Comprehensive gene expression analysis of the response to straight-chain alcohols in Saccharomyces cerevisiae using cDNA microarray


K. Fujita, National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Higashi no. 6, Tsukuba, Ibaraki, 305-8566, Japan (e-mail:


Aims:  The purpose of this study was to examine the gene expression profiles of yeast Saccharomyces cerevisiae subjected to straight-chain alcohols.

Methods and Results:  Lipophilic alcohols with high log Pow values were more toxic to yeast than those with low log Pow values. Morphological changes after exposure to ethanol, 1-pentanol, 1-octanol were observed, whereas n-pentane as a model hydrocarbon affected the surface of the outer membrane, with little change in organelles. Using cDNA microarrays, quite a few up-regulated gene categories were classified into the category ‘cell rescue, defence and virulence’ by ethanol, and the category ‘energy’ and ‘metabolism’ by 1-pentanol. Meanwhile, the characteristic genes up-regulated by n-pentane were not observed, and the expression profile was distantly related to ethanol, 1-pentanol and 1-octanol.

Conclusions:  This study suggests that gene expression profiles at the whole genome level were intimately associated with the cell growth inhibition and morphological changes by straight-chain alcohols with differing log Pow values.

Significance and Impact of the Study:  The study of comprehensive gene expression profiles by cDNA microarrays elucidates the straight-chain alcohol adaptation mechanisms.


The organic compounds are frequently toxic to micro-organisms, and inhibit cell growth and metabolism. These issues stand in the way of toxicological study or development for industrial applications e.g. biocatalytic engineering, wastewater handling, etc. The hydrophobicity of organic compounds is a vital factor in its cell activity. The log Pow, defined as the logarithm of a solvent's octanol and water partition coefficient, correlates with the toxicity of organic solvents (Salter and Kell 1995). Lipophilic compounds with a log Pow <4–5 are more toxic than those with lower log Pow values, whereas hydrophobic compounds with a log Pow >4–5 are not toxic to micro-organisms (Weber and de Bont 1996). Investigation on the effects of log Pow on the properties of the cell membrane might be useful to elucidate cell adaptation mechanisms (Sikkema et al. 1995; Weber and de Bont 1996).

In recent years, cDNA microarray or DNA chip technology has been developed and widely used in the fields of genomic study, disease prevention, drug discovery, toxicology and diagnostic microbiology (Blohm and Guiseppi-Elie 2001; Shoemaker and Linsley 2002). The yeast Saccharomyces cerevisiae cDNA microarray is ca 6000 open reading frames (ORFs) spotted onto a glass slide, and previous studies include response to various chemical substances e.g. hydrogen peroxide, the superoxide-generating drug menadione, the sulphydryl-oxidizing agent diamide, the disulphide-reducing agent dithiothreitol (Gasch et al. 2000), pesticides, heavy metals (Momose and Iwahashi 2001; Kitagawa et al. 2003), and antimicrobial compounds used in food materials (Kurita et al. 2002). The complete genome sequence of S. cerevisiae and databases such as the Saccharomyces Genome Database (SGD) (, the Munich Information Center Yeast Genome Database (MIPS) ( and the Yeast Protein Database (YPD) ( allow us to accurately monitor the experimental genome expression data. The yeast cDNA microarray technique can measure almost all of the yeast genome at once, so that the molecular network activated in response to chemical substances can be studied. In this study, we attempted to elucidate the effects of straight-chain alcohols with differing log Pow on global gene expression in S. cerevisiae using yeast cDNA microarray in contrast with morphological studies.

Materials and methods

Yeast strain and pregrowth conditions

Saccharomyces cerevisiae S288C (alpha SUC2 mal mel gal2 CUP1) was grown overnight at 25°C in YPD medium [10 g bacto yeast extract, 20 g bacto peptone per litre and 2% (w/v) glucose]. Mid-log phase (O.D. at 660 nm = 0·5) cell suspensions were used as an untreated control sample, and then incubated in the presence of straight-chain alcohols or hydrocarbons at 25°C for 120 min as chemical-treated samples for flow cytometry (FCM), transmission electron microscopy (TEM) and cDNA microarray experiments.

Spot susceptibility test

In order to assess the effect of chemicals on cell growth, 10 μl of cell suspensions treated with chemicals at 25°C for 120 min were dropped onto YPD agar plates at 25°C for 3 days. The EC50 of each chemical was estimated to be the concentration required to reduce cell growth to ca 50% of the untreated cell spot size.

