Identification of molecular pathways affected by pterostilbene, a natural dimethylether analog of resveratrol

1755-8794-1-7 1755-8794 Research article Identification of molecular pathways affected by pterostilbene, a natural dimethylether analog of resveratrol Pan Zhiqiang zpan@olemiss.edu Agarwal K Ameeta aagarwal@olemiss.edu Xu Tao taoxu@olemiss.edu Feng Qin qfeng@olemiss.edu Baerson R Scott sbaerson@msa-oxford.ars.usda.gov Duke O Stephen sduke@msa-oxford.ars.usda.gov Rimando M Agnes arimando@msa-oxford.ars.usda.gov

United States Department of Agriculture, Agricultural Research Service, Natural Products Utilization Research Unit, University, Mississippi 38677, USA

National Center for Natural Products Research, School of Pharmacy, University of Mississippi, University, Mississippi, 38677, USA

BMC Medical Genomics 1755-8794 2008 1 1 7 http://www.biomedcentral.com/1755-8794/1/7 18366703 10.1186/1755-8794-1-7 29 11 2007 20 3 2008 20 3 2008 2008 Pan et al; licensee 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.

Abstract

Background

Pterostilbene, a naturally occurring phenolic compound produced by agronomically important plant genera such as Vitis and Vacciunium, is a phytoalexin exhibiting potent antifungal activity. Additionally, recent studies have demonstrated several important pharmacological properties associated with pterostilbene. Despite this, a systematic study of the effects of pterostilbene on eukaryotic cells at the molecular level has not been previously reported. Thus, the aim of the present study was to identify the cellular pathways affected by pterostilbene by performing transcript profiling studies, employing the model yeast Saccharomyces cerevisiae.

Methods

S. cerevisiae strain S288C was exposed to pterostilbene at the IC₅₀concentration (70 μM) for one generation (3 h). Transcript profiling experiments were performed on three biological replicate samples using the Affymetrix GeneChip Yeast Genome S98 Array. The data were analyzed using the statistical methods available in the GeneSifter microarray data analysis system. To validate the results, eleven differentially expressed genes were further examined by quantitative real-time RT-PCR, and S. cerevisiae mutant strains with deletions in these genes were analyzed for altered sensitivity to pterostilbene.

Results

Transcript profiling studies revealed that pterostilbene exposure significantly down-regulated the expression of genes involved in methionine metabolism, while the expression of genes involved in mitochondrial functions, drug detoxification, and transcription factor activity were significantly up-regulated. Additional analyses revealed that a large number of genes involved in lipid metabolism were also affected by pterostilbene treatment.

Conclusion

Using transcript profiling, we have identified the cellular pathways targeted by pterostilbene, an analog of resveratrol. The observed response in lipid metabolism genes is consistent with its known hypolipidemic properties, and the induction of mitochondrial genes is consistent with its demonstrated role in apoptosis in human cancer cell lines. Furthermore, our data show that pterostilbene has a significant effect on methionine metabolism, a previously unreported effect for this compound.

Background

Pterostilbene is a naturally-occurring phytoalexin identified in several plant species. It belongs to a group of phenolic compounds known as stilbenes, and is found in the heartwood of sandalwood (Pterocarpus santalinus) 1 and P. marsupium 2. It was also identified in the leaves of Vitis vinifera 3, in infected grape berries of var. Chardonnay and Gamay 4, and in healthy and immature berries of var. Pinot Noir and Gamay 5. Pterostilbene has also been found in berries of some Vacciunium species 5. Pterostilbene, one of the most extensively studied secondary metabolites found in grapes and wine, is a dimethylether analog of resveratrol that is well known for its hypolipidemic activity. A considerable amount of research effort has been expended to address the biochemical and physiological effects of pterostilbene in animal and microbial systems. For example, the antioxidative activity of pterostilbene was first demonstrated in vitro by its inhibition of methyl linoleate oxidation 6. Pterostilbene was reported to scavenge 1,1-diphenyl-2-picryl-hydrazyl (DPPH) free radicals and to inhibit the oxidation of citronellal, and lipid peroxidation in rat liver microsomes and in cultured human fibroblasts 7. Pterostilbene isolated from Anogeissus acuminata (Family Combretaceae) is cytotoxic against a number of cancer cell lines, including human breast cancer and murine lymphoid neoplasma cells 89. More recently, it has been demonstrated that pterostilbene can reduce cholesterol levels in vivo in hamsters through the activation of the peroxisome proliferator-activated receptor α (PPARα) 10. Pterostilbene has been reported to reduce glucose and increase plasma insulin levels significantly in normal and diabetic rats 11. Furthermore, pterostilbene has been shown to be cancer-chemopreventive 812 and anti-inflammatory 13.

