMS, 0.0001% or 0.001% TBHP in overnight culture; keeping a negative, non-induced WT control sample.

The translocation of phosphatidyl serine to the cell surface, a marker of apoptosis [58], was measured using an Annexin V-FITC Apoptosis Detection kit. (Sigma). Cells were harvested, washed in 1.2M sorbitol, 0.5 mM MgCl2, 35 mM K phosphate (pH 6.8) and then digested in 5.5% glusulase (Sigma) and 15 U/mL lyticase (Sigma) for 2 hours at 28C. Spheroplasts were harvested, washed in binding buffer (10 mM Hepes/NaOH pH 7.4, 140 mM NaCl, 2.5 mM CaCl2 in 1.2 M sorbitol buffer) and resuspended in binding buffer/sorbitol. 5 mL of FITC-labelled annexin V, and 10 mL of 10010 mg/mL propidium iodide were added to each sample, with control samples containing 1.) no label, 2.) FITC-annexin V only, and 3.) PI only. Fluorescence was quantified using a CyAn (Beckman Coulter). Gates were fitted on the basis of the the control samples, dividing a log PI versus log FITC plot into four quadrants: lower left (neither FITC nor PI-stained) – viable cells; upper left (PI stain only) – necrotic cells; lower right (FITC only) – early apoptotic cells; and upper right (PI and FITC-stained) – late apoptotic cells. FlowJo software (TreeStar) was used to count the fraction of the total cell population in each quadrant. The proportion of both necrotic and apoptotic cells for each strain was normalised to strain viability (i.e. on the basis of the proportion of cells assigned to the lower-left FITC/PI quadrant), and the ratio of necrotic:apoptotic cells calculated. Ratios for each strain were normalised to the WT value, and the standard deviation across all samples calculated. Strains having a necrosis:apoptosis ratio further than 1.5x this standard deviation from WT levels were deemed to demonstrate abnormal apoptosis rates.

Growth rate and drug sensitivity assays

Growth and drug sensitivity assays were performed both on solid media and in liquid cultures. For solid assays, the required drug concentration was added to YPD-agar containing 10μg/m/mL phloxine B. Overnight cultures of the strains were spotted onto the (drug-containing) plates using a Singer rotor, as above. Plates were incubated at 3°C and photographed at 24 and 48 hours and analysed using an image-processing code as described above. Strain growth and viability was compared both with WT growth on the same plate, and with growth on YPD-agar (or YPD-agar plus DMSO, where the drug is DMSO-soluble). The ratio of viability and size with and without drug was calculated for every strain on a plate, and the standard deviation of all ratios calculated. Strains having a drug:untreated ratio greater than or less than two standard deviations from that of the WT were deemed to be resistant and sensitive, respectively.

Assays in liquid culture were performed by transferring 5mL of overnight culture into each well of a 96-well microtitre plate, containing 200 μL of YPD plus the required concentration of drug. Absorbance was measured for 30 hours at 3°C using a BMG Optima platereader, maximum growth rate calculated using a curve-fitting script written in R, and the growth rate for each strain compared with that of the WT in the same plate, and growth in YPD/YPD + DMSO.

Abbreviations

CNV: 

Copy number variation

HP: 

Haploproficient

HPGI: 

Haploproficient genome integrity

TBHP: 

Tert-butyl hydroperoxide

MMS: 

Methyl methanesulfonate

SGD: 

Saccharomyces Genome Database.

Declarations

Acknowledgements

MdC was a Herchel Smith Scholar. This work was supported by awards to SGO from the Biotechnology & Biological Sciences Research Council (Grant BB/C505140/1) and the UNICELLSYS Collaborative Project (No. 201142) of the European Commission.

