Open Access

Selective inhibition of yeast regulons by daunorubicin: A transcriptome-wide analysis

  • Marta Rojas1,
  • Marta Casado1,
  • José Portugal1 and
  • Benjamin Piña1Email author
BMC Genomics20089:358

DOI: 10.1186/1471-2164-9-358

Received: 11 April 2008

Accepted: 30 July 2008

Published: 30 July 2008

Abstract

Background

The antitumor drug daunorubicin exerts some of its cytotoxic effects by binding to DNA and inhibiting the transcription of different genes. We analysed this effect in vivo at the transcriptome level using the budding yeast Saccharomyces cerevisiae as a model and sublethal (IC40) concentrations of the drug to minimise general toxic effects.

Results

Daunorubicin affected a minor proportion (14%) of the yeast transcriptome, increasing the expression of 195 genes and reducing expression of 280 genes. Daunorubicin down-regulated genes included essentially all genes involved in the glycolytic pathway, the tricarboxylic acid cycle and alcohol metabolism, whereas transcription of ribosomal protein genes was not affected or even slightly increased. This pattern is consistent with a specific inhibition of glucose usage in treated cells, with only minor effects on proliferation or other basic cell functions. Analysis of promoters of down-regulated genes showed that they belong to a limited number of transcriptional regulatory units (regulons). Consistently, data mining showed that daunorubicin-induced changes in expression patterns were similar to those observed in yeast strains deleted for some transcription factors functionally related to the glycolysis and/or the cAMP regulatory pathway, which appeared to be particularly sensitive to daunorubicin.

Conclusion

The effects of daunorubicin treatment on the yeast transcriptome are consistent with a model in which this drug impairs binding of different transcription factors by competing for their DNA binding sequences, therefore limiting their effectiveness and affecting the corresponding regulatory networks. This proposed mechanism might have broad therapeutic implications against cancer cells growing under hypoxic conditions.

Background

Understanding the mode of action of antitumor drugs is considered an absolute prerequisite for the advancement on the design of new drugs. It is generally believed that antitumor activity is mediated by the capacity of certain drugs to induce DNA damage and trigger apoptosis. However, there are many indications that this mechanism, whatever relevant may it be, does not account for all therapeutic effects of some antitumor drugs [1, 2].

The anthracycline antibiotic daunorubicin is widely used in cancer chemotherapy [3]. It accumulates in the nuclei of living cells and intercalates into DNA quantitatively [4, 5], a property associated to some of the most relevant effects of the drug: inhibition of DNA replication and gene transcription [1, 6, 7], displacement of protein factors from the transcription complex [8] and topoisomerase II poisoning [9]. Daunorubicin has the property of arresting cell growth at drug concentrations not sufficient for promoting noticeable DNA damage, and through mechanisms that differ from the apoptotic pathway [7]. These findings impelled to define new mechanisms of daunorubicin antiproliferative activity at clinically relevant concentrations.

Daunorubicin shows remarkable sequence specificity for 5'-WCG-3' DNA tracts [10]. This property has led to the suggestion that daunorubicin may compete with transcription factors with overlapping recognition sites for binding to DNA. This model would explain several effects of daunorubicin, such as inhibition of RNA polymerase II [1, 6, 7] and the suppression of the co-ordinate initiation of DNA replication in Xenopus oocyte extracts [11].

To test the capacity of daunorubicin to displace key transcription factors from their binding sites in chromatin in vivo, and, therefore, to inhibit their action [6], we used the yeast Saccharomyces cerevisiae as a model. In a previous work [12], we showed that yeast strains deficient in ergosterol synthesis (Δerg6 strains) are particularly sensitive to daunorubicin, overcoming one of the main setbacks to the use of yeast in pharmacological studies, which is their resistance to many anti-tumour drugs [13, 14].

We demonstrated that daunorubicin treatment in Δerg6 cells precluded activation of several genes required for galactose utilization (GAL genes) and, consequently, treated cells were unable to growth in galactose. This effect was related to the presence of CpG steps in the cognate DNA binding sequence of Gal4p, the key transcription factor for activation of GAL genes [12, 15]. The present work aims to extend this type of analysis to the totality of the yeast genome, in order to assess the generality of this model.

Results

Effects of daunorubicin on the yeast transcriptome

The effects of daunorubicin on the yeast transcriptome were studied after 1 h and 4 h of treatment (Figure 1). The results indicate a general inhibitory effect of daunorubicin at both time points, as down regulated genes predominate over up regulated ones, and this trend was especially significant when considering genes whose expression changed by more than four-fold (lines "4X" and "0.25X" in Figure 1). Multi-array analysis of the expression changes in the whole dataset confirmed these trends. ANOVA analysis of normalized data showed statistically significant differences in expression upon daunorubicin treatment for 475 genes (14%) at least in one of the time points analysed. Affected genes were grouped in four clusters by a Self-Organising Maps (SOM) algorithm, according to their differential expression at the three time points analysed (Figure 2, list of genes for each cluster in Table 1). Clusters A to C (280 genes in total) corresponded to genes whose transcription decreased upon daunorubicin treatment, whereas all genes that became activated by the treatment (195 genes) were grouped in Cluster D. Genes in Clusters C and D showed very little or no difference in expression between one and four hours of treatment (see the horizontal median line in the corresponding plots between time points 1 h and 4 h in Figure 2), whereas genes in Cluster A were the only ones in which the effect (an inhibition, in this case) after four hours of treatment was significantly stronger than the observed after one hour (Figure 2). Cluster B, consisting only in three genes, was the only one in which the effect was stronger at one hour than at four hours. Our data thus indicated that most daunorubicin-related changes in gene expression were already significant after only one hour of treatment and that these effects either increased or remained stable after four hours for essentially all analysed genes.
Table 1

Gene clusters defined by SOM analysis

Cluster A

Cluster B

Cluster C

Cluster D

AAH1

GPI12

PPM1

YDR428C

URA2

ACT1

ACC1

RPC31

YBL051C

YMR074C

AAT2

GPM1

PRB1

YDR453C

YJU3

ARG8

ANB1

RPC40

YBL057C

YMR085W

ACO1

GPM2

PRY1

YDR516C

YML056C

ARO4

ARL1

RPG1

YBR012W-B

YMR130W

ADE12

GRE2

PRY3

YDR539W

 

AYR1

BFR1

RPL13B

YCL019W

YMR158C-B

ADE17

GRE3

PSA1

YFR017C

 

CAR2

CAF20

RPL32

YCR082W

YNL054W-B

ADH1

GSF2

PST1

YGL121C

 

CDC91

CBF5

RPL34B

YDL076C

YNL296W

ADH2

GSY2

RAD51

YGL157W

 

DAK1

CCT5

RPL6A

YDL157C

YNR046W

ADH5

GTT1

RHR2

YGP1

 

ERG10

CDC20

RPL6B

YDL166C

YOL026C

ALD4

GYP7

RIB1

YGR045C

 

FAS1

CDC33

RPN10

YDR034C-D

YOL092W

ALD6

HHO1

RIB4

YGR161C

 

GDH1

CDC60

RPO26

YDR060W

YOL124C

AMS1

HMT1

RIP1

YHL021C

 

GLT1

COP1

RPS11B

YDR084C

YOR021C

ARA1

HOR2

RME1

YHM1

 

NUP82

CPR6

RPS19A

YDR098C-B

YOR262W

ARG1

HSP104

RNR1

YHR087W

 

PFK1

DIB1

RPS26A

YDR154C

YOR343C-A

ARG4

HSP12

SCM4

YIL011W

 

PHB1

DPB4

RPS4B

YDR210C-D

YOR343C-B

ARG5

HSP26

SCS7

YIL056W

 

PYC2

DST1

RPS8A

YDR210W-D

YOR382W

ARO3

HSP42

SCW11

YIL077C

 

QCR10

FCY1

RPT3

YDR261C-D

YPL199C

ASH1

HXK1

SDS24

YJL016W

 