Investigation of the pathogen-host interactions of Vitis vinifera has led to the hypothesis that resistance is not due to preformed physical or chemical factors, but rather to an active defense mechanism that is triggered by the pathogen, of which stress metabolites including resveratrol, α-viniferin and ε-viniferin are an important component 14. Pterostilbene, produced in leaf tissues by various species of the Vitaceae family following fungal infection, proved to have more potent antifungal activity than resveratrol (reviewed in 31516). However, the mechanism by which pterostilbene inhibits fungi is not well understood. Results from early studies suggested that the biological activities of the compound mainly involved effects on the plasma membrane 517, and destruction of ribosomes, endoplasmic reticulum, and mitochondrial membranes 17. Further information on its precise mechanism of action would be useful not only for its potential development as a drug, but also in understanding its ecological significance to producing plant species. In the present study, using transcript profiling analysis, we monitored the gene expression profile of yeast cells treated with pterostilbene in an effort to identify the molecular pathways affected by this compound.

Methods

Yeast strains and media

S. cerevisiae S288C (MATα, SUC2, mal, mel, gal2, CUP1, flo1, flo8-1), obtained from ATCC (Manassas, VA), was used in the microarray experiments. The deletion strains and the isogenic wild type strain (BY4742) were obtained from Open Biosystems (Huntsville, AL). Synthetic dextrose (SD) medium, containing 0.67% (w/v) yeast nitrogen base without amino acids and 2% (w/v) dextrose, was used to grow the wild type S288C strain. Standard Yeast Peptone Dextrose (YPD) medium (1% yeast extract, 2% peptone, 2% dextrose) was used to grow the deletion strains and their respective isogenic wild type control. In all cases, the medium was buffered with 0.165 M 3- [N-Morpholino]propanesulfonic acid (MOPS) (Sigma-Aldrich Corp.) and the pH was adjusted to 7.0 with NaOH.

IC₅₀determination and yeast cultures for microarray studies

IC₅₀value (concentration of inhibitor required for 50 percent inhibition of growth) was first determined in a conventional microplate assay. The obtained IC₅₀was then further confirmed in 50-ml large-scale cultures as previously described in order to closely mimic the microarray experimental conditions 18. For microarray experiments, a single colony of S. cerevisiae was inoculated into 25 ml of SD medium and grown overnight at 30°C with shaking at 200 rpm. The culture was used to inoculate 50-ml of SD medium to an A₆₀₀of 0.1. Three independent 50-ml cultures were grown for pterostilbene treatments. In parallel, another set of three independent cultures were grown for solvent treatments which served as the untreated controls. When the cultures reached an A₆₀₀of 0.2, pterostilbene was added at 70 μM concentration from a stock solution of 0.5 M dissolved in dimethyl sulfoxide (DMSO). In the control cultures, 0.25% of DMSO was simultaneously added. The cultures were allowed to grow until an A₆₀₀of 0.5 was reached 18. Cells were harvested by centrifugation at 500 × g in a Sorvall centrifuge using a SH-3000 rotor. The cells were flash-frozen in liquid nitrogen and stored at -80°C until use.

