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Microbial alcohol-conferred hemolysis is a late response to alcohol stress

Amir Shuster, Moshe Korem, Jasmine Jacob-Hirsch, Ninette Amariglio, Gideon Rechavi, Mel Rosenberg
DOI: http://dx.doi.org/10.1111/j.1567-1364.2011.00722.x 315-323 First published online: 1 June 2011

Abstract

We have reported previously that growth on alcohol vapors confers hemolytic properties on certain yeast species and strains [‘microbial alcohol-conferred hemolysis’ (MACH)]. In a recent study, we analyzed the genetic basis of MACH in Saccharomyces cerevisiae using the EUROSCARF mutant collection. The data suggested that intact mitochondrial and respiratory chain functions are critical for the observed alcohol-mediated hemolysis. We proposed that the uncontrolled cellular uptake of alcohol results in yeast ‘hyper-respiration’, leading to elaboration of hemolytic molecules such as hydrogen peroxide and lytic lipids. In the current study, we have further analyzed the molecular mechanisms involved in the MACH phenomenon in S. cerevisiae, using DNA microarrays. The patterns of regulation were confirmed by quantitative reverse transcriptase PCR. The results presented here lend further support to this hypothesis, based on upregulation of the genes responsible for coping with vast amounts of hydrogen peroxide produced as a byproduct of excessive oxidation of alcohol. These results, taken together, show that alcohol-mediated hemolysis in yeast appears to be related to the overproduction of hemolytic byproducts, particularly hydrogen peroxide, which accumulates during long-term exposure of S. cerevisiae to both ethanol and n-butanol.

Keywords
  • hemolysis
  • ethanol
  • n-butanol
  • hydrogen peroxide
  • yeast
  • respiration

Introduction

In 1919, Brown introduced hemolysis of blood agar as a technique for identifying pathogenic streptococci (Brown, 1919). Since then, hemolysis has been widely used as a routine method for identifying a wide variety of pathogenic bacteria (Braun & Focareta, 1991). However, hemolysis of blood agar by yeast cells is both rare and surprising. For the most part, yeasts are not considered hemolytic, and only a few researchers have reported hemolysis, for example, in the presence of a high glucose in the medium (Luo, 2001). We have shown recently that certain yeast strains do become hemolytic when grown in the presence of alcohol. We called this novel phenomenon ‘microbial alcohol-conferred hemolysis’ (MACH) (Shuster, 2004). The phenomenon is strain and species specific, and varies according to the type of alcohol provided. In a recent study, we analyzed the genetic basis of MACH in Saccharomyces cerevisiae using the EUROSCARF mutant collection. The data suggested that intact mitochondrial and respiratory chain functions are critical for the observed alcohol-mediated hemolysis. We proposed that the uncontrolled cellular uptake of alcohol results in yeast ‘hyper-respiration’, leading to elaboration of hemolytic molecules such as hydrogen peroxide and lytic lipids (Shuster, 2007). In the current study, we have further analyzed the molecular mechanisms involved in the MACH phenomenon in S. cerevisiae, using DNA microarrays. The results presented here lend further support to this hypothesis, based on upregulation of the genes responsible for coping with vast amounts of hydrogen peroxide produced as byproducts of excessive oxidation of alcohol.

Materials and methods

Saccharomyces cerevisiae BY4741

Saccharomyces cerevisiae BY4741, the wild-type strain, from the EUROSCARF (Frankfurt, Germany) deletion strain collection was used for this study (http://sequence-www.stanford.edu/group/yeast_deletion_project/deletions3.html; Brachmann, 1998; Winzeler, 1999). The strain was stored at −70 °C in YPD (1% yeast extract, 2% Bacto peptone, 2% glucose) medium supplemented with 15% glycerol.

Total RNA isolation

Cells (S. cerevisiae BY4741) were inoculated into a 15-mL test tube containing 4 mL YPD medium and grown aerobically without agitation at 30 °C for 24 h to a concentration of 1 × 108 cells. Ten-microliter (1 × 106 cells) drops were applied to blood agar plates. Following 24-h growth under aerobic conditions at 30 °C, ethanol (400 μL) or n-butanol (40 μL) was applied to discs of Whatman paper placed within the lid of the plates. Control plates received no alcohol. The plates were sealed with ParafilmR and incubated for an additional 24 h under the same conditions. Cells were harvested immediately and washed once in ice-cold sterile phosphate-buffered saline. RNA was extracted using the RiboPureTM-Yeast Kit (Ambion Inc., Austin, TX). Briefly, cells (3 × 108 cells from each sample) were disrupted with frozen zirconia beads. RNA was extracted with phenol and filtered through a glass-fiber filter. The concentration and purity of the RNA samples were assessed by reading the absorbance in a spectrophotometer at 260 and 280 nm (BioPhotometer, Eppendorf). The A260 nm/A280 nm ratio ranged between 1.99 and 2.0 for all the samples. The quality of sample RNA was also confirmed using agarose gels.