QCR2

FKB2

RRP4

YDR261W-B

YPL225W

BAP2

HXK2

SGE1

YJL094C

 

RFC5

FPR1

RRP5

YDR316W-B

YPR137C-B

BAP3

HXT1

SHM2

YJR008W

 

RNR4

FRQ1

RRP9

YDR361C

YPR158W-B

BAT2

HXT2

SNO1

YKL151C

 

STI1

HCH1

RRS1

YDR365W-B

YPS7

CAP2

IDH1

SNQ2

YKR067W

 

STP3

HIR1

RSC6

YDR449C

YPT31

CBP4

IDH2

SNZ1

YLL012W

 

TEF1

HIS7

RVB2

YER007C-A

 

CHA1

ILV5

SPI1

YLR110C

 

TKL1

HRP1

SAS10

YER092W

 

CHS1

INO1

SRL3

YLR111W

 

TSA1

HRR25

SBH1

YER126C

 

CIT1

IPT1

SRY1

YLR122C

 

TTR1

HRT1

SEC21

YER138C

 

CLN2

IRA2

SSA1

YLR231C

 

UGA1

ILS1

SEC65

YER160C

 

COQ1

KNS1

SSA2

YLR331C

 

URA4

IMP4

SEC72

YER183C

 

COS1

LAP4

SSD1

YLR352W

 

YBR070C

KAP123

SER3

YFH1

 

COS7

LSC2

SUN4

YLR414C

 

YDR214W

KRI1

SES1

YFL002W-A

 

COX20

MCR1

TAT2

YLR454W

 

YDR476C

KRR1

SIT1

YFL004W

 

CPA1

MDH1

TDH1

YML128C

 

YER134C

LOS1

SKP1

YGR038C-B

 

CTS1

MDH2

TDH2

YMR090W

 

YER182W

LYS7

SMD3

YGR081C

 

CYC3

MEP1

TDH3

YMR173W-A

 

YGL047W

MGM101

SNF8

YGR161W-B

 

CYT1

MEP3

THO1

YMR181C

 

YGR201C

NAT3

SNT309

YHR052W

 

DDR2

MET6

TIR2

YMR315W

 

YHR049W

NIP7

SPB1

YHR214C-B

 

DDR48

MMD1

TPI1

YNL200C

 

YIL087C

NMD3

SPE3

YHR214C-C

 

DED1

MOG1

TPS2

YNL212W

 

YIR035C

NOP12

SPE4

YIL127C

 

DYN1

MRPL35

TRR2

YOL101C

 

YLL023C

NOP58

SSF1

YJR027W

 

EHT1

MSF1'

TSL1

YOR009W

 

YLR112W

NPI46

SSP120

YJR029W

 

ENO1

MTF2

TUF1

YOR022C

 

YLR356W

NPT1

STS1

YKL014C

 

ENO2

NCE102

UGP1

YOR062C

 

YMR178W

NRD1

SUI1

YKL054C

 

ERG11

NCR1

URA1

YOR081C

 

YNL100W

OLI1

SUI2

YKR081C

 

ERG26

OAC1

UTR2

YOR258W

 

YNL305C

OST3

SXM1

YKT6

 

ERG5

OPI3

VAP1

YOR280C

 

YPL101W

PCL1

TIF11

YLR009W

 

ERG6

PBI2

VID24

YOR289W

 

YPR098C

PFS2

TIF34

YLR035C-A

 

EXG1

PCL7

YAL053W

YOR338W

 

YSA1

PHO11

TIF35

YLR065C

 

FBA1

PDC1

YBL049W

YPL004C

  

PHO12

TIP1

YLR106C

 

FUN14

PDC5

YBL064C

YPL066W

  

PRE10

TPM1

YLR157C-B

 

GCV1

PDH1

YBR006W

YPL134C

  

PRE2

TPM2

YLR159W

 

GCV2

PDR5

YBR053C

YPL156C

  

PRE3

TRP1

YLR221C

 

GCY1

PET8

YBR230C

YPR153W

  

PRE9

UBA1

YLR227W-B

 

GLK1

PEX11

YDC1

YPR172W

  

PUP2

UBC1

YLR410W-B

 

GLO1

PGK1

YDL124W

YRA1

  

RDI1

UBC13

YML039W

 

GLY1

PGM2

YDR041W

YTP1

  

RLP7

UBC4

YML093W

 

GND1

PHO3

YDR233C

ZRT1

  

RNA14

UBC6

YML125C

 

GPA2

PIR1

YDR319C

ZRT2

  

RNH70

URA5

YMR045C

 

GPD2

PLB1

YDR387C

   

RPA49

VAR1

YMR046W-A

 

GPH1

PPA2

YDR391C

   

RPC10

YBL005W-B

YMR050C

 
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-358/MediaObjects/12864_2008_Article_1551_Fig1_HTML.jpg
Figure 1

Effects of daunorubicin to the yeast transcriptome. Expression data from treated and untreated cells (expressed as binary logs) were compared before and after one and four hours of incubation with daunorubicin. Data are represented as log2 of the ratios of gene expression values after 1 h (left) and 4 h (right) of daunorubicin treatment versus the initial values (Time 0). Only genes whose expression was significantly altered by the treatment (T-test, brown dots, p < 10-5, yellow squares, p < 10-2) are shown. Discontinuous lines in the plots indicate the calculated positions of genes changed by 4-, 2-, 0.5- and 0.25-fold; they are included as references to compare with the changes in expression of different genes.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-358/MediaObjects/12864_2008_Article_1551_Fig2_HTML.jpg
Figure 2

Transcriptional profiles for genes classified into clusters by SOM. Data are shown as logarithmic values of the ratio of fluorescence between treated and untreated cells before (0 h) and 1 and 4 hours after treatment. No correction was performed to compensate differences in labelling or detection of the two fluorochromes. The thick solid lines in the middle of the graphs correspond to median values, coloured areas correspond to the intervals between 1st and 3rd quartiles (dark gray) and the total distribution (light gray). Averaged values for Cluster B (3 genes, discontinuous line) in included in the Cluster A plot.

Gene Ontology (GO) analysis of genes activated and repressed by daunorubicin treatment showed a very different distribution of GO categories for both groups. Up-regulated genes fell into three main functional categories: Genes related to ribosome assembly and metabolism, Ty transposition, and proteolytic processes (Table 2). Whereas the two last categories may indicate a certain level of stress, up regulation of ribosome assembling-related genes usually correlates with a positive effect in cell growth. In contrast, GO analysis of genes down regulated by daunorubicin showed a general decrease of energy-producing metabolism, including genes involved in fermentation and in the tricarboxylic acid cycle. A significant proportion of down-regulated genes appeared involved in the metabolism of nitrogen compounds, including amino acids (Table 3). The dissociation between expression of ribosomal and glycolytic genes upon daunorubicin treatment can be observed in Figure 3, which shows up-regulation of most ribosomal protein genes and down-regulation of sugar and alcohol-metabolism related genes at one and four hours of daunorubicin treatment. Figure 4 shows a scheme of the glycolytic pathway, highlighting genes down regulated by daunorubicin. These genes codify the enzymes responsible for no less than 9 consecutive steps of the pathway. Therefore, the data suggests that the fermentation capacity should be depressed in daunorubicin-treated yeast cells.
Table 2

GO Term finder results for genes up-regulated by daunorubicin

Gen Ontology Term clustering

Functional categories

GOID

GOID- associated functions

 

A

32196; 32197

Transposition, Ty metabolism

 

B

27; 460; 466; 6364; 6396; 6996; 16043; 16070; 16072; 22613; 22618; 42254; 42255; 42257; 42273; 43170; 65003

Ribosome assembling (Protein and rRNA) Proteolysis. Ubiquitin-

 

C

6508; 6511; 19941; 30163; 43632; 44257; 51603

mediated preoteolysis.