Authors’ Affiliations

(1)
Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge

References

  1. Craddock N: Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature. 2010, 464: 713-20. 10.1038/nature08979.View ArticlePubMedGoogle Scholar
  2. Speleman F, Kumps C, Buysse K, Poppe B, Menten B, De Preter K: Copy number alterations and copy number variations in cancer: close encounters of the bad kind. Cytogenet Genome Res. 2008, 123: 176-82. 10.1159/000184706.View ArticlePubMedGoogle Scholar
  3. Shlien A, Malkin D: Copy number variations and cancer susceptibility. Curr Opin Oncol. 2010, 22: 55-63. 10.1097/CCO.0b013e328333dca4.View ArticlePubMedGoogle Scholar
  4. Weaver BA, Silk AD, Montagna C, Verdier-Pinard P, Cleveland DW: Aneuploidy acts both oncogenically and as a tumor suppressor. Cancer Cell. 2007, 11: 25-36. 10.1016/j.ccr.2006.12.003.View ArticlePubMedGoogle Scholar
  5. Wilkie AO: The molecular basis of genetic dominance. J Med Genet. 1994, 31: 89-98. 10.1136/jmg.31.2.89.PubMed CentralView ArticlePubMedGoogle Scholar
  6. Veitia RA, Birchler JA: Dominance and gene dosage balance in health and disease: why levels matter!. J Pathol. 2010, 220: 174-85.PubMedGoogle Scholar
  7. Delneri D, Hoyle DC, Gkargkas K, Cross EJ, Rash B, Zeef L, Leong HS, Davey HM, Hayes A, Kell DB, Griffith GW, Oliver SG: Identification and characterisation of high-flux-control genes of yeast through competition analyses in continuous cultures. Nat Genet. 2008, 40: 113-7. 10.1038/ng.2007.49.View ArticlePubMedGoogle Scholar
  8. Pir P, Gutteridge A, Wu J, Rash B, Kell DB, Zhang N, Oliver SG: The genetic control of growth rate: a systems biology study with yeast. BMC Syst Biol. 2012, 6: 4-10.1186/1752-0509-6-4.PubMed CentralView ArticlePubMedGoogle Scholar
  9. de Clare M, Pir P, Oliver SG: Haploinsufficiency and the sex chromosomes from yeasts to humans. BMC Biol. 2011, 9: 15-10.1186/1741-7007-9-15.PubMed CentralView ArticlePubMedGoogle Scholar
  10. Freeland MC: The Impact of Gene Copy Number on the Control of Cell Growth Rate from Yeast to Humans. 2012, Cambridge: (PhD Thesis) University of CambridgeGoogle Scholar
  11. Piskur J, Rozpedowska E, Polakova S, Merico A, Compagno C: How did Saccharomyces evolve to become a good brewer?. Trends Genet. 2006, 22: 183-6. 10.1016/j.tig.2006.02.002.View ArticlePubMedGoogle Scholar
  12. Scheltzer JM, Blank HM, Pfau SJ, Tange Y, George BM, Humpton TJ, Brito IL, Hiraoka Y, Niwa O, Amon A: Aneuploidy drives genomic instability in yeast. Science. 2011, 333: 1026-30. 10.1126/science.1206412.View ArticleGoogle Scholar
  13. Futcher B, Carbon J: Toxic effects of excess cloned centromeres. Mol Cell Biol. 1986, 6: 2213-22.PubMed CentralView ArticlePubMedGoogle Scholar
  14. Giaever G, Shoemaker DD, Jones TW, Liang H, Winzeler EA, Astromoff A, Davis RW: Genomic profiling of drug sensitivities via induced haploinsufficiency. Nat Genet. 1999, 21: 278-83. 10.1038/6791.View ArticlePubMedGoogle Scholar
  15. Baetz K, McHardy L, Gable K, Tarling T, Reberioux D, Bryan J, Andersen RJ, Dunn T, Hieter P, Roberge M: Yeast genome-wide drug-induced haploinsufficiency screens to determine drug mode of action. Proc Natl Acad Sci USA. 2004, 101: 4525-30. 10.1073/pnas.0307122101.PubMed CentralView ArticlePubMedGoogle Scholar