Microarray processing

All experiments were performed using Affymetrix Yeast genome S98 oligonucleotide arrays, as described at http://www.affymetrix.com/support/technical/datasheets/yeast_datasheet.pdf. Total RNA from each sample was used to prepare biotinylated target RNA, with minor modifications based on the manufacturer's recommendations (http://www.affymetrix.com/support/technical/manual/expression_manual.affx).

Briefly, 5 μg of mRNA were used to generate first-strand cDNA using a T7-linked oligo(dT) primer. After second-strand synthesis, in vitro transcription was performed with biotinylated UTP and CTP (Enzo Diagnostics), resulting in approximately 300-fold amplification of RNA. The target cDNA generated from each sample was processed as per the manufacturer's recommendation using an Affymetrix GeneChip Instrument System. Briefly, spike controls were added to 15 μg fragmented cRNA before overnight hybridization. Arrays were then washed and stained with streptavidin–phycoerythrin and subsequently scanned on an Affymetrix GeneChip scanner. A complete description of these procedures is available at http://www.affymetrix.com/support/technical/manual/expression_manual.affx.

After scanning, array images were assessed visually to confirm scanner alignment and the absence of significant bubbles or scratches on the chip surface. The 3′/5′ ratios for SRB4 (YER022W), SPT15 (YER148W) and ACT1 (YFL039C) were confirmed to be within acceptable limits (2.12–2.6, 2.56–3.13 and 1.31–1.63, respectively), and BioB spike controls were found to be present on all chips, with BioC, BioD and CreX also present in increasing intensity. When scaled to a target intensity of 150 (using Affymetrix mas 5.0 array analysis software), the scaling factors for all arrays were within acceptable limits (4.01–8.29), as were the background, Q values and mean intensities. The details of the quality control measures can be found at http://www.ncbi.nlm.nih.gov/geo/ or at http://eng.sheba.co.il/Functional_Genomics/

Microarray analysis

The 9335 probe sets contained in the Affymetrix Yeast Genome S98 oligonucleotide array were filtered using the mas 5 algorithm. A list of 6355 ‘valid’ probe sets, representing probe sets with signals higher than 20 and detected as present (P) in at least one set of sample replicates, was obtained. We chose to expose cells to alcohols that promote hemolysis (ethanol or n-butanol). We included methanol (which does not promote hemolysis) as an additional control; however, the results were not consistent (data not shown). Microarrays were tested in triplicate for ethanol and control and in duplicate for n-butanol. The comparison generated a list of 1092 ‘active genes’ representing probe sets changed by at least threefold as calculated from the mas 5 log ratio values (LR≥1 or LR≤−1) and detected as ‘increased’ or as ‘decreased’ (I or D, P-value 0.0025) or ‘marginally increased’ or as ‘marginally decreased’ (MI or MD, P-value 0.003) in all treated samples as compared with all the control samples with respect to one or more time points. This list excluded upregulated genes in all treated samples with signals lower than 20 or detected as absent, and downregulated genes with baseline signals lower than 20 and detected as absent in the control samples. Hierarchical clustering was performed using the spotfire decision site for functional genomics (Somerville, MA). Genes were classified into functional groups using the ‘SGD Genes Ontology Slim Mapper’ (http://db.yeastgenome.org/cgi-bin/GO/goTermMapper), with further manual adjustment according to Saccharomyces Genome Database (SGD) (http://www.yeastgenome.org).

The classification was carried out after excluding long terminal repeats, rRNA, snRNA, tRNA and retrotransposons from the list.