RNA preparation and hybridization to Affymetrix GeneChips

Total RNA was isolated using the Qiagen RNeasy^®Midi-kit (Qiagen, Inc., Valencia, CA) with modifications as previously described 18. RNA was processed for target preparation using the GeneChip One-Cycle Target Labeling and Control Reagents kit (Affymetrix, Santa Clara, CA), according to the manufacturer's protocol. Microarray hybridization was performed using the Affymetrix GeneChip Yeast Genome S98 Array using protocols described by Affymetrix (Santa Clara, CA). All microarray data are deposited at the Gene Expression Omnibus as GSE10554.

Microarray data analysis

Normalized data from the Affymetrix software were analyzed using the GeneSifter microarray data analysis system (VizX Labs LLC, Seattle, WA; 19). This program identifies differentially expressed genes and establishes the biological significance based on Gene Ontology (GO) classification 2021 into biological process, molecular function and cellular component. The GeneSifter program also produces z-score reports. A z-score is a statistical rating of gene ontologies and indicates whether each GO term occurs more or less frequently than expected. The differential expression of genes was calculated by averaging the normalized triplicate samples and running a pairwise analysis 2223. Statistical significance was determined using Student t-test (two tail, unpaired) with correction factor for false discovery rate (Benjamini and Hochberg) included 24. Gene lists generated using GeneSifter were further analyzed using the Saccharomyces Genome Database 25.

Quantitative real-time RT-PCR assay

To confirm the results from microarray experiments, real-time quantitative RT-PCR was carried out using the same RNA preparations that were used in the microarray experiments. Total RNA was treated with DNase I and the RT-PCR reaction was performed as described 18. The sequences of the primers for each gene selected for the assays are listed in Table 1. Additionally, linear regression analysis as well as an F-Test to assess goodness-of-fit was performed on log₂-transformed expression ratios (pterostilbene treated:untreated cells) obtained from real-time RT-PCR and microarray experiments (see Additional file 1: Correlation between expression ratios obtained from quantitative real-time RT-PCR and microarray experiments) using the Microsoft Excel Analysis ToolPak add-in module, and a statistically-significant (p < 0.05) correlation between the two methods was indicated.

Additional file 1

Correlation between expression ratios obtained from quantitative real-time RT-PCR and microarray experiments.

Click here for file

Table 1

Gene-specific primer sequences for quantitative real-time RT-PCR analysis

Gene name

Forward Primer

Reverse primer

ORF

BAG7

5'-TGCTTCCAAGGTCACGTGC-3'

5'-GATGTATGGTAAAATTGTTGGAGTCAG-3'

YOR134W

RSB1

5'-ACCCGTTGTTGTTACCGTTTG-3'

5'-GATAGCCATCCCAACCGACA-3'

YOR049C

UPC2

5'-TTCGAGGACTGAAACTGGACTG-3'

5'-TCTAGGCGGTGAGATGAAACG-3'

YDR213W

OAF1

5'-CAGTGGTTTACGTCAGGGTCC-3'

5'-CGTTTGCTCTTTTGAACCACC-3'

YAL051W

INO4

5'-ATGAGGCAAAAACCGGCAG-3'

5'-TTTGTTCTTGTACGGGATCGC-3'

YOL108C

MET3

5'-GTGCCTCCCACTTCATTGTTG-3'

5'-GAGTTCTTACCTGGGCCCG-3'

YJR010W

AZR1

5'-TATTACCTGGCGTTGCTTTCG-3'

5'-TGAGAAGCGAGCATGGAACTT-3'

YGR224W

PDR3

5'-CTGCGGCATCAAGATCGAT-3'

5'-GCTAGGCGCAGAATGTTGTCT-3'

YBL005W

RTG1

5'-CAGTAAGACCGCCAGCAAAGA-3'