Flow cytometry analysis

Side scatters (SS) value for the inner cellular structure was quantified with an EPICS XLTM flow cytometry (Beckman Coulter Inc., Hialeah, FL, USA) equipped with a 15 mW argon-ion laser (excitation wavelength, 488 nm). Cells were washed three times with 100 mmol l−1 potassium phosphate buffer (pH 7·2) and resuspended in the same buffer. A total of 5000 cells were detected per sample. Data were analysed using the computer program windows multiple document interface flow cytometry application (WinMDI; J. Trotter, Salk Institute for Biological Studies, San Diego, CA, USA).

Transmission electron microscopy

Transmission electron microscopy was performed according to the method of Osumi et al. (1974) with some minor alternations (Fujita et al. 1998). Cells were prefixed in 2·5% glutaraldehyde solution, prepared in 100 mmol l−1 potassium phosphate buffer (pH 7·2), for 120 min at 4°C, washed three times in buffer and incubated in 3% potassium permanganate solution for 90 min at room temperature. Samples embedded in 2% agarose were stained with 2% uranyl acetate for over 30 min at 4°C, dehydrated using a graded acetone series (50, 70, 80, 90, 95 and 99·5%), and then embedded in Quetol 653 mixture (Nisshin EM Ltd., Tokyo, Japan). Ultrathin sections were stained with 6% uranyl acetate and 0·4% lead citrate and viewed with a JEM-1010 (JEOL Ltd, Tokyo, Japan) transmission electron microscope at a voltage of 100 kV.

Preparation of mRNA for microarrays hybridization

When the CFUs of control cells was 100%, the concentration of the added chemicals was determined so that it might be ca 120% of CFUs. This indicates that although cell growth was inhibited by chemicals, the cells did not die. Poly (A)+ mRNA was purified from total RNA (Takara Bio Inc., Otsu, Shiga, Japan) extracted from harvested cells by the conventional hot phenol–chloroform method. The RNA extracted from untreated control or chemical-treated cells were labelled with Cy3-dUTP or Cy5-dUTP, respectively (Amersham Biosciences, Piscataway, NJ, USA). The fluorescent Cy3- or Cy5-labelled cDNA probes were combined after purification (Amersham Biosciences) and hybridized onto microarray slides containing probes for ca 6200 yeast ORFs (DNA Chip Research Inc., Yokohama, Japan) at 65°C overnight. Slides were washed twice with 2× SSC (0·015 mol l−1 sodium citrate, 0·15 mol l−1 NaCl, pH 7·0) and 0·1% SDS for 20 min, twice with 0·2 × SSC and 0·1% SDS for 20 min, once with 0·2 × SSC for 10 min and once with 0·05 × SSC for 10 min, at room temperature before drying.

Scanning and quantification of microarrays data

Hybridized slides were scanned with a ScanArray 4000® scanner (GSI Lumonics Inc., Billerica, MA, USA). The scanned images were analysed numerically using GenePixTM Pro 3·0 (Axon Instruments Inc., Union City, CA, USA). Gene expression ratios (normalized Cy5 intensity/normalized Cy3 intensity) >3·0 as up-regulated genes, and ratios of <0·5 as down-regulated genes were calculated and normalized with the median value. Up-regulated genes were classified into cell function categories provided by the MIPS. All gene expression data from two independent experiments were adopted. P-values were calculated using a Student's t-test (P < 0·05), and the significance of each gene was confirmed. Some invalid data derived from a lone highly expressed gene in one condition, and questionable data set (the 95·45% confidence level) were excluded from the statistical analysis using the Microsoft Excel program (Kitagawa et al. 2003). The complete data sets are available on the website,