Reverse transcription and quantitative reverse transcriptase PCR (QRT-PCR)

Saccharomyces cerevisiae RNA (the same sample material was used for both QRT-PCR and microarray experiments) was isolated using the RiboPureTM-Yeast Kit (Ambion Inc.) according to the manufacturer's instructions, followed by treatment of DNase I (Ambion Inc.) at 37 °C for 20 min. To verify the absence of genomic DNA, PCR was carried out using DNase I-treated RNA samples as templates and cyc1 primers. One microgram of each RNA sample was used for cDNA synthesis. Random hexamers (Qiagen) were used to prime the reaction. To set up the real-time PCR, we used the SYBR Green PCR Master Mix (Applied Biosystems) according to the manufacturer's instructions. The transcripts for cyc1, agp2, cor1, mep3, ctt1, ccp1, yat1, sul1, stl1 and cat1 were amplified using the primers shown in Table 1. The 18S rRNA gene transcript that is constitutively expressed and showed no change in response to ethanol was used as an internal control. Amplification efficiency was analyzed by performing PCR on a dilution series of DNA. The amplification reaction mixture (20 μL) contained 5 μL template cDNA (diluted); 1 × TaqMan buffer A; 5 mM MgCl2; 200 μM each of dATP, dCTP, dGTP, and 400 μM dUTP; 80 μM AmpErase uracil N-glycosylase; 0.2 μM forward and reverse primer; 0.1 μM; and 1.25 U AmpliTaq Gold DNA polymerase (Applied Biosystems). Before amplification, the reaction mixture was heated to 50 °C for 2 min and then denatured at 95 °C for 10 min. The amplification profile was as follows: 40 cycles of 95 °C for 15 s and then 60 °C for 1 min. Reactions were performed in the ABI PRISM 7900HT Sequence Detection system and data were analyzed using the sds 2.3 software (Applied Biosystems). ΔΔCT was calculated as the CT value from the untreated control cells minus the CT value from the treated cells and corrected for differences in signals from the 18S rRNA gene controls.

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1

Primers used in QRT-PCR expression studies

GeneNameSequence (5′–3′)
agp2Agp2FTCGAACCTAGTGAGGGATGGTT
Agp2RCCCGCTTCGGGTATCGT
stl1Stl1FCCGTGTCAATGCAAATCGTT
Stl1RCGTGGCGATTCAGGTAGTTTAAT
cta1Cta1FTGCCCAATGATAAGGGATAGCT
Cta1RTCCAACAGGACAGACCCATTC
sul1Sul1FAACAAGGCGAGGCCAACTT
Sul1RCCATTCACCAGGATCATTCCAT
mep3Mep3FAATGCTGCAAGCTCACTGTCA
Mep3RTGGCGCTGAGACAAGTGTTC
cyc1Cyc1FTTCAAGGCCGGTTCTGCTAA
Cyc1RCCACGGTGTGGCATTGTAGA
yat1Yat1FGGTCGCATACTTCGCGTTGTA
Yat1RTTGGAACGCCTTGGTCATG
ccp1Ccp1FGTCACTGCCGTGCAGGAAAT
Ccp1RCCTCTGGCGTGTCGACTCTAC
ctt1Ctt1FCACGTCTTGTCGGATACTGGTTT
Ctt1RGGTGGGAGCCTGACAGTTCA
cor1Cor1FACTGTCTCTGGGTGAGGCTTTC
Cor1RACCGGCCCAAGCCTTAAC
18S rRNA gene18sFCGGCTACCACATCCAAGGAA
18sRGCTGGAATTACCGCGGCT

To monitor specificity, the PCR products were analyzed by melting curves and the values (Table 2) are an average of three replications normalized with respect to 18S rRNA gene expression.

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2

Gene expression analysis by QRT-PCR

GeneGene functionEthanoln-Butanol
ΔΔCTFold changeFold change (arrays)ΔΔCTFold changeFold change (arrays)
cyc1Cytochrome c4.2 ± 0.2318.3712.754.31 ± 0.7219.838
agp2High-affinity glutamine permease2.1 ± 0.334.283.12.9 ± 0.857.467.3
cor1CORe protein of QH2 cytochrome c reductase1.97 ± 0.123.95.32.52 ± 0.345.734.6
mep3Ammonium permease of high capacity and low affinity0.97 ± 0.66210.912.12 ± 1.154.3434.95
ctt1Cytosolic catalase T2.95 ± 0.637.7212.22.47 ± 1.625.543.6
ccp1Mitochondrial cytochrome c peroxidase1.25 ± 0.072.33.52.74 ± 0.136.688.6
yat1Outer mitochondrial carnitine acetyltransferase4.13 ± 1.7617.53.43.14 ± 2.268.83.8
sul1High-affinity sulfate permease−0.5 ± 0.48−1.418.500.18 ± 0.031.13114.1
stl1Sugar transporter-like protein3.0 ± 0.74818.523.06 ± 0.88.3317.7
cat1Catalase A2.03 ± 2.04.123.1 ± 0.98.573.4
  • * ΔΔCT values are shown with SDs. 18S rRNA gene expression was used as a reference.

  • Data extracted from Table 3b.