 

Gene Clustering

   

Distribution among functional categories

Genes

Main gene functions

Number of genes

A only

FCY1; FRQ1; HIS7; PCL1; PHO11; SER3; SIT1; SPE3; SPE4; TRP1; URA5; YBL005W-B; YBR012W-B; YCL019W; YDR034C-D; YDR098C-B; YDR210C-D; YDR261C-D; YDR261W-B; YDR316W-B; YDR365W-B; YER138C; YER160C; YFL002W-A; YGR038C-B; YGR161W-B; YHR214C-B; YHR214C-C; YJR027W; YJR029W; YLR035C A; YLR157C-B; YLR227W-B; YLR410W-B; YML039W; YMR045C; YMR050C; YNL054W-B; YPR137C-B; YPR158W-B

Ty genes

40

B only

ACC1; ANB1; ARL1; BFR1; CAF20; CBF5; CCT5; CDC33; CDC60; COP1; CPR6; DIB1; DPB4; DST1; FPR1; HCH1; HIR1; HRP1; HRR25; ILS1; IMP4; KAP123; KRI1; KRR1; LOS1; MGM101; NAT3; NIP7; NMD3; NOP12; NOP58; NPT1; NRD1; OST3; PFS2; RDI1; RLP7; RNA14; RNH70; RPA49; RPC10; RPC31; RPC40; RPG1; RPL13B; RPL32; RPL34B; RPL6A; RPL6B; RPO26; RPS11B; RPS19A; RPS26A; RPS4B; RPS8A; RRP4; RRP5; RRP9; RRS1; RSC6; RVB2; SAS10; SEC21; SEC65; SEC72; SES1; SMD3; SNT309; SPB1; SSF1; SSP120; SUI1; SUI2; SXM1; TIF11; TIF34; TIF35; TIP1; TPM1; TPM2; UBA1; UBC13; YFH1; YIL127C; YKT6; YNL296W; YOR021C; YPT31

Ribosomal protein genes, rRNA metabolism, translation.

87

C>B

CDC20; HRT1; PRE10; PRE2; PRE3; PRE9; PUP2; RPN10; RPT3; SKP1; SNF8; STS1; UBC1; UBC4; UBC6

Endopeptidases, ubiquitin-protein ligases

15

No GO Term

  

53

Table 3

GO Term finder results for genes down-regulated by daunorubicin

Gen Ontology Term clustering

Functional categories

GOID

GOID- associated functions

 

A

5975; 5996; 6006; 6007; 6066; 6067; 6082; 6090; 6094; 6096; 6113; 6766; 6767; 9056; 9063; 15980; 16051; 16052; 19318; 19319; 19320; 19752; 32787; 44248; 44262; 44275; 46164; 46165; 46364; 46365;

Alcohol and carbohydrate metabolism (including glycolysis). Vitamin and organic acid metabolism.

 

B

6091; 6099; 6100; 6519; 6520; 6536; 6537; 6807; 8652; 9064; 9084; 9308; 9309; 44271; 46356

Amino acid metabolic process. Tricarboxilic acid cycle.

 

Gene Clustering

Distribution among functional categories

Genes

Main gene functions

Number of genes

A>>B

GPD2; PDC1; PDC5; PCL7; UGP1; DAK1; GLO1; INO1; PGM2; MDH2; PSA1; GRE3; GCY1; GLK1; TPI1; HXK1; HXK2; PFK1; VID24; GND1; TKL1; PYC2; PGK1; TDH3; ENO1; ENO2; TDH1; TDH2; FBA1; GPM1

Glycolysis

30

A>B

AAH1; ADH1; ADH2; ADH5; ALD4; ALD6; AMS1; ARA1; AYR1; CTS1; EHT1; ERG10; ERG11; ERG26; ERG5; EXG1; FAS1; GPH1; GSY2; HOR2; LAP4; MDH1; PDH1; PEX11; PHO3; PRB1; RHR2; RIB1; RIB4; SCS7; SNO1; SNZ1; TPS2; TSL1

Alcohol, lipid and sterol metabolism

34

A ≈ B

AAT2; BAT2; CAR2; CHA1; COX20; GCV1; GCV2; GLY1; LSC2; MCR1; PPA2; QCR10; QCR2; RIP1; SRY1; UGA1

Amino acid metabolism. Respiration

16

A<B

ACO1; ARG1; ARG4; ARG8; ARO3; ARO4; CIT1; CPA1; CYT1; GDH1; GLT1; IDH1; IDH2; ILV5; MEP1; MEP3; MET6; URA2

Nitrogen compound (including amino acids) metabolism. Tricarboxilic acid cylce

18

No GO term

  

181

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-358/MediaObjects/12864_2008_Article_1551_Fig3_HTML.jpg
Figure 3

Transcriptional rate changes for Ribosomal Protein genes (solid dots) and Glycolytic genes (diamonds) after 1 (Y-axis) and 4 h (X-axis) of daunorubicin treatment. Data are expressed as logarithmic values of expression ratios between treated and untreated cells.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-358/MediaObjects/12864_2008_Article_1551_Fig4_HTML.jpg
Figure 4

Scheme of the glycolytic pathway. Genes codifying for the enzymes implicated in each step are detailed; green labels indicate genes whose expression was reduced upon daunorubicin treatment.

The effects of daunorubicin treatment in gene expression of 15 selected genes were validated by qRT-PCR (list of genes and primers in Table 4, results in Table 5). The results, presented as ratios between treated and untreated cells at 0 h and 4 h of treatment, include data from up to 5 biological replicates, showed a general good agreement with microarray data. Most (8 out of 9) sugar and alcohol-metabolism related genes showed a 2 to 4 fold decrease on expression of after 4 h of treatment, a behaviour comparable to the one observed in the microarray analysis. Similarly, two out of the three amino acid metabolism genes analysed showed a 3 to 4 fold decrease on expression. In contrast, a small, but significant, increase on the expression of the ribosomal protein genes RPS28A was also observed, also in agreement with the general trend observed for ribosomal-protein genes in the microarray data. We added to this analysis the heat-shock protein HSP26, as a representative of a small group of HSP genes (HSP12, HSP26, HSP42 and HSP104) with appeared down regulated by daunorubicin in the microarray analysis (Table 1). These results were corroborated by qRT-PCR quantitation, which showed 8-fold reduction of HSP26 transcription after four hours of daunorubicin treatment (Table 5). These results confirmed the general decrease in genes related with glucose utilisation while transcription of ribosomal protein gene was either not affected or slightly increased.
Table 4

Primers used in this study

GENE

Primer Sequence

Function

ACO1

for: 5'-GTGGTGCTGATGCCGTTG-3'

Aconitase

 

rev: 5'-CCTTCAATTCCCATGGACGA-3'

 

ACT1

for: 5'-TGTGTAAAGCCGGTTTTGCC-3'

Actin

 

rev: 5'-TTGACCCATACCGACCATGAT-3'

 

ARG1

for: 5'-GCCCACATTTCTTACGAGGC-3'

Arginosuccinate synthetase

 

rev: 5'-TGGTCCGGAGCATCCATT-3'

 

ARG4

for: 5'-AAATTTGTCCGTCATCCAAACG-3'

Argininosuccinate lyase

 

rev: 5'-CCGGTGTGGACTTTACCAGC-3'

 

CAR2

for: 5'-CATCGCCCAATTGAAAGCTC-3'

L-ornithine transaminase

 

rev: 5'-CCTTGGATGGGTCGATTACG-3'

 

CDC19

for: 5'-TGGCCATTGCTTTGGACAC-3'

Pyruvate kinase

 

rev: 5'-GGTGAAGATCATTTCGTGGTTTG-3'

 

FBA1

for: 5'-AATGCTTCCATCAAGGGTGC-3'

Fructose 1,6-bisphosphate aldolase

 

rev: 5'-CAACTGGGATACCGTAAGCTG-3'

 

GPM1

for: 5'-TCACCGGTTGGGTTGATGTTA-3'

Glycerate Phosphomutase

 

rev: 5'-TCCTTCAACAATTCACCGGC-3'