5'-CACCATACCCTCCGTACTCGA-3'

YOL067C

RTG3

5'-CAATTGGGAGCCACCGTTAT-3'

5'-CCAGCATGATTGTGGTTACCG-3'

YBL103C

RLM1

5'-AAATACCGGGCTGACTCCATAC-3'

5'-GTGCCTAGTGGTGTTTGAGCAG-3'

YPL089C

Sensitivity assays with yeast mutants

Cells were grown overnight in YPD broth (pH 7.0, MOPS buffered) until the optical density of 3.0 at A₆₀₀was reached. For mutants, G418 at the concentration of 200 μg/ml was added to the broth. Seven serial 1:5 dilutions of the overnight cultures were prepared in appropriate medium and plated on YPD agar containing either 110 μM of pterostilbene or 1% DMSO as control. The plates were photographed after incubating for 2 days at 30°C.

Results

IC₅₀determination in S. cerevisiae

Pterostilbene is known to possess strong antifungal activity 16. In this study, in order to investigate how pterostilbene functions in fungal cells, we determined the consequences of exposure to this compound by monitoring the whole-genome transcriptional response in the model yeast Saccharomyces cerevisiae. We made use of S. cerevisiae since it has been extensively used for elucidating the molecular targets of antifungal and therapeutic compounds (reviewed in 2627). We first determined the IC₅₀value for pterostilbene in S. cerevisiae cells using a combination of microplate-based and large-scale culture assays as previously described 18. The IC₅₀was determined to be 70 μM based on the average value from four independent experiments (Figure 1), and this concentration was used for subsequent microarray studies.

Figure 1

Determination of IC₅₀concentration for pterostilbene in yeast cells

Determination of IC₅₀concentration for pterostilbene in yeast cells. Results are shown from a microtiter assay (experiment 1) and three independent assays performed in large scale cultures (experiment 2–4) as described in "Methods".

Categories of genes responding to pterostilbene

Our results demonstrate that pterostilbene significantly (p < 0.05) affected the expression of over a thousand genes under our experimental conditions, with the up- and down-regulation of 1007 and 182 genes, respectively (see Additional file 2: List of all significant pterostilbene-responding genes). The most immediate observation is the very high fraction of up-regulated genes, indicating many different biological processes are affected by pterostilbene (Table 2). Of these, 44 genes were up-regulated more than 10-fold. The majority of the genes showed fold-changes ranging between 2.0 to 5.0, while approximately 200 genes showed fold-changes of 5.0 to10.0 (Table 2).

Additional file 2

List of all significant pterostilbene-responding genes.

Click here for file

Table 2

Distribution of differentially expressed genes based on fold-change of expression

Threshold

Number of differentially expressed genes

Number of genes up-regulated

Number of genes down-regulated

≥ 2 to <5-fold

1032

863

169

≥ 5 to <10-fold

113

102

≥ 10 to <50-fold

≥ 50-fold to <100-fold

≥ 100-fold

To explore the biological significance of the observed transcriptome response to pterostilbene, a z-score report was generated (Table 3) using the GeneSifter software 19. A positive z-score indicates that more genes than expected by chance alone fulfill the criterion in a specific ontology term, while a negative z-score indicates that the term occurs less frequently than expected 22. Pterostilbene affected the expression of genes that were associated with a diverse array of biological, molecular and cellular ontologies. As shown in Table 3, the most significantly enriched ontologies identified for the up-regulated genes included "response to drug," "regulation of transcription from PolII promoter," "mitochondrion organization and biogenesis," and "protein folding," with z-up scores of 5.05, 2.4, 2.12, and 4.24 respectively (Table 3, under Biological Process). Related processes were also significantly over-represented under Molecular Function, as represented by the following categories: "ABC transporter activity," "transcription factor activity," "ATPase activity," "peptidase activity," and "chaperone activity," with z-scores of 3.02, 4.67, 2.24, 2.12, and 3.56 respectively (Table 3, under Molecular Function). Among the down-regulated genes, terms associated with the biological process "amino acid and derivative metabolism" (specifically, the subcategory of "methionine metabolism"), and the cellular component "cell wall" were over-represented with z-scores of 5.08 and 8.22, respectively.