Results

Exposure to ethanol and n-butanol altered the expression of 1092 ‘active genes’ by at least threefold. This represents 17% of the 6355 ‘valid’ probe sets analyzed. Except where otherwise noted, the present analysis focuses on those genes whose regulation was affected by at least a factor of three in the presence of one or both alcohols. Among these genes, 109 and 254 were upregulated by ethanol and n-butanol, respectively, whereas 126 and 603 genes were downregulated by ethanol and n-butanol, respectively (Table 3a and b).

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3a

Numbers of upregulated and downregulated genes based on microarray analysis

Ethanoln-Butanol
Upregulated>3-fold109254
Upregulated>5-fold3792
Upregulated>10-fold1218
Downregulated>3-fold126603
Downregulated>5-fold18293
Downregulated>10-fold071
Genes upregulated in the presence of both alcohols66
Genes downregulated in the presence of both alcohols102
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3b

Genes that are most upregulated by ethanol and n-butanol

Ethanoln-Butanol
Genes that are most upregulated >10-foldBIO (20.43), STL1 (18.52), SUL1 (18.50), SE1 (16.57), YHB1 (15.25), PRY2 (15.01), OPT (13.21), BIO2 (13.17), CYC1 (12.75), GIC2 (12.42), CTT1 (12.17), MEP3 (10.91)SUL1 (114.1), PDR12 (44.2), CTF3 (39.15), MEP3 (34.95), MEP2 (34.90), CHA1 (24.95), PTR2 (22.85), VAC17 (18), FMP17 (18.2), STL1 (17.7), SER3 (16.20), DAL3 (14.85), AGP (12.90)1, MET2 (12.03), PRY2 (11.98), COX23 (11.88), CAN1 (10.6), SPL2 (10.42)
  • The full list of upregulated and downregulated genes appears in Tables S1–S4.

Genes upregulated in the presence of both alcohols

Among the 66 genes upregulated by both ethanol and n-butanol (Table 4), 30 (46%) were localized to the mitochondrion, 10 to the uncompartmentalized cytoplasm (15.2%), seven to the nucleus (10.6%) and four to the endoplasmic reticulum (ER) (6.1%). The remaining 16 genes were localized to other cellular components (e.g. plasma membrane, cell wall).

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4

Genes upregulated by both ethanol and n-butanol

Biological processCellular locationGene name and fold change (ethanol, n-butanol)
TransportCytosol, peroxisome, mitochondriaSTL1 (18.5, 17.7), SUL1 (18.5, 114.1), MEP3 (10.9, 35), SUT1 (7.6, 5.9), BAP3 (6, 7.8), PTR2 (5.7, 22.9), AGP1 (4.7, 12.9), AGP2 (3.1, 7.3), YHM2 (4.6, 3.7), ATO2 (4.6, 5.7), TIM9 (3.8, 7.9), CYB2 (3.7, 6.6), CAN1 (3.5, 10.6), MUP3 (3.5, 6.6), YAT1 (3.4, 3.8), YAT2 (3.4, 5.9), HNM1 (3.2, 3.5)
Respiratory chainMitochondriaCYC1 (12.8, 8), COR1 (5.3, 4.6), SDH2 (3.7, 4.1), SDH3 (4.8, 3.6), SDH4 (5.1, 6.8), CYT1 (4.4, 5), COX8 (3.1, 3.1), PET100 (3.2, 4.4)
Lipid metabolismMitochondria, cytosol, endoplasmic reticulumEHT1 (5.1, 4.3), CHO1 (3.5, 5.4), CRC1 (3.3, 3.6), EHD3 (3.3, 3.5), IPT1 (3.1, 3.9)
Amino acid metabolismMitochondria, cytosolMET2 (6.9, 12), LEU1 (5.6, 5.3), GDH1 (5, 5.5), GLT1 (3.6, 3.4), CHA1 (3.6, 25)
SporulationMitochondria, cytosol, cell membrane, nucleusSUR7 (7.9, 6.9), MPC54 (3.7, 9.6), UBX6 (3.3, 3.5), UME6 (3, 3.2)
Carbohydrate metabolismCytosol, peroxisome, mitochondria, nucleusNRG1 (4.8, 9.5), FBP1 (3.7, 3.64), CIT2 (3.5, 5.3)
CytokinesisPlasma membrane, cell septum, cell membraneGIC2 (12.4, 8.5), DSE4 (5.5, 4.9), EGT2 (5.3, 4.9)
Response to stressMitochondria, cytosolCTT1 (12.2, 3.6), CCP1 (3.5, 8.6)
ConjugationCell wall, cytosol, endoplasmic reticulum, plasma membraneSCW4 (6.2, 5.5), SCW10 (4.4, 5.7)
OtherMitochondria, cytosol, cell membrane, nucleusBIO2 (13.2, 9.2), ACH1 (5.8, 6), BAG7 (4, 4.2), LIP5 (3.7, 8.9), HEM15 (3.6, 8), SRD1 (3.4, 6.7), DMC1 (3.2, 9.4)
UnknownMitochondria, cytosol, cell membrane, nucleusPRY2 (15, 12),YLL055W (5.6, 3.6), FMP12 (4.5, 18.3), YER130C (4, 7.4), YEL059W (3.5, 3.2), YCR007C (3.3, 4.1), FMP10 (3.2, 6), FMP37 (3.2, 5.1), YLR177W (3.1, 4), ICY1 (3, 3.5)