Effects of straight-chain alcohols or hydrocarbons on S. cerevisiae cell growth

We examined the effects of a variety of straight-chain alcohols or hydrocarbons which have a wide range of log Pow values, on yeast cell growth by spot susceptibility test. Physical constants of the chemicals used in this study are shown in Table 1 (Dean 1979; Sangster 1989). The results showed that the effects of the straight-chain alcohols on cell growth correlated with their log Pow values as shown in Fig. 1. The straight-chain alcohols with high log Pow values, such as 1-octanol (log Pow, 3·07), 1-nonanol (log Pow, 4·02) and 1-decanol (log Pow, 4·57) were more toxic to yeast cells than those with low log Pow values, such as ethanol (log Pow, −0·30). Alternately, yeast cells were less sensitive to hydrocarbons, n-pentane (log Pow, 3·45) or n-hexane (log Pow, 4·00), and not toxic to n-heptane (log Pow, 4·50), n-octane (log Pow, 5·15), n-nonane (log Pow, 5·65), n-decane (log Pow, 6·25) and n-dodecane (log Pow, 6·80) with high log Pow values. We performed morphological experiments and genetic analysis to elucidate the relationships between cell growth inhibition and their log Pow values of ethanol, 1-pentanol (log Pow, 1·51), 1-octanol, and n-pentane as straight-chain alcohols or a hydrocarbon models.

Table 1.  Physical constants of straight-chain alcohols and hydrocarbons used in this study
ChemicalsFormulalog PowSolubility*
  1. *In 100 part (water).

Straight-chain alcohols
 1-ButanolCH3(CH2)2CH2OH0·849·0 (15°C)
 1-PentanolCH3(CH2)3CH2OH1·512·7 (22°C)
 1-HexanolCH3(CH2)4CH2OH2·030·6 (20°C)
 1-HeptanolCH3(CH2)5CH2OH2·620·18 (25°C)
 1-OctanolCH3(CH2)6CH2OH3·070·054 (20°C)
 n-PentaneCH3(CH2)3CH33·450·036 (16°C)
 n-HeptaneCH3(CH2)5CH34·500·0052 (18°C)
 n-OctaneCH3(CH2)6CH35·150·002 (16°C)
Figure 1.

Effect of various straight-chain alcohols and hydrocarbons on cell growth in Saccharomyces cerevisiae. Log-phase cells were incubated in the presence of straight-chain alcohols (solid circle) or hydrocarbons (open circle) at 25°C for 120 min and then dropped onto Yeast Protein Database medium before incubation at 25°C for 3 days. The EC50 of each chemical was estimated to be the concentration required to reduce cell growth to ca 50% of untreated the cell spot size

Morphological changes in cells exposed to the chemicals

Flow cytometry was used to examine the changes in intracellular structures after exposure to a series of concentrations of ethanol, 1-pentanol, 1-octanol and n-pentane (Fig. 2). Exposure to high concentrations of ethanol, 1-pentanol and 1-octanol shifted the lower SS values. The sideways-scattered light is affected by several parameters, including granularity, cell size and cell morphology (Rieseberg et al. 2001). Thus, it is likely that these alcohols permeated cell membranes and organelles, and simplified the intracellular structures. In contrast, the SS values were not changed by any n-pentane concentration, suggesting that n-pentane hardly deforms intracellular structures.

Figure 2.

Intracellular structural alteration by ethanol (control, blue; 9%, pink; 50%, orange and 100%, light blue), 1-pentanol (control, blue; 0·45%, pink; 10%, orange and 100%, light blue), 1-octanol (control, blue; 0·01%, pink; 0·5%, orange and 100%, light blue), and n-pentane (control, blue; 1·5%, pink; 10%, orange and 100%, light blue). Side scatters (SS) values for the inner cellular structure of 5000 cells were individually detected

We also observed changes in intracellular conformation after exposure to ethanol, 1-pentanol, 1-octanol and n-pentane, using TEM (Fig. 3). Changes to intracellular structures were not observed under sublethal conditions, e.g. 9·0% ethanol, 0·45% 1-pentanol, 0·01% 1-octanol, and 1·5%n-pentane (data not shown). Exposure to higher concentrations, cell growth were completely inhibited (data not shown), 50% ethanol or 10% 1-pentanol, organelles such as the nucleus, vacuole and mitochondria disappeared. 1-octanol (0·5%) remarkably affected the conformation of intracellular structures, and deformed organelles (Fig. 3b–d). Alternately, 10%n-pentane affected the surface of the outer membrane but organelles were less affected (Fig. 3e).

Figure 3.