Table 3

Z-score reports for selected gene ontologies represented in the transcription profile of pterostilbene-treated yeast cells

Ontology

Genes

Down

Array

z-up

z-down

Biological process

Biosynthesis

150

104

871

-2.73

2.7

Response to stress

365

3.35

-1.92

Response to drug

5.05

-1.01

Amino acid and derivative metabolism

189

-1.27

5.08

Sulfur amino acid metabolism

-1.27

10.5

Aspartate family amino acid metabolism

-2.25

9.48

Methionine metabolism

-1.92

11.78

Regulation of transcription from Pol II promoter

169

2.4

-0.96

Reproduction

176

-1.77

3.01

Ion transport

104

0.71

2.68

Regulation of cell cycle

122

-0.81

2.15

Mitochondrion organization and biogenesis

2.12

-0.53

Protein folding

4.24

0.22

Molecular function

Transferase activity

604

-2.76

2.11

Transporter activity

406

0.49

2.36

Ion transporter activity

121

0.05

2.13

Organic acid transporter activity

0.1

2.52

ATP-binding cassette (ABC) transporter activity

3.02

-1.08

Oxidoreductase activity

236

0.63

4.23

ATPase activity

158

2.24

-2.56

Peptidase activity

116

2.12

-2.18

Chaperone activity

3.56

-1.86

Transcription factor activity

4.67

-0.76

Kinase regulator activity

-0.14

4.93

Cellular component

Mitochondrion

161

148

644

6.1

-2.23

Plasma membrane

215

1.13

4.31

Endoplasmic reticulum

364

-2.68

0.63

Cytosol

390

-4.8

-0.51

Cell wall

1.09

8.22

Chromosome

182

-2.81

0.64

Cytoskeleton

194

-2.68

-0.34

Z-scores were determined using GeneSifter software. Criteria for inclusion in the table were an ontology having a z-score ≥ 2 or ≤ -2 and containing ≥ 10 genes. Genes, number of genes differentially expressed within the assigned ontology; array, the number of genes on the microarray assigned to the particular gene ontology. Sub-categories within a given ontology are shown in italics. Significant z-scores and corresponding numbers of genes in each category are shown in bold.

As mentioned, pterostilbene is involved in fatty acid and lipid catabolism through activation of the peroxisome proliferator-activated receptor α (PPARα) 10. With respect to the gene ontology analysis (Table 3), the effect on lipid metabolism by pterostilbene was observed to be less statistically significant, however further examination revealed several important genes associated with lipid metabolism within the microarray data (Table 4). These included several genes involved in fatty acid breakdown including FOX2 (multifunctional beta-oxidation protein), ECI1 (peroxisomal Δ3, Δ2-enoyl-CoA isomerase), and OAF1 (oleate-activated transcription factor). In addition, many genes involved in the biosynthesis of fatty acids, phospholipids and sphingolipids were up-regulated by pterostilbene treatment, as well as 8 genes involved in the regulation of lipid metabolism. The latter include OAF1 (involved in regulation of beta-oxidation of fatty acids) 2829, UPC2 (involved in regulation of sterol biosynthesis and transport) 30, and INO4 (involved in regulation of phospholipid metabolism) 31. Furthermore, two additional genes, MGA2 and SPT23, encoding endoplasmic reticulum membrane proteins, Spt23p and Mga2p, respectively, were also up-regulated. Spt23p and Mga2p regulate expression of OLE1, a gene encoding Ole1p, an intrinsic membrane-bound -9 fatty acid desaturase required for the synthesis of oleic acid in yeast cells 32.