Most of the upregulated genes were involved in metabolism (36 genes, 55%), mainly cellular transport (including genes of the respiratory chain) (25 genes, 38%), amino acid (five genes, 7.5%) and lipid metabolism (five genes, 7.5%). Among the most highly upregulated genes were those involved in protection from the effects of aldehydes and oxidative stress response (e.g. STL1, SUL1, CTT1 and CCP1); both cytosolic catalase CTT1 (YGR088W) and mitochondrial cytochrome c peroxidase CCP1 (YKR066C) function in the degradation of hydrogen peroxide generated during aerobic respiration. All of the five carnitine-O-acetyl transferases [YAT1 (3.4-, 3.8-fold), YAT2 (3.4-, 5.9-fold), CRC1 (3.3-, 3.6-fold), AGP2 (3.1-, 7.3-fold), CAT2 (2.4-, 3.8-fold)] were upregulated by both alcohols. Carnitine transport is important for the transfer of acetyl Co-A and fatty acids from the cytosol and peroxisome into the mitochondria. Several genes of the respiratory chain were highly upregulated by ethanol and n-butanol, respectively (Table 5). These include the four succinate dehydrogenase (SDH) genes of complex II [SDH1 (2.8-, 4.0-fold), SDH2 (3.7-, 4.1-fold), SDH3 (4.8-, 3.6-fold), SDH4 (5.1-, 6.8-fold)], YJL045W, a minor SDH isozyme, homologous to Sdh1p (7.3-, 3.0-fold), and cytochrome c CYT1 (4.4-, 5-fold), and cytochrome c, isoform 1 CYC1 (12.8-, 8-fold).

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5

Respiratory chain complexes I–IV genes

Standard gene nameSystematic nameGene descriptionFold change (ethanol)Fold change (n-butanol)
NADH:ubiquinone oxidoreductase
NDI1YML120CNADH:ubiquinone oxidoreductase2.2
Respiratory chain complex II
SDH1YKL148CFlavoprotein subunit of succinate dehydrogenase2.84.0
SDH2YLL041CIron–sulfur protein subunit of succinate dehydrogenase3.74.1
SDH3YKL141WCytochrome b subunit of succinate dehydrogenase4.83.6
SDH4YDR178WMembrane anchor subunit of succinate dehydrogenase5.16.8
Respiratory chain complex III
COBQ0105Cytochrome b, mitochondrially encoded subunit of the ubiquinol-cytochrome c reductase complex
COR1YBL045CCore subunit of the ubiquinol-cytochrome c reductase complex (bc1 complex)5.34.6
CYT1YOR065WCytochrome c14.45.0
QCR2YPR191WSubunit 2 of the ubiquinol cytochrome c reductase complex
QCR6YFR033CSubunit 6 of the ubiquinol cytochrome c reductase complex1.62.9
QCR7YDR529CSubunit 7 of the ubiquinol cytochrome c reductase complex2.83.4
QCR8YJL166WSubunit 8 of ubiquinol cytochrome c reductase complex2.4
QCR9YGR183CSubunit 9 of the ubiquinol cytochrome c reductase complex2.43.3
QCR10YHR001W-ASubunit of the ubiquinol-cytochrome c oxidoreductase complex2.81.9
RIP1YEL024WRieske iron–sulfur protein of the mitochondrial cytochrome bc1 complex2.54.3
Respiratory chain complex IV
COX1Q0045Subunit I of cytochrome c oxidase
COX2Q0250Subunit II of cytochrome c oxidase
COX3Q0275Subunit III of cytochrome c oxidase
COX4YGL187CSubunit IV of cytochrome c oxidase2.11.5
COX5AYNL052WSubunit Va of cytochrome c oxidase3.02.5
COX5BYIL111WSubunit Vb of cytochrome c oxidase1.42.6
COX6YHR051WSubunit VI of cytochrome c oxidase3.02.6
COX7YMR256CSubunit VII of cytochrome c oxidase1.8
COX8YLR395CSubunit VIII of cytochrome c oxidase3.23.1
COX9YDL067CSubunit VIIa of cytochrome c oxidase1.42.0
COX12YLR038CSubunit VIb of cytochrome c oxidase1.3
COX13YGL191WSubunit VIa of cytochrome c oxidase1.91.7
  • * Encoded in the mitochondrial genome.