Morphological changing ethanol, 1-pentanol, 1-octanol, and n-pentane under lethal conditions. Log-phase cells were incubated in the presence of various chemicals at 25°C for 120 min. Electron micrographs (TEM) show the untreated control cell (a), 50% ethanol-treated cell (b), 10% 1-pentanol-treated cell (c), 0·5% 1-octanol-treated cell (d) and 10%n-pentane-treated cell (e) (N, nucleus; M, mitochondria; V, vacuole; bars, 500 nm)

Characterization of genes affected by ethanol, 1-pentanol, 1-octanol and n-pentane

Of 5509 valid gene spots, 271 genes were up-regulated after exposure to 9·0% ethanol. Up-regulated genes were predominantly classified into the subcategories ‘stress response’ (10·8%) from the category ‘cell rescue, defence and virulence’ (9·7%). Indeed, genes from the heat shock protein family e.g. HSP12 (YFL014W), HSP26 (YBR072W), HSP78 (YDR258C), HSP82 (YPL240C), HSP104 (YLL026W) and HSP70 family members encoding SSA3 (YBL075C), SSA4 (YER103W) and SSE2 (YBR169C) were dominantly induced by 9·0% ethanol (Table 2).

Table 2.  Up-regulated genes in the subcategories ‘stress response’ from the category ‘cell rescue, defence and virulence’
Open reading framesGeneDescriptionGene expression ratios*
  1. *Numerical values represent gene expression ratios (normalized Cy5 intensity/normalized Cy3 intensity).

  2. IV, invalid value.

YGR088WCTT1Cytoplasmic catalase T2·53·34·70·2
YEL039CCYC7Iso-2-cytochrome c1·24·33·60·9
YML070WDAK1Putative dihydroxyacetone kinase1·93·13·61·0
YOL052CDDR2DNA damage responsive3·133·5IVIV
YMR173WDDR48Flocculent-specific protein1·64·74·11·0
YHR104WGRE3A keto-aldose reductase1·83·03·00·5
YIR038CGTT1Glutathione transferase2·09·21·31·0
YMR251WHOR7Hyperosmolarity-responsive gene4·32·43·0IV
YMR186WHSC82Constitutively expressed heat shock protein4·40·41·2IV
YLL026WHSP104104-kDa heat shock protein13·01·71·90·8
YFL014WHSP1212-kDa heat shock protein8·918·97·70·2
YBR072WHSP26Heat shock protein 2645·513·24·50·4
YCR021CHSP30Protein induced by heat shock, ethanol treatment and entry into stationary phaseIV4·70·91·1
YDR258CHSP78Mitochondrial HSP78 family6·42·31·20·5
YPL240CHSP8282-kDa heat shock protein5·50·51·1IV
YIR037WHYR1Putative glutathione peroxidase0·75·81·71·1
YPR141CKAR3Kinesin-like nuclear fusion protein5·8IV0·70·9
YMR174CPAI3Cytoplasmic inhibitor of proteinase Pep4p7·22·2IVIV
YJL223CPAU1Member of the seripauperin protein family7·91·31·10·6
YLR461WPAU4Member of the seripauperin protein family4·70·41·61·2
YFL020CPAU5Member of the seripauperin (PAU) family3·81·60·92·2
YNR076WPAU6Member of the seripauperin family5·41·01·61·2
YKL163WPIR3Protein containing tandem internal repeats1·98·92·0IV
YBR103WSIF2535 amino acid protein4·20·80·51·1
YMR095CSNO1SNZ1 proximal ORF, stationary phase induced geneIV9·8IV1·0
YMR096WSNZ1Encodes highly conserved 35 kDa protein1·046·6IV1·0
YBL075CSSA3Heat-inducible cytosolic member of HSP70 family3·70·90·60·8
YER103WSSA4Member of HSP70 family58·80·6IV0·5
YBR169CSSE2HSP70 family member, highly homologous to Sse1p4·82·92·71·1
YBR044CTCM62Mitochondrial protein; (putative) chaperone1·23·81·11·5
YCR083WTRX3Mitochondrial thioredoxin0·81·33·11·4
YDR059CUBC5Ubiquitin-conjugating enzyme0·83·11·50·8
YKR042WUTH1Involved in cell growth0·6IV4·9IV
YBR054WYRO2Homologue to HSP30 heat shock protein4·9IV0·2IV