 

HSP26

for: 5'-AGAGGCTACGCACCAAGACG-3'

Heat Shock Protein

 

rev: 5'-AGAATCCTTTGCGGGTGTGT-3'

 

HXK1

for: 5'-GTTGACAGCGAGACCTTGAGAA-3'

Hexokinase isoenzyme 1

 

rev: 5'-CAACCGGGAATCATTGGAAT-3'

 

PGI1

for: 5'-CTCAAAGAACTTGGTCAACGAT-3'

Phosphoglucoisomerase

 

rev: 5'-CAAACCGGTGACGTTAGCCT-3'

 

PGK1

for: 5'-CCCAGGTTCCGTTCTTTTGTTG-3'

3-phosphoglycerate kinase

 

rev: 5'-TTGACCATCGACCTTTCTGGA-3'

 

RPO21

for: 5'-AGGTTTGCTGCAATTTGGACTT-3'

RNA polymerase II largest subunit B220

 

rev: 5'-CAACCTCCCCTTGATACGAGC-3'

 

RPS28A

for: 5'-AGCCAAGGTCATCAAAGTTTTAGG-3'

Ribosomal Protein of the Small subunit

 

rev: 5'-TTCCAAGAATTCGACACGGAC

 

TDH(1-3)

for: 5'-AGACTGTTGACGGTCCATCCC-3'

Glyceraldehyde-3-phosphate dehydrogenase

 

rev: 5'-AAGCGGTTCTACCACCTCTCC-3'

 

HOR2

for: 5'-GTGCAACGCTTTGAACGCT-3'

Glicerol-1-phosphatase

 

rev: 5'-GAAGTTGCCACAGCCCATTT-3'

 

TPS2

for: 5'-TCATGCCCCATGGCCTAGTA-3'

Trehalose-6-phosphate phosphatase

 

rev: 5'-TTTCTACGTGGCAAACAACGAA-3'

 

GLO1

for: 5'-AGGATCCAGCAAGGACCGTT-3'

Glyoxalase

 

rev: 5'-GCTTCATACCGAAGTGTTCGG-3'

 
Table 5

Differential expression in daunorubicin-treated versus non-treated cells, measured by RT-qPCR

 

Treated/Non treateda)

    

Function

ORF

Time 0

Time 4 h

Fold variation

(4 h/0 h)

p b)

Corrected p

(Bonferroni)

n

(technical replicates)

n

(biological replicates)

 

ACO1

0.001

-1.090

0.470

0.001

0.020

60

5

 

CDC19

0.034

-1.132

0.446

6.3 × 10-13

9.5 × 10-12

108

5

 

FBA1

0.005

-1.207

0.432

1.0 × 10-13

1.5 × 10-12

60

5

 

GPM1

-0.005

-0.948

0.520

3.0 × 10-8

4.5 × 10-7

60

5

Energy metabolism

HOR2

-0.010

-1.413

0.378

9.0 × 10-4

0.014

24

2

 

HXK1

0.315

-1.935

0.210

1.6 × 10-21

2.5 × 10-20

72

5

 

PGI1

0.005

-0.061

0.956

0.80

> 0.05

60

5

 

PGK1

0.005

-1.228

0.425

8.9 × 10-19

1.3 × 10-17

60

5

 

TDH

-0.015

-1.428

0.375

5.9 × 10-12

8.8 × 10-11

60

5

 

ARG1

-0.010

-2.032

0.246

2.1 × 10-7

3.2 × 10-6

24

2

Amino acid metabolism

ARG4

-0.001

-1.413

0.376

5.3 × 10-6

8.0 × 10-5

23

2

 

CAR2

-0.011

-0.294

0.822

0.09

> 0.05

35

3

 

ACT1

-0.480

-1.440

0.514

0.126

> 0.05

8

3

Others

HSP26

0.081

-2.921

0.125

5.1 × 10-8

7.6 × 10-7

24

2

 

RPS28A

-0.005

0.476

1.396

0.002

0.028

60

5

 

TPS2

0.002

0.120

1.086

0.42

> 0.05

22

2

a) Data expressed as dual logarithmic values of expression ratios, treated versus untreated. Corrected by RPO21 expression.

b) Student's T-Test, time 0 versus time 4 h ratios

Identification of transcription factors associated to daunorubicin-repressed genes

Transcription factors reported to bind to the promoters of daunorubicin-repressed genes were identified using the on-line bioinformatics tools available at the YEASTRACT web page (http://www.yeastract.com/, [16]). From the 170 transcription factors included in the YEASTRACT database, 32 of them were found to bind to daunorubicin-repressed gene promoters in a significantly higher proportion than expected only by chance (Table 6). The table indicates the total number of genes associated to each transcription factor present in the whole dataset (that is, the 3458 ORF analysed), the number of these genes showing down-regulation by daunorubicin, the expected number by a random distribution (over 280 down regulated genes) and the "enrichment factor", that is, the ratio between observed and expected absolute frequencies for each factor.
Table 6

Transcription factors preferently associated to DNR-inhibited genes

Factor

Total regulated genesa)

DNR-down regulated genes

p

  

Observed

Expected (out of 280)

Observed/Expected

Hypergeometric

Bonferroni

Sok2p

561

118

45.45

2.6

5.6 × 10-27

7.2 × 10-25

Msn2p

316

72

25.58

2.8

2.0 × 10-17

2.6 × 10-15

Msn4p

286

67

23.13

2.9

8.3 × 10-17

1.1 × 10-14

Gis1p

91

35

7.35

4.8

1.5 × 10-16

1.9 × 10-14

Cst6p

104

36

8.44

4.3

4.0 × 10-15

5.1 × 10-13

Pdr3p

84

29

6.8

4.3

2.4 × 10-12

3.1 × 10-10

Yap1p

1025

133

83

1.6

2.1 × 10-11

2.8 × 10-9

Met4p

746

105

60.42

1.7

8.8 × 10-11

1.1 × 10-8

Adr1p

148

36

11.97

3.0

3.6 × 10-10

4.6 × 10-8

Xbp1p

84

26

6.8

3.8

5.3 × 10-10

6.9 × 10-8

Rox1p

202

44

16.33

2.7

6.2 × 10-10

7.9 × 10-8

Aft1p

397

66

32.11

2.1

9.5 × 10-10

1.2 × 10-7

Crz1p

155

37

12.52

3.0

1.4 × 10-9

1.8 × 10-7

Pdr1p

205

42

16.6

2.5

3.9 × 10-9

5.1 × 10-7

Skn7p

215

44

17.42

2.5

5.4 × 10-9

7.0 × 10-7

Gcn4p

309

54

25.04

2.2

7.8 × 10-9

1.0 × 10-6

Stp2p

131

32

10.61

3.0

1.5 × 10-8

2.0 × 10-6

Hsf1p

266

48

21.5

2.2

5.2 × 10-8

6.7 × 10-6

Mig1p

74

21

5.99

3.5

1.1 × 10-7

1.4 × 10-5

Ino2p

81

22

6.53

3.4

1.2 × 10-7

1.6 × 10-5

Gcr2p

97

25

7.89

3.2

2.8 × 10-7

3.6 × 10-5

Mga1p

151

31

12.25

2.5

4.6 × 10-7

5.9 × 10-5

Mbp1p

242

42

19.59

2.1

4.6 × 10-7

5.9 × 10-5

Rfx1p

87

23

7.08

3.2

6.0 × 10-7

7.7 × 10-5

Stp1p

91

23

7.35

3.1

1.1 × 10-6

1.4 × 10-4

Rtg3p

108

24

8.71

2.8

1.9 × 10-6

2.4 × 10-4

Swi4p

302

47

24.49

1.9

2.5 × 10-6

3.3 × 10-4

Rgt1p

44

14

3.54

4.0

2.9 × 10-6

3.7 × 10-4

Ino4p

333

50

26.94

1.9

3.1 × 10-6

4.0 × 10-4

Sut1p

34

12

2.72

4.4

4.1 × 10-6

5.3 × 10-4

Gat4p

64

18

5.17

3.5

4.5 × 10-6

5.8 × 10-4

Nrg1p

168

31

13.61

2.3

4.7 × 10-6

6.1 × 10-4

a) Number of genes associated to each factor, following YEASTRACT. Only genes used in the microarray analysis (3458) were considered.