It should be noted that both COX5A and COX5B were upregulated both by ethanol and by n-butanol. COX5B is usually upregulated during anaerobiosis, whereas this experimental setting is conducted under oxygenated conditions. This phenomenon may be explained by S. cerevisiae's competition mechanism, which evolved millions of years ago, allowing it to display ‘anaerobiosis’ genes while growing on fruit in the atmospheric air (Thomson, 2005).

Genes downregulated in the presence of both alcohols

Among the 102 genes downregulated by both ethanol and n-butanol, 52 (51%) were localized to the nucleus, 14 to the cytosolic ribosome (13.7%), 13 to the cytoplasm (after excluding genes localized to the mitochondrion, ribosome and ER) (12.7%) and only eight to the mitochondrion (7.8%). The remaining 15 genes were localized to other cellular components. The full list of genes downregulated in the presence of both alcohols appears in Supporting Information, Table S5. Most of the downregulated genes appear to be related to growth arrest resulting from the alcohol stress (Gasch, 2000; Alexandre, 2001): organelle organization and biogenesis (46 genes, 45%), primarily ribosome biogenesis and assembly (41 genes, 40%), RNA metabolism (33 genes, 32.4%), protein biosynthesis (18 genes, 17.6%), transport (12 genes, 11.8%) and transcription (eight genes, 7.8%).

The number of downregulated genes is by far greater for n-butanol than for ethanol (603 and 126, respectively). This may be explained by the fact that n-butanol is not an abundant metabolic product as ethanol is for S. cerevisiae. In this experimental setting, the level of n-butanol that the cells are exposed to is potentially toxic. Thus, the cells are forced into a complete growth shutdown to maximize their metabolic ability to channel n-butanol to less harmful byproducts.

Genes upregulated in the presence of ethanol alone

Certain genes were upregulated by exposure to ethanol alone, for example, genes related to biotin synthesis (BIO5 and BIO3) and transport (VHT1). Biotin plays an important role in nitrogen, amino acid and lipid metabolism (Bohlscheid, 2007), and is required for the activation of the acetyl-CoA carboxylases (ACC1, HFA1), which are key enzymes in de novo fatty acid synthesis. One of the most highly upregulated genes by ethanol (by a factor of 15.3) is YHB1, a flavohemoglobin. Its exact biological role is still controversial, but it has been suggested that the YHB1 gene may take part in oxygen stress response protection against reactive nitrogen species (Lewinska & Bartosz, 2006).

Genes upregulated in the presence of n-butanol alone

Among the 90 genes upregulated only by n-butanol, 25 (27.8%) were localized to the mitochondrion. Eighteen (20%) were associated with the nucleus, 14 with plasma membrane (15.6%) and nine with the ER (10%). The remaining 24 genes were localized to other cellular components. Most of the upregulated genes were involved in metabolism (49 genes, 54.4%), mainly cellular transport (20 genes, 22.2%). Nine genes were involved in signal transduction (10%), and 11 were related to an unknown biological process (12.2%).

Validation of the microarray experiments

To test the validity of the microarray data, we carried out QRT-PCR analysis on 10 genes affected by ethanol and n-butanol with a range of fold changes using 18S rRNA gene expression as the reference gene. The values are an average of three replications normalized with respect to 18S rRNA gene expression (Table 2). The QRT-PCR analysis agreed well with the microarray data and provided independent verification of the changes in the transcript levels of the genes that we discuss in this paper. Real-time PCR results of sul1 are contradictory to the microarray results, where, according to real-time PCR, expression is reduced in the presence of ethanol (both microarray and real-time tested at least three times). This discrepancy between microarray and real-time PCR results may be due to technical inherent experimental differences, the choice of oligonucleotides, sensitivity or variability in expression levels or detection.