Of 5499 valid gene spots, 373 genes were up-regulated by 0·45% 1-pentanol. Quite a few up-regulated genes were classified into the subcategory ‘stress response’ (10·2%) from the category ‘cell rescue, defence and virulence’ (9·7%; Fig. 4). Highly stress-responsive genes included not only the heat shock proteins encoding HSP12, HSP26 and HSP30, but also cytoplasmic catalase, CTT1 (YGR088W) and ubiquitin, UBI4 (YLL039C) (Table 2). In addition, genes classified into the subcategory ‘TCA cycle’ (48·0%), ‘metabolism of energy reserves’ (10·8%), ‘glycolysis and gluconeogenesis’ (20·0%) from the category ‘energy’ (17·1%) were up-regulated (Fig. 4 and Table 3). It is notable that 1-pentanol-inducible genes are mainly TCA cycle related genes e.g. FUM1 (YPL262W), IDH1 (YNL037C), IDH2 (YOR136W), LPD1 (YFL018C), LSC1 (YOR142W), LSC2 (YGR244C) and SDH2 (YLL041C). Furthermore, a large number of genes subcategorized into ‘amino acid biosynthesis’ (33·6%) and ‘amino acid degradation’ (20·0%) were up-regulated by 1-pentanol (Table 4). Meanwhile, most down-regulated genes were in the subcategory ‘ribosome biogenesis’ (42·8%), ‘translation’ (39·1%) and the category ‘protein synthesis’ (36·5%) (data not shown).

Figure 4.

Genes up-regulated greater than threefold by 9·0% ethanol (shaded bars), 0·45% 1-pentanol (open bars), 0·01% 1-octanol (solid bars) and 1·5%n-pentane (hatched bars). The y-axis shows the category of gene expression. 1, cell cycle and DNA processing [628 open reading frames (ORFs)]; 2, cell fate (427 ORFs); 3, cell rescue, defence and virulence (278 ORFs); 4, cellular communication/signal transduction mechanism (59 ORFs); 5, cellular transport and transport mechanisms (495 ORFs); 6, classification not yet cleared-cut (115 ORFs); 7, control of cellular organization (209 ORFs); 8, energy (252 ORFs); 9, metabolism (1066 ORFs); 10, protein activity regulation (13 ORFs); 11, protein fate (folding, modification, destination) (595 ORFs); 12, protein synthesis (359 ORFs); 13, protein with binding function or cofactor requirement (structural or catalytic) (four ORFs); 14, Regulation of/interaction with cellular environment (199 ORFs); 15, subcellular localization (2258 ORFs); 16, transcription (771 ORFs); 17, transport facilitation (313 ORFs); 18, transposable elements, viral and plasmid proteins (116 ORFs); 19, unclassified proteins (2399 ORFs). Categories are referred to in the Munich Information Center for Protein Sequences (MIPS,

Table 3.  Up-regulated genes in the subcategories ‘metabolism of energy rescue’, ‘TCA cycle’ and ‘glycolysis and gluconeogenesis’ from the category ‘energy’
ORFGeneDescriptionGene expression ratios*
  1. *Numerical values represent gene expression ratios (normalized Cy5 intensity/normalized Cy3 intensity).

  2. TCA, tricarboxylic acid; IV, invalid value; UDP, uridine diphosphate; NADP nicotinamide-adenine dinucleotide phosphate; ADP, adenosine diphosphate.