Some of these factors (Yap1p, Msn2p, Msn4p) are intimately related to stress response, whereas others, such as Gcr2p, Adr1p, Mig1p and Rgt1p, are associated to carbohydrate and alcohol metabolism. In addition, Gcn4p and Met4p are known regulators of amino acids biosynthetic pathways. In this regard, the transcription factor list recapitulates the functional distribution of daunorubicin down regulated genes in Table 3. Fourteen transcription factors showed enrichment factors over 3 fold, indicating that their associated genes were found in the daunorubicin down regulated dataset at 3 to 5 times higher frequencies than expected (Table 7). Many of these factors are known regulators of glycolytic genes, such as Rgt1p, Mig1p, Gcr2p or Adr1p; therefore, their inclusion in the list may merely reflect the general decrease of transcription of the regulated genes. In addition, this list includes a strikingly high proportion (10 out 14) of transcription factors encompassing CpG steps in their DNA binding sites, irrespectively their relationship with the glycolytic pathway. This observation is consistent with a preferential effect of daunorubicin on the expression of genes regulated by transcription factors with CpG steps in their DNA recognition sequences, in keeping with previous results [8]. This specific inhibition of transcriptional activation by daunorubicin suggests that it may compete with some transcription factors for DNA binding in CpG-reach sequences in gene promoters.
Table 7

Transcription factors selectively enriched in daunorubicin-down regulated gene promoters

Factor

Found/expected

p a)

Binding sequences

CpG steps

Characteristics/Function

Gis1p

4.76

1.9 × 10-14

TWAGGGAT, AGGGG

 

JmjC domain-containing histone demethylase; transcription factor involved in the expression of genes during nutrient limitation; also involved in the negative regulation of DPP1 and PHR1

Sut1p

4.41

5.3 × 10-4

CGCG

*

Transcription factor of the Zn [II]2Cys6 family involved in sterol uptake; involved in induction of hypoxic gene expression

Cst6p

4.27

5.1 × 10-13

TGACGTCA, TTACGTAA

*

Basic leucine zipper (bZIP) transcription factor of the ATF/CREB family, activates transcription of genes involved in utilization of non-optimal carbon sources; involved in telomere maintenance

Pdr3p

4.26

3.1 × 10-10

TCCGCGGA

*

Transcriptional activator of the pleiotropic drug resistance network, regulates expression of ATP-binding cassette (ABC) transporters through binding to cis-acting sites known as PDREs (PDR responsive elements)

Rgt1p

3.95

3.7 × 10-4

CGGANNA

*

Glucose-responsive transcription factor that regulates expression of several glucose transporter (HXT) genes in response to glucose; binds to promoters and acts both as a transcriptional activator and repressor

Xbp1p

3.82

6.9 × 10-8

GCCTCGARMGA

*

Transcriptional repressor that binds to promoter sequences of the cyclin genes, CYS3, and SMF2; expression is induced by stress or starvation during mitosis, and late in meiosis; member of the Swi4p/Mbp1p family; potential Cdc28p substrate

Mig1p

3.51

1.4 × 10-5

W(4-5)GCGGGG

*

Transcription factor involved in glucose repression; sequence specific DNA binding protein containing two Cys2His2 zinc finger motifs; regulated by the SNF1 kinase and the GLC7 phosphatase

Gat4p

3.48

5.8 × 10-4

GATA

 

Protein containing GATA family zinc finger motifs

Ino2p

3.37

1.6 × 10-5

WYTTCAYRTGS

*

Component of the heteromeric Ino2p/Ino4p basic helix-loop-helix transcription activator that binds inositol/choline-responsive elements (ICREs), required for derepression of phospholipid biosynthetic genes in response to inositol depletion

Rfx1p

3.25

7.7 × 10-5

TCRYYRYRGCAAC

*

Protein involved in DNA damage and replication checkpoint pathway; recruits repressors Tup1p and Cyc8p to promoters of DNA damage-inducible genes; similar to a family of mammalian DNA binding RFX1-4 proteins

Gcr2p

3.17

3.6 × 10-5

CTTCC, CWTCC (Gcr1p)

 

Transcriptional activator of genes involved in glycolysis; interacts and functions with the DNA binding protein Gcr1p

Stp1p

3.13

1.4 × 10-4

CGGCN(6)CGGC

*

Transcription factor, activated by proteolytic processing in response to signals from the SPS sensor system for external amino acids; activates transcription of amino acid permease genes and may have a role in tRNA processing

Stp2p

3.02

2.0 × 10-6

CGGGGTGN(7)CGCACCG

*

Transcription factor, activated by proteolytic processing in response to signals from the SPS sensor system for external amino acids; activates transcription of amino acid permease genes

Adr1p

3.01

4.6 × 10-8

TTGGRGN(6-38)CYCCAA

 

Carbon source-responsive zinc-finger transcription factor, required for transcription of the glucose-repressed gene ADH2, of peroxisomal protein genes, and of genes required for ethanol, glycerol, and fatty acid utilization

a) Hypergeometric distribution with Bonferroni correction

Correlation of daunorubicin effects and deletions of transcription factor genes

A direct prediction of the DNA-binding competition model for daunorubicin action is that its presence in the cell should produce a phenocopy of genetic deletion of these factors [12], or their partial depletion [7]. To test this prediction, we compared the effects of daunorubicin shown here with a large dataset of null deletions of 42 transcription factors, many of them coincident with the set in Table 6[17]. Table 8 shows the correlation between microarray data from six deletion strains [17] and the corresponding figures from the 4 h daunorubicin-treatment dataset. For these calculations, ratios between deleted and wild type strains were compared to 4 h to 0 h ratios, only for those genes that showed significant variations in expression (positive or negative) due to daunorubicin treatment. The six strains shown in Table 8 are the only ones in the dataset [17] showing positive and significant correlation (p < 0.001, Bonferroni) with daunorubicin-treatment data. The best correlation values corresponded to three strains deleted for factors Adr1p, Cst6p and Sok2p; graphs in Figure 5 show expression ratios for these three strains plotted against the corresponding values from daunorubicin treatment. These plots strongly suggest that at least part of the changes in transcription ratios induced by daunorubicin may be due to competition of the drug with these and other transcription factors for binding to consensus DNA sequences.
Table 8

Correlation coefficient and associated p values between daunorubicin-treated and Transcription-factor deleted strainsa)

Deletion strain

r

p (T-test)

Bonferroni

Δsok2

0.428

3.1 × 10-19

3.1 × 10-17

Δadr1

0.427

3.8 × 10-19

3.8 × 10-17

Δcst6

0.344

1.5 × 10-12

1.5 × 10-10

Δpho4

0.256

2.1 × 10-7

2.1 × 10-5

Δste12

0.239

1.3 × 10-6

1.3 × 10-4

Δhap4

0.236

1.9 × 10-6

1.9 × 10-4

a) Only genes significantly altered by daunorubicin treatment were considered (n = 445).

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-358/MediaObjects/12864_2008_Article_1551_Fig5_HTML.jpg
Figure 5

Transcription ratios between daunorubicin-treated cells and three strains deleted for different transcription factors. The X-axis corresponds to microarray data for cells treated with daunorubicin for four hours (treated vs. untreated, log2 values). The Y-axis corresponds to data from reference [17]. Only data for the 475 genes affected by daunorubicin were considered.