Discussion

A number of gene expression studies have been published regarding yeast cells' ability to cope with and survive sudden stressful changes in their environment. These studies have generally addressed cells grown under standard conditions (e.g. a defined liquid medium, a temperature of 25–30 °C or an enological setting), in which the cells are exposed briefly to a challenging growth condition and their genome-wide behavior is then analyzed. Previous studies on alcohol exposure using DNA microarrays have similarly focused only on short-term exposure (e.g. 30 min) of cells growing in the presence of alcohol in a liquid medium (Alexandre, 2001). These studies have identified common short-term responses, for example, the ‘environmental stress response’ (ESR) genes (Gasch, 2000) or the ‘common environmental response’ (CER) genes (Causton, 2001). The present study focuses on the regulation of S. cerevisiae genes under conditions of long-term exposure that render the cells hemolytic (Shuster, 2004, 2007). Thus, many of the upregulated genes reported here represent late response genes that are not drastically altered following short exposure and vice versa. For example, several studies report the induction of heat shock proteins (HSPs) and trehalose accumulation in yeast upon short-term exposure to ethanol (Plesset, 1982; Sanchez, 1992; Mansure, 1994; Alexandre, 2001), whereas under the conditions studied here, trehalose metabolism appears to be shutdown, and most of the HSPs are downregulated or unchanged in our study, suggesting that they play a brief role upon initial exposure of the yeast cell to alcohol (e.g. SSA1, SSA2, SSA3, SSA4, HSP12, HSP26, HSP78, HSP104, SSE1). One interesting exception is HSP30 in this study (7.6-fold), which is highly upregulated by ethanol in this study, suggesting a prolonged role following exposure (the role of this gene is not yet clear). In general, the results presented here suggest that long-term exposure of cells to both ethanol and n-butanol involves coping with uncontrolled influx of alcohol, leading to the production of vast amounts of acetyl Co-A and ensuing reactive oxygen species (ROS). Whereas ethanol is oxidized to acetaldehyde and then to acetate, n-butanol is oxidized to butyraldehyde and then to butyrate. As we have shown previously, the two major aldehyde dehydrogenase cytosolic (ALD6) and major mitochondrial (ALD4) mutants have MACH-reduced and MACH-negative phenotypes, respectively, implying that further metabolism of the aldehyde to the corresponding acid is critical for MACH (Shuster, 2007). Acetate is further metabolized to acetyl-CoA by an acetyl-CoA synthetase in the cytosol or the mitochondrion. Butyrate can be further metabolized to acetyl-CoA by the process of β-oxidation in the peroxisome (hydrogen peroxide is also produced during this process). Acetyl-CoA in the peroxisome can be trafficked to lipid synthesis or shuttled out by carnitine transporters to the mitochondrion. In the mitochondrion, the overaccumulated acetyl-CoA enters the tricarboxylic acid (TCA) cycle. The final metabolic pathway of the TCA cycle is the respiratory chain. The high levels of hydrogen peroxide produced as a respiratory byproduct (mainly via cytochrome complex III) (Kwon, 2003) following the alcohol challenge result in the upregulation of genes (e.g. CTT1, CCP1) that cope with excess hydrogen peroxide and other ROS. The main genes related to hydrogen peroxide degradation are the cytoplasmic catalase T (CTT1), the mitochondrial cytochrome c peroxidase (CCP1) and the peroxisomal catalase A (CTA1). The latter gene product breaks down hydrogen peroxide in the peroxisomal matrix formed by acyl-CoA oxidase (Pox1p) during fatty acid β-oxidation (Rosenfeld & Beauvoit, 2003). CTA1 upregulation indicates that fatty acids are being catabolized to acetyl-CoA. This process is more drastic in the case of n-butanol oxidation, as indicated by the upregulation of POX1 (3.2-fold), presumably due to the high levels of butyrate generated (Fig. 1).

1

Aerobic metabolism of alcohol and the detoxification of hydrogen peroxide (H2O2). Ethanol is oxidized to acetaldehyde and then to acetate. Acetate can be further metabolized to acetyl-CoA by an acetyl-CoA synthetase in the cytosol or the mitochondrion. Acetyl-CoA in the cytosol can be trafficked to lipid synthesis (peroxisome) or shuttled out by carnitine transporters to the mitochondrion. n-Butanol is oxidized to butyraldehyde and then to butyrate. Butyrate can be further metabolized to acetyl-CoA by the process of β-oxidation in the peroxisome (H2O2 is produced during this process). In the mitochondrion, acetyl-CoA enters the TCA cycle. The final metabolic pathway of the TCA cycle is the respiratory chain. H2O2 produced by respiratory chain complex III can be detoxified by cytochrome c peroxidase (CCP1) inside the mitochondrion. Once in the cytosol, the main enzyme that degrades it is the cytoplasmic catalase T (CTT1). H2O2 can also be formed in the peroxisome during fatty acid β-oxidation (n-butanol metabolism). It is degraded there by the peroxisomal catalase CTA1.