  16. Scannell DR, Frank AC, Conant GC, Byrne KP, Woolfit M, Wolfe KH: Independent sorting-out of thousands of duplicated gene pairs in two yeast species descended from a whole-genome duplication. Proc Nat Acad Sci USA. 2007, 104: 8397-402. 10.1073/pnas.0608218104.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Cancer Gene Index. http://www.sophicalliance.com/,
  18. Collins JA, Schandi CA, Young KK, Vesely J, Willingham MC: Major DNA fragmentation is a late event in apoptosis. J Histochem Cytochem. 1997, 45: 923-34. 10.1177/002215549704500702.View ArticlePubMedGoogle Scholar
  19. Engel SR, Balakrishnan R, Binkley G, Christie KR, Costanzo MC, Dwight SS, Fisk DG, Hirschman JE, Hitz BC, Hong EL, Krieger CJ, Livstone MS, Miyasato SR, Nash R, Oughtred R, Park J, Skrzypek MS, Weng S, Wong ED, Dolinski K, Botstein D, Cherry JM: Saccharomyces Genome Database provides mutant phenotype data. Nucleic Acids Res. 2010, 38: D433-6. 10.1093/nar/gkp917.PubMed CentralView ArticlePubMedGoogle Scholar
  20. Wang JYJ: DNA damage and apoptosis. Cell Death Differ. 2001, 8: 1047-1048. 10.1038/sj.cdd.4400938.View ArticlePubMedGoogle Scholar
  21. Hanahan D, Weinberg RA: The hallmarks of cancer. Cell. 2000, 100: 57-70. 10.1016/S0092-8674(00)81683-9.View ArticlePubMedGoogle Scholar
  22. Carmona-Gutierrez D, Eisenberg T, Büttner S, Meisinger C, Kroemer G, Madeo F: Apoptosis in yeast: triggers, pathways, subroutines. Cell Death Differ. 2010, 17: 763-73. 10.1038/cdd.2009.219.View ArticlePubMedGoogle Scholar
  23. Vermes I, Haanen C, Steffens-Nakken H, Reutelingsperger C: A novel assay for apoptosis. Flow cytometric detection of phosphatidylserine expression on early apoptotic cells using fluorescein labelled Annexin V. J Immunol Methods. 1995, 184: 39-51. 10.1016/0022-1759(95)00072-I.View ArticlePubMedGoogle Scholar
  24. Madeo F, Frohlich E, Frohlich KU: A yeast mutant showing diagnostic markers of early and late apoptosis. J Cell Biol. 1997, 139: 729-34. 10.1083/jcb.139.3.729.PubMed CentralView ArticlePubMedGoogle Scholar
  25. Pabla N, Ma Z, McIlhatton MA, Fishel R, Dong Z: hMSH2 recruits ATR to DNA damage sites for activation during DNA damage-induced apoptosis. J Biol Chem. 2011, 286: 10411-8. 10.1074/jbc.M110.210989.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Ray RM, Bhattacharya S, Johnson LR: Protein phosphatase 2A regulates apoptosis in intestinal epithelial cells. J Biol Chem. 2005, 280: 31091-100. 10.1074/jbc.M503041200.View ArticlePubMedGoogle Scholar
  27. Sordet O, Goldman A, Redon C, Solier S, Rao VA, Pommier Y: Topoisomerase I requirement for death receptor-induced apoptotic nuclear fission. J Biol Chem. 2008, 283: 23200-8. 10.1074/jbc.M801146200.PubMed CentralView ArticlePubMedGoogle Scholar
  28. Ghiselli G: SMC3 knockdown triggers genomic instability and p53-dependent apoptosis in human and zebrafish cells. Mol Cancer. 2006, 5: 52-PubMed CentralView ArticlePubMedGoogle Scholar
  29. Eppig JT, Blake JA, Bult CJ, Kadin JA, Richardson JE, the Mouse Genome Database Group: The Mouse Genome Database (MGD): comprehensive resource for genetics and genomics of the laboratory mouse. Nucleic Acids Res. 2012, 40: D881-86. 10.1093/nar/gkr974.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Basu I, Cordovano G, Das I, Belbin TJ, Guha C, Schramm VL: A transition state analogue of 5’-methylthioadenosine phosphorylase induces apoptosis in head and neck cancers. J Biol Chem. 2007, 282: 21477-86. 10.1074/jbc.M702287200.View ArticlePubMedGoogle Scholar