Discussion

The yeast Saccharomyces cerevisiae is a favourite tool for testing drugs that interact and/or modify gene regulation, since it shares many common regulatory mechanisms with vertebrates, ranging from cell cycle to transcriptional regulation [13, 1820]. In a previous paper [12], we showed that daunorubicin specifically inhibited genes required for galactose utilisation, a phenotype we proposed linked to the presence of CpG steps in the recognition sequence of the main regulator for these genes, Gal4p. Here we extended these studies to the whole yeast transcriptome, in conditions of mild inhibition of cell growth.

Daunorubicin treatment affected transcription of a relative small proportion of genes. We chose a relatively mild treatment, slightly under the IC50, in order to minimise general toxic effects in cell membranes and widespread DNA damage. A conclusion from our analysis is the selective repression by daunorubicin of genes involved in the glycolytic pathway, whereas other genes involved in growth, like ribosomal protein genes, were either not affected or slightly activated. This pattern is very rarely observed in yeast, as glucose utilisation is required for fast growth. Figure 6 shows ratios of expression changes for 32 glycolysis-related genes (gly genes) and 123 ribosomal protein genes (rpg genes) in 146 stress conditions, including DNA damage (both chemical and by irradiation), oxidative and osmotic stress, amino acid and nitrogen starvation, entering in stationary phase, and temperature shifts ([21, 22]; list of genes and conditions in Table 9). The graph shows both the ratio between both sets of genes and p-values associated to their differential response to each stress. Low p-values (upper part of the graph, note the reversed Y-axis) correspond to data sets in which the response of both sets of genes showed little or no overlap, whereas high p-values (lower part of the graph) implicate that both sets of genes responded similarly to that specific stress condition. The graph shows that ribosomal protein genes are preferentially inhibited in many stress conditions compared to glycolysis-related genes (right portion of the graph), whereas daunorubicin treatment datasets (1 h and 4 h) differentiate clearly from the rest by specifically depressing glycolytic gene transcription without a parallel decrease of ribosomal synthesis (upper left part of the graph). We concluded that daunorubicin effects couldn't be ascribed to any of the tested stresses, including DNA damage and oxidative stress. This conclusion is further supported by the fact that many stress-related genes, like HSPs, were down regulated, rather than up regulated upon daunorubicin treatment.
Table 9

Genes and conditions used for the graph in Figure 6.

Gly genes

rpg genes

rpg genes

Experiments/conditions

ADH1

RPL10

RPL6A

DNA damage a

Osmotic stress b

Oxidative stress b

ADH2

RPL11A

RPL6B

DES460 + 0.02% MMS - 120 min

1M sorbitol - 120 min

1 mM Menadione (10 min)redo

ADH3

RPL11B

RPL7A

DES460 + 0.02% MMS - 15 min

1M sorbitol - 15 min

1 mM Menadione (105 min) redo

ADH5

RPL12A

RPL7B

DES460 + 0.02% MMS - 30 min

1M sorbitol - 30 min

1 mM Menadione (120 min)redo

CDC19

RPL12B

RPL8A

DES460 + 0.02% MMS - 5 min

1M sorbitol - 45 min

1 mM Menadione (160 min) redo

ENO1

RPL13A

RPL8B

DES460 + 0.02% MMS - 60 min

1M sorbitol - 5 min

1 mM Menadione (20 min) redo

ENO2

RPL13B

RPL9A

DES460 + 0.02% MMS - 90 min

1M sorbitol - 60 min

1 mM Menadione (30 min) redo

FBA1

RPL14B

RPL9B

DES460 + 0.2% MMS - 45 min

1M sorbitol - 90 min

1 mM Menadione (50 min)redo

GLK1

RPL15B

RPS0A

wt_plus_gamma_10_min

Hypo-osmotic shock - 15 min

1 mM Menadione (80 min) redo

GPM1

RPL16A

RPS0B

wt_plus_gamma_120_min

Hypo-osmotic shock - 30 min

1.5 mM diamide (10 min)

GPM2

RPL16B

RPS10A

wt_plus_gamma_20_min

Hypo-osmotic shock - 45 min

1.5 mM diamide (20 min)

GPM3

RPL17A

RPS10B

wt_plus_gamma_30_min

Hypo-osmotic shock - 5 min

1.5 mM diamide (30 min)

HXK1

RPL17B

RPS11A

wt_plus_gamma_45_min

Hypo-osmotic shock - 60 min

1.5 mM diamide (40 min)

HXK2

RPL18A

RPS11B

wt_plus_gamma_5_min

 

1.5 mM diamide (5 min)

LAT1

RPL18B

RPS12

wt_plus_gamma_60_min

AA/N starvation b

1.5 mM diamide (50 min)

PDA1

RPL19A

RPS13

wt_plus_gamma_90_min

aa starv 0.5 h

1.5 mM diamide (60 min)

PDB1

RPL19B

RPS14A

 

aa starv 1 h

1.5 mM diamide (90 min)

PDC1

RPL1A

RPS14B

 

aa starv 2 h

1 mM Menadione (40 min) redo

PDC5

RPL1B

RPS15

Temperature b

aa starv 4 h

2.5 mM DTT 005 min dtt-1

PDX1

RPL20A

RPS16A

17 deg growth ct-1

aa starv 6 h

2.5 mM DTT 015 min dtt-1

PFK1

RPL20B

RPS16B

21 deg growth ct-1

Nitrogen Depletion 1 d

2.5 mM DTT 030 min dtt-1

PFK2

RPL21A

RPS17A

25 deg growth ct-1

Nitrogen Depletion 1 h

2.5 mM DTT 045 min dtt-1

PGI1

RPL21B

RPS17B

29 deg growth ct-1

Nitrogen Depletion 12 h

2.5 mM DTT 060 min dtt-1

PGK1

RPL22A

RPS18A

29C to 33C - 15 minutes

Nitrogen Depletion 2 d

2.5 mM DTT 090 min dtt-1

PGM1

RPL22B

RPS18B

29C to 33C - 30 minutes

Nitrogen Depletion 2 h

2.5 mM DTT 120 min dtt-1

PGM2

RPL23A

RPS19A

29C to 33C - 5 minutes

Nitrogen Depletion 3 d

2.5 mM DTT 180 min dtt-1

STO1

RPL23B

RPS19B

33C vs. 30C - 90 minutes

Nitrogen Depletion 30 min.

constant 0.32 mM H2O2 (10 min) redo

TDH1

RPL24A

RPS1A

37 deg growth ct-1

Nitrogen Depletion 4 h

constant 0.32 mM H2O2 (100 min) redo

TDH2

RPL24B

RPS1B

DBY7286 37 degree heat - 20 min

Nitrogen Depletion 5 d

constant 0.32 mM H2O2 (120 min) redo

TDH3

RPL25

RPS2

DBYmsn2/4 (real strain) + 37 degrees (20 min)

Nitrogen Depletion 8 h

constant 0.32 mM H2O2 (160 min) redo

TPI1

RPL26A

RPS20

DBYmsn2-4- 37 degree heat - 20 min

 

constant 0.32 mM H2O2 (20 min) redo

TYE7

RPL26B

RPS21A

Heat Shock 005 minutes hs-2

Stationary phase b

constant 0.32 mM H2O2 (30 min) redo

 

RPL27A

RPS22A

Heat Shock 015 minutes hs-2

YPD 1 d ypd-2

constant 0.32 mM H2O2 (40 min) rescan

 

RPL27B

RPS22B

Heat Shock 030inutes hs-2

YPD 10 h ypd-2

constant 0.32 mM H2O2 (50 min) redo

 

RPL28

RPS23A

Heat Shock 05 minutes hs-1

YPD 12 h ypd-2

constant 0.32 mM H2O2 (60 min) redo

 

RPL2A

RPS23B

Heat Shock 060 minutes hs-2

YPD 2 d ypd-2

constant 0.32 mM H2O2 (80 min) redo

 

RPL3

RPS24A

Heat Shock 10 minutes hs-1

YPD 2 h ypd-2

DBY7286 + 0.3 mM H2O2 (20 min)

 

RPL30

RPS24B

Heat Shock 15 minutes hs-1

YPD 3 d ypd-2

DBYmsn2/4 (real strain) + 0.32 mM H2O2 (20 min)