Two other enzymes in the detoxification of ROS in yeast are the superoxide dismutases (SODs): Sod1p, which localizes to the cytoplasm and the mitochondrial intermembrane space, and Sod2p, a mitochondrial matrix protein (Table 6).

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6

Fold change of the main ROS-degrading genes

Standard gene nameSystematic nameFold change (ethanol)Fold change (n-butanol)Gene description
CCP1YKR066C3.58.6Mitochondrial cytochrome c peroxidase; degrades reactive oxygen species in the mitochondria
CTT1YGR088W12.23.6Cytosolic catalase T, has a role in protection from oxidative damage by hydrogen peroxide
CTA1YDR256C23.4Catalase A, breaks down hydrogen peroxide in the peroxisomal matrix formed by acyl-CoA oxidase (Pox1p) during fatty acid β-oxidation
SOD1YJR104C2Cu, Zn superoxide dismutase
SOD2YHR008C3.4Manganese-containing superoxide dismutase; protects cells against oxygen toxicity

Less direct ways of coping with the oxidative stress are also evident from the results. Among the highly upregulated genes, STL1 encodes a glycerol proton symporter of the plasma membrane, which is sensitive to osmotic shock (Ferreira, 2005). The induction of STL1 may be promoted by Hog1p (1.8, 2.6) (Rep, 2000). STL1 has been described as inducible by growth on nonfermentable carbon sources, including ethanol (Lages & Lucas, 1997). Glycerol has varied cellular functions, including cytosolic/mitochondrial redox balance, lipid synthesis (Ferreira & Lucas, 2007) and protection against oxidative stress (Påhlman, 2001), all of which are related to aerobic growth.

Aldehyde detoxification

The ongoing metabolism of alcohol provides the cells with another challenge – excess accumulation of aldehydes. SUL1 (18.5-fold, 114.1-fold) encodes a high-affinity sulfate permease. Sulfate uptake is mediated by specific sulfate transporters Sul1p and Sul2p, which control the concentration of endogenous activated sulfate intermediates. SUL2 is upregulated only by n-butanol (5.3-fold). Sulfur is essential for glutathione production, which plays a role in the inactivation of ROS, but also for the biosynthesis of sulfur-containing amino acids, which play a role in the detoxification of aldehydes (Aranda & del Olmo, 2004). Homocysteine and methionine are the major amino acids used by the cell for this purpose. The biosynthesis of these amino acids is upregulated [homocysteine production from homoserine (MET2 6.9-fold, 12.0-fold), methionine production from homocysteine (MET6 2.2-fold, 3.3-fold) and homocysteine production from cystathionine (STR3 4.7-fold by n-butanol)].

The high-affinity methionine permease (MUP1) is highly upregulated (7.1-fold) by n-butanol, but not by ethanol.

In recent years, there has been increasing interest in the mitochondrial COX genes, which encode critical structural proteins in the mitochondrial respiratory chain complex IV. Complex IV plays a fundamental role in the energy production of aerobic cells. This multimeric enzyme catalyzes the last step of respiration: the transfer of electrons from cytochrome c to molecular oxygen. The central role of COX in aerobic metabolism is highlighted by its participation in respiratory control.

In humans, COX deficiency has been shown to be the most frequent cause of mitochondrial encephalomyopathies, a heterogeneous group of human disorders characterized by alteration of aerobic energy production (OXPHOS defects) (Fontanesi, 2008).

The present study has focused on chromosomal genes whose regulation has been altered. It would be of potential interest to determine whether mitochondrial encoded genes in yeast are upregulated similar to the many mitochondrial genes encoded on the chromosome by alcohol. Furthermore, it would be interesting to determine whether human subjects with altered COX genes show differences in their response to alcohol challenge.

In summary, alcohol-mediated hemolysis in yeast appears to be related to the overproduction of hemolytic byproducts, particularly hydrogen peroxide, which accumulates during long-term exposure of S. cerevisiae to both ethanol and n-butanol. This phenomenon appears to be unrelated to the effects of short-term ethanol exposure, which primarily involves HSPs and other mechanisms. Further research is required to determine the potential effect of hydrogen peroxide elaboration.

Supporting Information

Table S1. Genes upregulated by ethanol.

Table S2. Genes upregulated by n-butanol.

Table S3. Genes downregulated by ethanol.

Table S4. Genes downregulated by n-butanol.

Table S5. Genes down-regulated by both ethanol and n-butanol.

Footnotes

  • Editor: Monique Bolotin-Fukuhara

References

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