Metabolism of energy reserves
 YOR178CGAC1Regulatory subunit for Glc7p8·07·06·41·5
 YPR184WGDB1Glycogen debranching enzyme1·511·83·80·6
 YPR160WGPH1Glycogen phosphorylase2·19·45·70·4
 YLR258WGSY2Glycogen synthase (UDP-glucose-starch glucosyltransferase)1·04·62·10·7
 YPL240CHSP8282-kDa heat shock protein; homologue of mammalian Hsp905·50·51·1IV
 YLR273CPIG1Protein similar to Gac1p, a putative type 1 protein phosphatase targeting subunit0·8IV7·01·2
TCA cycle
 YLR304CACO1Aconitase, mitochondrial0·83·51·40·7
 YNR001CCIT1Citrate synthase0·94·11·21·0
 YPR001WCIT3Mitochondrial isoform of citrate synthase0·73·51·00·6
 YPL262WFUM1Mitochondrial and cytoplasmic fumarase1·13·50·80·8
 YNL037CIDH1Alpha-4-beta-4 subunit of mitochondrial isocitrate dehydrogenase11·03·31·11·1
 YOR136WIDH2NAD+-dependent isocitrate dehydrogenase1·34·51·70·6
 YDL066WIDP1Mitochondrial form of NADP-specific isocitrate dehydrogenase1·29·24·11·0
 YDR148CKGD2Dihydrolipoyl trans-succinylase component of alpha-ketoglutarate dehydrogenase complex1·62·33·01·1
 YFL018CLPD1Dihydrolipoamide dehydrogenase precursor1·13·81·81·2
 YOR142WLSC1Succinate-CoA ligase (ADP forming)1·03·01·21·0
 YGR244CLSC2Succinate-CoA ligase (ADP forming)1·13·92·70·8
 YLL041CSDH2Succinate dehydrogenase (ubiquinone) iron–sulphur protein subunit1·13·71·31·3
Glycolysis and gluconeogenesis
 YDL021WGPM2Phosphoglycerate mutase involved in glycolysis13·24·94·21·0
 YFR053CHXK1Hexokinase I (PI) (also called hexokinase A)4·110·93·5IV
 YKR097WPCK1Phosphoenolpyruvate carboxylkinase1·44·33·00·9
 YCR012WPGK13-Phosphoglycerate kinase3·8IV0·6IV
 YBR218CPYC2Pyruvate carboxylase1·33·63·0IV
 YOR347CPYK2Pyruvate kinase1·13·02·41·2
 YJL052WTDH1Glyceraldehyde-3-phosphate dehydrogenase 1IV4·0IVIV
 YDR050CTPI1Triosephosphate isomeraseIVIV0·36·3
Table 4.  Up-regulated genes in the subcategories ‘amino acid biosynthesis’ and ‘amino acid degradation’ from the category ‘metabolism’
ORFGeneDescriptionGene expression ratios*
  1. *Numerical values represent gene expression ratios (normalized Cy5 intensity/normalized Cy3 intensity).

  2. IV, invalid value.

Amino acid biosynthesis
 YLR027CAAT2Aspartate aminotransferase, cytosolic1·65·61·91·1
 YOL058WARG1Arginosuccinate synthetase2·683·4IV1·3
 YJL071WARG2Acetylglutamate synthase2·24·51·31·2
 YJL088WARG3Ornithine carbamoyltransferase2·921·24·62·2
 YHR018CARG4Argininosuccinate lyase1·533·64·31·1
 YER069WARG5,6N-acetyl-gamma-glutamyl-phosphate reductase and acetylglutamate kinase7·88·211·71·4
 YOL140WARG8Acetylornithine aminotransferase1·24·1IV1·3
 YGL148WARO2Chorismate synthase1·03·31·20·7
 YDR035WARO3DAHP synthase0·97·37·40·8
 YBR249CARO43-Deoxy-d-arabino-heptulosonate 7-phosphate (DAHP) synthase isoenzyme0·74·32·20·9
 YHR208WBAT1Branched-chain amino acid transaminase0·83·91·41·3
 YOR303WCPA1Carbamoyl phosphate synthetase1·37·81·51·7
 YJR109CCPA2 Carbamyl phosphate synthetase2·017·3IV0·7
 YMR250WGAD1Glutamate decarboxylase1·54·84·80·3
 YCL030CHIS4Histidinol dehydrogenase0·710·92·50·6
 YIL116WHIS5Histidinol-phosphate aminotransferase1·012·34·21·0
 YBR248CHIS7Imidazole glycerol phosphate synthase1·44·01·70·8
 YDR158WHOM2Aspartic beta semi-aldehyde dehydrogenase0·69·00·10·8
 YER052CHOM3Aspartate kinase (l-aspartate 4-P-transferase)2·17·43·31·2
 YMR108WILV2Acetolactate synthase1·44·11·00·9
 YCL009CILV6Small regulatory subunit of acetolactate synthase0·94·02·31·0
 YCL018WLEU2Beta-IPM (isopropylmalate) dehydrogenase2·05·81·4IV
 YFL018CLPD1Dihydrolipoamide dehydrogenase precursor1·13·81·81·2
 YIR034CLYS1Saccharopine dehydrogenase1·58·1IV0·8
 YBR115CLYS2Alpha aminoadipate reductase0·94·5IV1·1
 YDL182WLYS20Homocitrate synthase1·04·72·91·0
 YDL131WLYS21Homocitrate synthase1·25·41·61·1
 YGL154CLYS5Aminoadipate-semialdehyde dehydrogenase small subunit1·23·51·81·4
 YJR010WMET3ATP sulphurylase3·31·82·70·9
 YER091CMET6Vitamin B12-(cobalamin)-independent isozyme of methionine synthase1·519·012·10·8
 YFR030WMET10Subunit of assimilatory sulphite reductase1·26·14·31·2
 YGL125WMET13Putative methylenetetrahydrofolate reductase1·611·02·21·9
 YPR167CMET163-phosphoadenylylsulphate reductase2·54·33·50·8
 YLR303WMET17O-acetylhomoserine-O-acetylserine sulphydralase0·69·30·10·6
 YER081WSER33-Phosphoglycerate dehydrogenase3·91·41·10·5
 YJR130CSTR2Cystathionine gamma-synthase1·26·01·31·0
 YGL184CSTR3Cystathionine beta-lyase2·43·30·80·6
 YBR294WSUL1Probable sulphate transport protein2·24·04·13·2
 YER090WTRP2Anthranilate synthase component I1·17·73·50·9
 YKL211CTRP3Anthranilate synthase component II and indole-3- phosphate1·34·72·50·6
 YDR354WTRP4Anthranilate phosphoribosyl transferase1·14·02·01·3
 YGL026CTRP5Tryptophan synthetase1·04·42·50·8
 YBR166CTYR1Prephenate dehydrogenase (NADP+)1·22·02·23·4
Amino acid degradation
 YHR208WBAT1Branched-chain amino acid transaminase0·83·91·41·3
 YJR025CBNA13-Hydroxyanthranilic acid dioxygenase1·234·58·71·2
 YCL064CCHA1Catabolic serine (threonine) dehydratase0·87·70·23·3
 YBR208CDUR1,2Urea amidolyase4·00·71·11·6
 YDR019CGCV1Glycine cleavage T protein0·98·32·60·6
 YMR189WGCV2Glycine decarboxylase complex, glycine synthase, glycine cleavage system0·85·91·60·4
 YDL215CGDH2NAD-dependent glutamate dehydrogenase0·93·01·81·5
 YOR040WGLO4Mitochondrial glyoxylase-II8·70·81·21·8
 YKL218CSRY1Pyridoxal-5phosphate-dependent enzyme0·810·83·51·0