Table 4

Pterostilbene-responsive genes involved in lipid metabolism

Accession

Gene

Ratio

p-value

Gene Description

a. Lipid biosynthesis/metabolism process

YBR004C

GPI18

3.1

1.28 × 10^-4

Functional ortholog of human PIG-V

YGL126W

SCS3

2.4

1.06 × 10^-3

Required for inositol prototrophy

YDR173C

ARG82

2.3

2.53 × 10^-4

Inositol polyphosphate multikinase (IPMK)

YGR036C

CAX4

2.3

0.02

Dolichyl pyrophosphate (Dol-P-P) phosphatase

YIL124W

AYR1

-2.7

6.79 × 10^-4

1-acyl dihydroxyacetone phosphate reductase

YJL153C

INO1

10.0

6.73 × 10^-4

L-myo-inositol-1-phosphate synthase

YJL196C

ELO1

-2.8

1.18 × 10^-5

Elongase

YKL051W

SFK1

4.0

4.36 × 10^-4

Suppressor of PI Four Kinase

YMR165C

PAH1

2.2

1.15 × 10^-3

Mg²⁺-dependent phosphatidate (PA) phosphatase

YMR207C

HFA1

2.0

3.22 × 10^-4

Mitochondrial acetyl-coenzyme A carboxylase

YNL169C

PSD1

-2.1

1.93 × 10^-3

Phosphatidylserine decarboxylase

YNL231C

PDR16

6.1

3.54 × 10^-4

by the multiple drug resistance regulator Pdr1p

YNR043W

MVD1

-2.2

3.55 × 10^-4

Mevalonate pyrophosphate decarboxylase

YPR183W

DPM1

-2.3

2.88 × 10^-4

Dolichol phosphate mannose synthase

YKR009C

FOX2

3.1

7.82 × 10^-4

Multifunctional beta-oxidation protein

YLR284C

ECI1

2.1

8.91 × 10^-3

Peroxisomal Δ3, Δ2-enoyl-CoA isomerase

YML042W

CAT2

2.4

2.21 × 10^-4

Carnitine O-acetyltransferase

YMR246W

FAA4

2.1

1.25 × 10^-4

Long chain fatty acyl-CoA synthetase

YNR019W

ARE2

4.1

1.28 × 10^-4

Acyl-CoA:sterol acyltransferase

YOR100C

CRC1

2.4

0.01

Carnitine transporter

YKR031C

SPO14

2.1

1.36 × 10^-3

Phospholipase D

YMR008C

PLB1

3.9

3.37 × 10^-4

Phospholipase B (lypophospholipase)

b. Sphingolipid metabolism

YBR265W

TSC10

-2.4

5.46 × 10^-4

3-ketosphinganine reductase

YCR034W

FEN1

-3.0

3.49 × 10^-4

Fatty acid elongase

YDR062W

LCB2

-2.1

5.13 × 10^-4

Component of serine palmitoyltransferase

YDR072C

IPT1

6.5

2.64 × 10^-6

Inositolphosphotransferase 1

YLR260W

LCB5

3.7

8.66 × 10^-4

Minor sphingoid long-chain base kinase

YOR171C

LCB4

2.6

8 × 10^-5

Sphingoid long-chain base kinase

YPL057C

SUR1

3.5

4.67 × 10^-3

Probable catalytic subunit of a mannosylinositol phosphorylceramide (MIPC) synthase

YPL087W

YDC1

2.2

2.93 × 10^-3

Alkaline dihydroceramidase

c. Genes involved in the regulation of lipid biosynthesis and metabolism

YAL051W

OAF1

2.0

1.1 × 10^-3

Oleate-activated transcription factor

YDR096W

GIS1

4.0

2.12 × 10^-3

Transcriptional factor

YDR213W

UPC2

8.8

6.78 × 10^-5

Sterol regulatory element binding protein

YOR237W

HES1

3.1

4.32 × 10^-3

Protein implicated in the regulation of ergosterol biosynthesis;