  31. Wang Y, Decker SJ, Sebolt-Leopold J: Knockdown of Chk1, Wee1 and Myt1 by RNA interference abrogates G2 checkpoint and induces apoptosis. Cancer Biol Ther. 2004, 3: 305-13. 10.4161/cbt.3.3.697.View ArticlePubMedGoogle Scholar
  32. Jackson T, Ahmed MAH, Seth R, Jackson D, Ilyas M: MLH1 function is context dependent in colorectal cancers. J Clin Pathol. 2010, 62: 141-5.Google Scholar
  33. Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95: 14863-8. 10.1073/pnas.95.25.14863.PubMed CentralView ArticlePubMedGoogle Scholar
  34. Moore CW, Del Valle R, McKoy J, Parmanik A, Gordon RE: Lesions and preferential initial localisation of [S-methyl-3H]bleomycin A2 on Saccharomyces cerevisiae cell walls and membranes. Antimicrob Agents Chemother. 1992, 36: 2497-505. 10.1128/AAC.36.11.2497.PubMed CentralView ArticlePubMedGoogle Scholar
  35. Mori N, Yamada Y, Ikeda S, Yamasaki Y, Tsukasaki K, Tanaka Y, Tomonaga M, Yamamoto N, Fugii M: Bay 11–7082 inhibits transcription factor NF-kappaB and induces apoptosis of HTLV-I-infected T-cell lines and primary adult T-cell leukemia cells. Blood. 2002, 100: 1828-34. 10.1182/blood-2002-01-0151.View ArticlePubMedGoogle Scholar
  36. Miller SC, Huang R, Sakamuru S, Shukla SJ, Attene-Ramon MS, Shinn P, Van Leer D, Leister W, Austin CP, Xia M: Identification of known drugs that act as inhibitors of NF-kappaB signalling and their mechanism of action. Biochem Pharmacol. 2010, 79: 1272-80. 10.1016/j.bcp.2009.12.021.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Galimberti S, Canestraro M, Maffei R, Marasca R, Guerrini F, Piaggi S, Ciabatti E, Petrini M: Vorinostat interferes with Wnt and NF-kappaB pathways in the M-07e cell line. Leukemia. 2009, 23: 1935-8. 10.1038/leu.2009.119.View ArticlePubMedGoogle Scholar
  38. Srinivasan V, Kriete A, Sacan A, Jazwinski SM: Comparing the yeast retrograde response and NF-kappaB stress responses: implications for aging. Aging Cell. 2010, 9: 933-41. 10.1111/j.1474-9726.2010.00622.x.PubMed CentralView ArticlePubMedGoogle Scholar
  39. Petruccelli LA, Dupere-Richer , Pettersson F, Retrouvey H, Skoulikas S, Miller WH: Vorinostat induces reactive oxygen species and DNA damage in acute myeloid leukemia cells. PLoS One. 2011, 6: e20987-10.1371/journal.pone.0020987.PubMed CentralView ArticlePubMedGoogle Scholar
  40. Namdar M, Perez G, Ngo L, Marks PA: Selective inhibition of histone deacetylase 6 (HDAC6) induces DNA damage and sensitises transformed cells to anticancer agents. Proc Natl Acad Sci USA. 2010, 107: 20003-8. 10.1073/pnas.1013754107.PubMed CentralView ArticlePubMedGoogle Scholar
  41. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M: Biogrid: A general repository for interaction datasets. Nucleic Acids Res. 2006, 34: D535-9. 10.1093/nar/gkj109.PubMed CentralView ArticlePubMedGoogle Scholar
  42. Berger AH, Knudson AG, Pandolfi PP: A continuum model for tumour suppression. Nature. 2011, 476: 163-9. 10.1038/nature10275.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Kondrashov FA, Koonin EV: A common framework for understanding the origin of genetic dominance and evolutionary fates of gene duplications. Trends Genet. 2004, 20: 287-90. 10.1016/j.tig.2004.05.001.View ArticlePubMedGoogle Scholar