 

RPL31A

RPS25A

heat shock 17 to 37, 20 minutes

YPD 4 h ypd-2

DBYmsn2msn4 (good strain) + 0.32 mM H2O2

 

RPL31B

RPS25B

Heat Shock 20 minutes hs-1

YPD 5 d ypd-2

dtt 000 min dtt-2

 

RPL32

RPS26A

heat shock 21 to 37, 20 minutes

YPD 6 h ypd-2

dtt 015 min dtt-2

 

RPL33A

RPS26B

heat shock 25 to 37, 20 minutes

YPD 8 h ypd-2

dtt 030 min dtt-2

 

RPL33B

RPS27A

heat shock 29 to 37, 20 minutes

YPD stationary phase 1 d ypd-1

dtt 060 min dtt-2

 

RPL34B

RPS27B

Heat Shock 30 minutes hs-1

YPD stationary phase 12 h ypd-1

dtt 120 min dtt-2

 

RPL35A

RPS28A

heat shock 33 to 37, 20 minutes

YPD stationary phase 13 d ypd-1

dtt 240 min dtt-2

 

RPL35B

RPS28B

Heat Shock 40 minutes hs-1

YPD stationary phase 2 d ypd-1

dtt 480 min dtt-2

 

RPL36A

RPS29A

Heat Shock 60 minutes hs-1

YPD stationary phase 2 h ypd-1

 
 

RPL37A

RPS29B

Heat Shock 80 minutes hs-1

YPD stationary phase 22 d ypd-1

 
 

RPL37B

RPS3

steady state 15 dec C ct-2

YPD stationary phase 28 d ypd-1

 
 

RPL38

RPS30A

steady state 17 dec C ct-2

YPD stationary phase 3 d ypd-1

 
 

RPL39

RPS30B

steady state 21 dec C ct-2

YPD stationary phase 4 h ypd-1

 
 

RPL40A

RPS31

steady state 25 dec C ct-2

YPD stationary phase 5 d ypd-1

 
 

RPL40B

RPS4A

steady state 29 dec C ct-2

YPD stationary phase 7 d ypd-1

 
 

RPL41A

RPS4B

steady state 33 dec C ct-2

YPD stationary phase 8 h ypd-1

 
 

RPL42A

RPS6A

steady state 36 dec C ct-2

  
 

RPL42B

RPS6B

steady state 36 dec C ct-2 (repeat hyb)

  
 

RPL43A

RPS7A

   
 

RPL43B

RPS7B

   
 

RPL4A

RPS8A

   
 

RPL4B

RPS8B

   
 

RPL5

RPS9A

   
  

RPS9B

   

a) Data from reference [21]

b) Data from reference [22]

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-358/MediaObjects/12864_2008_Article_1551_Fig6_HTML.jpg
Figure 6

Differential expression for glycolytic genes (gly) and ribosomal protein genes (rpg) in yeast cells subjected to different treatments. Fold induction or repression values were calculated for 32 glycolytic genes and 123 ribosomal protein genes for each of the 146 stress conditions, plus the two daunorubicin treatments. The X-axis values correspond to ratios between the average of fold induction/repression for glycolitic and ribosomal protein genes for in each experiment; Y-axis indicates the probability of both sets of genes being equally affected by each treatment. Note the reverse scale of the Y-axis. Each dot represent a single stress dataset for a particular stress condition; they are grouped in several categories: Daunorubicin treatment (DNR, 1 h and 4 h, red squares), DNA damaging agents (DD, 15 conditions, blue diamonds), osmotic stress (OS, 12 conditions, green triangles), oxidative stress (Ox, 45 conditions, yellow diamond), temperature stress (T, 37 conditions, orange circle), amino acid and nitrogen starvation (N, 15 conditions, dark brown circle) and maintenance in stationary phase for long periods of time (22 conditions, red triangles). Two vertical, discontinuous lines indicate 2-fold induction or repression; note that ratio values are expressed as log2 transformants. Except for daunorubicin-treatment, all data are from references [21, 22]. Genes and conditions analysed are listed in Table 9.

Inspection of promoters of daunorubicin-inhibited genes showed that they present a significant high proportion of DNA binding sites for a defined subset of transcription factors, most of them related to sugar metabolism. These data have to be interpreted not necessarily as an indication of direct interaction of the drug with these transcription factors, but only as a hint of the regulatory networks, or regulons, particularly affected by the drug. Due to the complexity of eukaryotic promoters, several factors may appear in any particular affected promoter, although the putative direct effect of the drug may affect to only one or two of them. A particularly relevant example is Mig1p, a transcriptional repressor central in the catabolite repression by glucose and that binds to many glycolytic gene promoters [23]. Therefore, it appears on the lists of transcription factors preferentially associated to daunorubicin-inhibited genes (Tables 6 and 7), although the hypothetical suppression of its binding to DNA would result in activation, rather than inhibition, of the affected gene. This is the most reasonable explanation by the appearance in these lists of some transcription factors that do not encompass daunorubicin-preferred sites in their recognition sequences (Table 7).

Data mining identified several microarray datasets with patterns resembling to the ones observed in daunorubicin-treated cells. Best correlations were observed for strains deleted for some glucose-related transcription factor genes, especially ADR1, CST6 and SOK2. Deletion of these genes results in a general decrease on transcription of glycolytic genes with relatively mild effects on transcription of genes related to cell growth, like ribosomal protein genes -exactly the pattern observed in daunorubicin-treated cells. Two of these three factors (Adr1p and Cst6p) were identified as preferentially associated to genes down regulated by daunorubicin (Table 6, Figure 4). This list also includes a high proportion of factors whose DNA recognition sequences include CpG steps, the preferred binding site for daunorubicin [4]. Therefore, we concluded that daunorubicin inhibition of yeast growth might be mediated by its interaction with DNA at sequences also recognized by some transcription factors, resulting in a transcriptional repression of glycolytic genes, among others. These results corroborate the interest in using yeast mutants as an in vivo system to identify the determinants of chemosensitivity [13].

The amazing conservation of regulatory elements among opisthokonta (taxon that includes fungi and animals, among other groups) allows identification of pathways and transcription factors common to yeast and humans. For example, Cst6p is a basic leucine zipper transcription factor of the ATF/CREB family, which includes bona fide orthologues in mammals, not only in functional terms (targets for the cAMP regulatory pathway), but also by their binding to identical DNA sequences, 5'-TGACGTCA-3' [24]. This sequence includes a high affinity site for daunorubicin, providing an explanation for several of the effects observed in this work. Sok2p is also known to participate in the cAMP regulatory pathway [25], and, therefore, many cAMP-regulated promoters encompass binding sites for both factors. This circumstance provides a good explanation for the good correlation between the changes in gene expression due to the deletion of the corresponding gene and those observed upon daunorubicin treatment, although the DNA recognition sequence for Sok2p (5'-TGCAGNNA-3', [26]) does not include high affinity sites for daunorubicin. Therefore, our data suggest that daunorubicin may target the cAMP signalling pathway of yeast, inhibiting expression of many regulated genes and particularly those under control of Cst6p, ant that may be explained by binding of the drug to the Cst6p DNA recognition site. The question of whether daunorubicin may have similar effects in the cAMP-mediated regulation of proliferation of mammalian cells is still open.

Extrapolation of these results to tumour cells can be undertaken at several levels. First, as a general model, they demonstrate that DNA-intercalating drugs can block cell growth by selectively reducing the efficiency of different transcription factors. If these factors are required for cell growth, this would prevent tumour propagation at effective concentration of the drug much below the ones required for the massive DNA damage required to trigger apoptosis [27, 28]. In addition, the specific effects of daunorubicin on the glycolysis pathway may be relevant to its antitumor effect. One of the most outstanding alterations in cancer cells is their dependence on glycolytic pathways for the generation of ATP [29], and there is compelling evidence that mitochondrial defects in tumour cells under hypoxia are remarkably sensitive to glycolysis inhibition [29]. Besides, it has been recently reported that some inhibitors of glucose uptake sensitize tumour cells to daunorubicin [30]. Our data would suggest that daunorubicin might work not only as a DNA-damaging agent but also as an inhibitor of glycolytic pathways, a combined effect that might have broad therapeutic implications against cancer cells growing under hypoxic conditions.