YIR033W

MGA2

2.0

1.53 × 10^-3

ER membrane protein involved in regulation of OLE1 transcription

YKL020C

SPT23

2.1

7.73 × 10^-4

ER membrane protein involved in regulation of OLE1 transcription

YOR049C

RSB1

131.8

2.23 × 10^-4

Suppressor of sphingoid long chain base (LCB) sensitivity of an LCB-lyase mutation

YOL108C

INO4

2.1

5.1 × 10^-3

Transcription factor required for derepression of inositol-choline-regulated genes

Pterostilbene exposure also resulted in a significant up-regulation of genes associated with the ontology term "response to drug" (z-score of 5.05 – Table 3; genes listed in Table 5). Three of these genes encode transcription factors, including PDR3 involved in the regulation of the pleiotropic drug resistance (PDR) response in yeast 33, YAP1, encoding a basic leucine zipper transcription factor required for oxidative stress tolerance 34, and YRR1, encoding a Zn2-Cys6 zinc-finger transcription factor that activates genes involved in multidrug resistance 35. Notably, the significantly induced genes in this category were transporters, especially the ABC transporters whose function is known to be involved in multidrug resistance against a broad range of antifungal agents 36. It should be also noted that the AZR1 gene, which encodes a plasma membrane protein required for adaptation to acetic acid and resistance to azole drugs such as ketoconazole and fluconazole in yeast 37, was induced by more than 73-fold (Table 5).

Table 5

Pterostilbene-responsive genes associated with "response to drug" gene ontology category

Accession

Gene

Ratio

p-value

Molecular Function

a. Multidrug resistance ABC transporters

YOR153W

PDR5

6.1

6.01 × 10^-5

Multidrug resistance ABC transporter

YOR328W

PDR10

7.1

1.01 × 10^-4

ABC transporter highly similar to Pdr5p

YGR281W

YOR1

10.3

1.01 × 10^-4

Multidrug resistance ABC transporter

YDR011W

SNQ2

8.2

9.71 × 10^-6

Multidrug resistance ABC transporter

b. Multidrug resistance MFS transporters

YNL065W

AQR1

2.2

7.19 × 10^-3

MFS-multidrug resistance transporter

YGR224W

AZR1

73.4

2.16 × 10^-4

MFS plasma membrane transporter

c. Multidrug resistance transcription factors

YBL005W

PDR3

9.5

4.22 × 10^-4

Transcriptional activator of the PDR network

YML007W

YAP1

3.1

1.66 × 10^-5

Transcription factor required for oxidative stress

YOR162C

YRR1

2.5

9.44 × 10^-5

Transcription factor involved in MDR

d. Other drug-responsive genes

YOR266W

PNT1

2.0

0.01

Mitochondrial protein involved in export of proteins

YNL231C

PDR16

6.1

3.54 × 10^-4

Pdr17p homolog controlled by Pdr1p

YGR197C

SNG1

4.1

3.7 × 10^-4

Protein involved in nitrosoguanidine resistance

YOR018W

ROD1

2.8

8.67 × 10^-3

Membrane protein, resistance to o-dinitrobenzene

Including the transcription factors discussed above involved in the regulation of lipid metabolism and pleiotropic drug resistance, in total more than 30 transcription factors were up-regulated in response to pterostilbene (see Additional file 3: List of pterostilbene-responding genes in the "cell wall," "transcription factor activity," and "mitochondrion" categories). Of particular interest are genes encoding transcription factors involved in mitochondrial function (RTG1 and RTG2), cell wall-related functions (RLM1), and methionine metabolism (MET4 and MET31), considering that these functional categories were over-represented in the gene ontology analysis (Table 3). Thus, the genes present in these functional categories include transcription factors as well as their cognate targets, suggesting the modulation of these processes, at least in part, via altered levels of these specific transcription factors in response to pterostilbene exposure.