  44. Papp B, Pál C, Hurst LD: Dosage sensitivity and the evolution of gene families in yeast. Nature. 2003, 424: 194-7. 10.1038/nature01771.View ArticlePubMedGoogle Scholar
  45. Feeling M, Thomas BC: Gene-balanced duplications, like tetraploidy, provide predictable drive to increase morphological complexity. Genome Res. 2006, 16: 805-14. 10.1101/gr.3681406.View ArticleGoogle Scholar
  46. Birchler JA, Veitia RA: Gene balance hypothesis: connecting issues of dosage sensitivity across biological disciplines. Proc Natl Acad Sci USA. 2012, 109: 14746-53. 10.1073/pnas.1207726109.PubMed CentralView ArticlePubMedGoogle Scholar
  47. Springer M, Weissman JS, Kirschner MW: A general lack of compensation for gene dosage in yeast. Mol Syst Biol. 2010, 6: 368-PubMed CentralView ArticlePubMedGoogle Scholar
  48. Gamazon ER, Huang RS, Dolan ME, Cox NJ: Copy number polymorphisms and anticancer pharmacogenomics. Genome Biol. 2011, 12: R46-10.1186/gb-2011-12-5-r46.PubMed CentralView ArticlePubMedGoogle Scholar
  49. Fitzgerald JB, Schoeberl B, Nielsen UB, Sorger PK: Systems biology and combination therapy in the quest for clinical efficacy. Nat Chem Biol. 2006, 2: 458-66. 10.1038/nchembio817.View ArticlePubMedGoogle Scholar
  50. O'Brien KP, Remm M, Sonnhammer ELL: Inparanoid: a comprehensive database of eukaryotic orthologs. Nucleic Acids Res. 2005, 33: D476-480.PubMed CentralView ArticlePubMedGoogle Scholar
  51. Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL: NCBI BLAST: a better web interface. Nucleic Acids Res. 2008, 36: W5-W9. 10.1093/nar/gkn201.PubMed CentralView ArticlePubMedGoogle Scholar
  52. Al-Shahrour F, Diaz-Uriarte R, Dopazo J: FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics. 2004, 20: 578-580. 10.1093/bioinformatics/btg455.View ArticlePubMedGoogle Scholar
  53. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2011, 38: D355-60.View ArticleGoogle Scholar
  54. Ball CA, Jin H, Sherlock G, Weng S, Matese JC, Andrada R, Binkley G, Dolinski K, Dwight SS, Harris MA, Issel-Tarver L, Schroeder M, Botstein D, Cherry JM: Saccharomyces Genome Database provides tools to survey gene expression and functional analysis data. Nucleic Acids Res. 2001, 30: 69-72.Google Scholar
  55. Ghaemmaghami S, Huh WK, Bower K, Howson RW, Belle A, Dephoure N, O’Shea EK, Weissman JS: Global analysis of protein expression in yeast. Nature. 2003, 425: 737-41. 10.1038/nature02046.View ArticlePubMedGoogle Scholar
  56. Haase SB, Reed SI: Improved flow cytometric analysis of the budding yeast cell cycle. Cell Cycle. 2002, 1: 132-6.View ArticlePubMedGoogle Scholar
  57. Nurse P, Thuriaux P, Nasmyth K: Genetic control of the cell division cycle in the fission yeast Schizosaccharomyces pombe. Mol Gen Genet. 1976, 146: 167-78. 10.1007/BF00268085.View ArticlePubMedGoogle Scholar
  58. Fadok VA, Bratton DL, Frasch SC, Warner ML, Henson PM: The role of phosphatidylserine in recognition of apoptotic cells by phagocytes. Cell Death Differ. 1998, 5: 551-62. 10.1038/sj.cdd.4400404.View ArticlePubMedGoogle Scholar

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© de Clare and Oliver; licensee BioMed Central Ltd. 2013

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