Of 5191 valid gene spots, 176 genes were up-regulated after exposure to 0·01% 1-octanol. Up-regulated genes were classified into the subcategories ‘stress response’ (8·0%) from the category ‘cell rescue, defence and virulence’ (6·8%), ‘metabolism of energy reserves’ (13·5%) from the category ‘energy’ (7·5%), and ‘amino acid biosynthesis’ (11·8%) from the category ‘metabolism’ (4·1%) (Fig. 4 and Tables 2–4).

Of 5357 valid gene spots, 35 genes were up-regulated by 1·5%n-pentane. The characteristics of up-regulated genes were not observed. The ratios of expression changes were not as high as those detected after ethanol, 1-pentanol and 1-octanol treatment (Fig. 4 and Tables 2–4).


It has been shown that lipophilic organic compounds with high log Pow values are more toxic to micro-organisms than those with low log Pow values (Weber and de Bont 1996). In addition, organic compounds with a log Pow between 1·5 and 4·4 are extremely toxic to micro-organisms (Ramos et al. 2002). These findings are consistent with our results, that the effect of various straight-chain alcohols on cell growth revealed a correlation with the log Pow values, and the alcohols with high log Pow values were more toxic to yeast cells than those with low log Pow values (Fig. 1). Straight-chain alcohols were found to cause large disorders in the glycerol backbone of the membrane lipids, suggesting that the terminal hydroxyl group of the compound is anchored near the aqueous interface of the lipid (Weber and de Bont 1996). In our morphological studies using both FCM and TEM, it was shown that exposure to lethal concentrations of ethanol, 1-pentanol and 1-octanol penetrate into the intracellularspace, and damaged organelles such as the nucleus, vacuole and mitochondria (Figs 2 and 3). These results suggests that the position of straight-chain alcohols in the membrane is closely involved in the relationships between log Pow values and the cell growth inhibition.