Conclusion

The yeast Saccharomyces cerevisiae is a powerful tool for the study the effects of drugs on eukaryotic cells. We showed that the antitumor drug daunorubicin alters transcription of some very specific subsets of genes, in a pattern in which sugar- metabolising pathways become down-regulated whereas proliferation-related genes, like ribosomal protein genes, are unaffected or even activated. This pattern is very similar to the one observed in yeast strains deleted for some transcription factors related to the regulation of the glycolytic pathway, like Adr1p, Cst6p and Sok2p. This results are consistent with the hypothesis that daunorubicin impairs binding of different transcription factors by competing for their DNA binding sequences, therefore limiting their effectiveness and affecting the corresponding regulatory networks. This proposed mechanism might have broad therapeutic implications in cancer therapeutics.

Methods

Yeast growth and daunorubicin treatment

Daunorubicin (Sigma, St. Louis, MO, U.S.A.) was freshly prepared as a 2 mM stock solution in sterile 150 mM NaCl solution, and diluted to each final concentrations before use. A single colony of S. cerevisiae (BY4741 erg6Δ (MATa, his3Δ1, leu2Δ0, met15Δ0, ura3Δ0, YML008c::KanMX4, from EUROSCARF, Frankfurt, Germany) was inoculated into 25 ml of YPD medium (10 g/L yeast extract, 20 g/L peptone and 20 g/L dextrose) and grown overnight at 30°C in an environmental shaker (250 rpm) until exponential phase. This yeast culture was used to inoculate 500 ml of YPD to an initial A600 of 0.1 and further incubated at the same conditions until A600 = 0.4. This culture was then divided into three aliquots and diluted four times with fresh YPD medium. Daunorubicin was then added to each culture at a final concentration of 12 mM and cultures were allowed to grow for 1 or 4 hours. The whole procedure was repeated for Real-Time quantitative PCR (qRT-PCR) validation; in this case, only two biological replicas were obtained.

RNA Preparation

Cultures were centrifuged for 5 min at 3000 rpm, washed with 5 ml MilliQ water and subsequently centrifuged (repeated twice). Total RNA was extracted with the RiboPure Yeast kit (Ambion, Austin, TX, USA). Total RNA was quantified by spectrophotometry in a NanoDrop ND-1000 (NanoDrop Technologies, Wilmintong DE, USA) and its integrity checked on TBE-agarose gels. The resulting total RNA was then treated with DNAseI I (F. Hoffmann-La Roche, Basel Switzerland) to remove contaminating genomic DNA.

DNA Microarray Analysis

Microarrays used in this work were produced at the Genomics Unit of the Scientific Park of Madrid (Spain). They consist of 13,824 spots, each one corresponding to a synthetic oligonucleotide (70-mer, Yeast Genome Oligo Set, OPERON, Cologne, Germany) encompassing the complete set of 6306 ORFs coded by the S. cerevisiae genome. Each ORF was printed at least twice; 600 spots were used as negative controls, either void or printed with random oligonucleotides; a small subset of genes (ACT1, HSP104, NUP159, NUP82, RPL32, RPS6B, SWI1, TDH1, TDH2, TUB4 and UBI1) were printed between 6 and 12 times for testing reproducibility.

Fifteen μ g of total RNA were used for cDNA synthesis and labelling with Cy3-dUTP and Cy5-dUTP fluorescent nucleotides, following indirect labelling protocol (CyScribe post-labelling kit, GE-Healthcare, New York, NY, USA). Labelling efficiency was evaluated by measuring Cy3 or Cy5 absorbance in Nanodrop Spectrophotometer. Microarray prehybridization was performed in 5× SSC (SSC: 150 mM NaCl, 15 mM Na-citrate, pH 7.0), 0.1% SDS, 1%BSA at 42°C for 45 min. (Fluka, Sigma-Aldrich, Buchs SG, Switzerland). Labelled cDNA was dried in a vacuum trap and used as probe after resuspension in 110 μ l of hybridization solution (50% Formamide, 5×SSC, 0.1% SDS, 100 μ g/ml salmon sperm from Invitrogen, Carlsbad, CA, USA). Hybridization and washing were performed in a Lucidea Slide Pro System (GE Healthcare, Uppsala, Sweden). Arrays were scanned with a GenePix 4000B fluorescence scanner and analyzed by Genepix 5.0 Pro software (Axon Instruments, MDS Analytical Technologies, Toronto, Canada). Data was filtered according to spot quality. Only those spots whose intensity was twice background signal and, at least 75% of pixels had intensities above background plus two standard deviations were selected for further calculations. In average, about 60 to 70% of spots in each array were considered suitable for further analysis following these criteria.

Quantitative Real Time RT-PCR Assay

An aliquot of RNA preparations from untreated and treated samples, used in the microarray experiments, was saved for qRT-PCR follow-up studies. First strand cDNA was synthesized from 2 μ g of total DNAseI-treated RNA in a 20 μ l reaction volume using Omniscript RT Kit (Qiagen, Valencia, CA, USA) following manufacture's instructions. qRT-PCR reactions were performed by triplicate using the ABI-PRISM 7000 Sequence Detection System (Applied Biosystems, Foster City, CA, USA) using the SYBR Green PCR Master Mix (Applied Biosystems). Gene-specific primers (listed in Table 4) were designed using Primer Express software (Applied Biosystems). Amplified fragments were confirmed by sequencing in a 3730 DNA Analyzer (Applied Biosystems) and sequences were compared with the published genomic data at SGD. Real time PCR conditions included an initial denaturation step at 95°C for 10 min, followed by 40 cycles of a two steps amplification protocol: denaturation at 95°C for 15 s and annealing/extension at 60°C for 1 min. Relative expression values of different genes were calculated following the ΔΔC T method [31, 32], using RPO21 as reference gene.

Clustering and statistical analysis

Our experimental design allowed to obtain up to 6 determinations for each gene and condition: three biological replicates per condition, two replicated spots for each gene in the array. Statistical analyses only considered genes for which a minimum of nine (out of 18) data values passed the microarray quality standards (3458 genes). Data were calculated as binary logarithms (log2) of fluorescence ratios (treated versus untreated samples). Significant changes on expression values between the starting point (time 0) and samples taken at 1 and 4 hours of daunorubicin treatment were determined by the Student's T-test. The whole dataset, combining data from the three time points, was analyzed with the TIGR MeV program [33]. Data were normalised by experiments and clustered by hierarchical clustering (Euclidean distance), treating duplicated spots as independent data series. Genes showing significant variations between time points were identified by ANOVA with the Bonferroni correction (p < 0.05). These genes were grouped by their expression patterns in a two-dimensional map grid by SOM (Self-Organizing Maps) [34], to generate hypotheses on the relationships and the function of genes. Classification of genes by gene ontology (GO) in biological process categories [35] was performed in the SDG page. Documented regulators of both affected and non-affected genes were retrieved from YEASTRACT [16]. Statistical analyses on the frequency of regulated genes in different subsets of data were performed using hypergeometric distribution tests with the Bonferroni correction (see SGD page, and http://mathworld.wolfram.com/HypergeometricDistribution.html)

Declarations

Acknowledgements

This work has been supported by the Spanish Ministry for Education and Science (MEC, grants BIO2005-00840, BFU2007-60998/BMC and AGL2000-0133-P4-03). The contribution of the Centre de Referència en Biotecnologia de la Generalitat de Catalunya is also acknowledged.

Authors’ Affiliations

(1)
Institut for Molecular Biology of Barcelona, IBMB-CSIC, Jordi Girona

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© Rojas et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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