Several studies on induction of heat shock proteins and trehalose accumulation in yeast S. cerevisiae after ethanol exposure have been reported (Plesset et al. 1982; Sanchez et al. 1992). Alexandre et al. (2001) implied that a large number of up-regulated genes during short-term ethanol treatment were involved in environmental stress response and energy metabolism concerned with ionic homeostasis, heat protection, trehalose synthesis and antioxidant defence. These suggestions are consistent with our findings that stress-responsive genes e.g. heat shock protein family encoding HSP12, HSP26, HSP78, HSP82, HSP104 and HSP70 family members encoding SSA3, SSA4 and SSE2 were dominantly induced by 9·0% ethanol (Fig. 4 and Table 2).

Alternately, little is known about the effect of 1-pentanol or 1-octanol on gene expression in yeast S. cerevisiae. In our results, genes associated with the category ‘‘cell rescue, defence and virulence’, ‘energy’ and ‘metabolism’ were highly up-regulated by 1-pentanol or 1-octanol (Fig. 4 and Tables 2–4). The number of these genes affected by 1-pentanol was greater than those affected by 1-octanol; however, several up-regulated genes overlapped. When considering all these factors together: morphological results, individual gene expression and comprehensive gene expression profiles, it is likely that yeast cells develop similar defence systems against 1-pentanol and 1-octanol.

Increasing membrane lipid solubility using ethanol and other alkanols increase the passive influx of protons across the membrane, leading to the dissipation of proton motive forces (Leao and Van Uden 1984). Moreover, it has been suggested that ethanol stress can cause changes to the plasma membrane protein composition, reducing the levels of plasma membrane proton ATPase and inducing the plasma membrane-associated Hsp30. Hsp30 might have an energy conservation role, limiting excessive ATP consumption by plasma membrane proton ATPase during prolonged stress exposure (Piper 1995; Piper et al. 1997). Genes categorized in ‘amino acid biosynthesis’ and ‘amino acid degradation’ closely linked with genes in the ‘energy’ category, especially the ‘TCA cycle’ subcategory ( Quite a few genes subcategorized in ‘amino acid biosynthesis’ and ‘amino acid degradation’ were up-regulated by 1-pentanol and 1-octanol (Table 4). Thus, 1-pentanol and 1-octanol may increase respiration activity in order to distribute energy supply in a similar way of ethanol.

It is of interest to us here that the severity of cell growth affected by straight-chain alcohols or hydrocarbons with log Pow between 3·0 and 4·5 fluctuate. In spite of similar log Pow values, yeast cells were significantly more sensitive to alcohols such as 1-octanol (log Pow, 3·07) and 1-nonanol (log Pow, 4·02), whereas hydrocarbons, such as n-pentane (log Pow, 3·45) and n-hexane (log Pow, 4·00) were nontoxic. Furthermore, n-pentane and 1-pentanol, which have the same number of carbon atoms, equally inhibited the cell growth (Fig. 1). It has been reported that the cell growth inhibition has relevance to the log Pow values of lipophilic or hydrophobic organic compounds (Weber and de Bont 1996). However, our results indicate that the effect of straight-chain alcohols or hydrocarbons with log Pow between 3·0 and 4·5 on cell growth inhibition is determined not only by the log Pow values but also other factors, such as functional groups. Thus, we examined n-pentane as a hydrocarbon comparing it with the straight-chain alcohols. In our morphological results, n-pentane neither drastically simplified the intracellular structures nor did it change the shape of organelles (Figs 2 and 3e). Hydrocarbons do not interact with the head group and accumulate more deeply in the lipid bilayer (Weber and de Bont 1996). The shorter chain hydrocarbons, such as n-hexane and n-octane, increase the bilayer width by partitioning between apposing monolayers (McIntosh et al. 1980). Our results suggest that n-pentane does not penetrate through the cellular membrane or into the intracellularspace, and the cell surface affinity to n-pentane is different from those of straight-chain alcohols. Furthermore, exposed to 1·5%n-pentane, the ratios of expression changes were not as high as those detected after ethanol, 1-pentanol or 1-octanol treatment, and the characteristics of up-regulated genes were not observed (Fig. 4, Tables 2–4). These genetic results imply that the characteristics of functional groups may have an impact on the relationship between the cell growth inhibition and the log Pow values between 3·0 and 4·5. This will be the subject of our future investigation.


This work was supported with funds from NEDO (New Energy and Industrial Technology Development Organization, Japan). The authors wish to thank J. Takahashi (Daikin Industries) for FCM, R. Matsumoto (AIST) for TEM and Y. Momose and S. Kurita (AIST) for helping with microarray data analysis and technical assistance.