Additional file 3

List of pterostilbene-responding genes in the "cell wall," "transcription factor activity," and "mitochondrion" categories.

Click here for file

In the Cellular Component-based ontology analysis, genes required for mitochondrial and cell wall-related functions were also over-represented (Table 3). The cell wall category had a z-score of 8.22 (Table 3), and most of the genes assigned within this category were down-regulated by pterostilbene treatment (Table 3). The majority of these genes are involved in mating responses [e.g., FIG1, FIG2, SAG1, AGA1, MF(ALPHA)1, and MF(ALPHA)2, see Additional file 3: op. cit.], which is also in agreement with the over-representation of the gene ontology term "reproduction" in the Biological Process category (z-score of 3.01, Table 3). Additional down-regulated cell wall-related genes included chitin synthase 2(CHS2), cell wall mannoproteins (TIP1, CWP1) and a major exo-1,3-beta-glucanase of the cell wall (EXG1) (see Additional file 3: op. cit.). These genes are responsible for the biosynthesis of three major constituents (chitin, mannoproteins and glucan) found in yeast cell walls 38. The gene BAG7 (Rho GTPase activating protein), however, was induced by ~137-fold in pterostilbene treated cells (see Additional file 3: op. cit.). It has been reported that Bag7p plays a role in the control of cell wall synthesis and is involved in regulating key components of the cell wall stress-response pathway 39.

With respect to mitochondrial genes, it is interesting to note that more than 100 genes involved in diverse mitochondrial functions were up-regulated by pterostilbene treatment (Table 3, and Additional file 3: op. cit.). These genes play important roles in respiration, electron transport, mitochondrial protein targeting, and mitochondrial protein synthesis (see Additional file 3: op. cit.).

Of particular interest was the finding that a group of genes involved in sulfur metabolism was significantly over-represented, generating a z-score of 10.5 for the category "sulfur amino acid metabolism," (the highest score obtained in the present study). Within this category, a subset of genes assigned to the ontology term "methionine metabolism" (z-score of 11.78) were down-regulated upon exposure to pterostilbene (Table 3). Of the 21 genes assigned to this ontology term on the array, eleven were differentially regulated. As shown in Figure 2 and Table 6, all responsive genes involved in sulfur-containing amino acid biosynthesis were down-regulated, with the exception of MET4 (transcriptional activator) and MET31 (transcriptional regulator), which were up-regulated (Table 6).

Figure 2

Summary of gene expression responses to pterostilbene in the sulfur metabolism pathway

Summary of gene expression responses to pterostilbene in the sulfur metabolism pathway. Methionine biosynthetic pathway and corresponding genes are based on Thorsen et al. [66] and the Saccharomyces Genome Database [25]. Genes shown in boldface in the pathway are those affected by pterostilbene. The up- and down-regulated genes were indicated by arrows, (↑) and (↓).

Table 6

Pterostilbene-responsive genes involved in sulfur metabolism and/or assimilation

Accession

Gene

Ratio

p-value

Gene Description

a. Methionine metabolism

YJR137C

ECM17

-2.5

2.48 × 10^-5

Sulfite reductase beta subunit

YKR069W

MET1

-3.1

3.88 × 10^-4

AdoMet-dependent uroporphyrinogen III transmethylase

YJR010W

MET3

-3.8

3.5 × 10^-4

ATP sulfurylase

YER091C

MET6

-2.4

8.83 × 10^-5

Cobalamin-independent methionine synthase

YFR030W

MET10

-2.1

1.5 × 10^-3

Sulfite reductase alpha subunit

YGL125W

MET13

-3.6

1.46 × 10^-4

Methylenetetrahydrofolate reductase

YKL001C

MET14

-3.1

1.12 × 10^-3

Adenylylsulfate kinase

YPR167C

MET16

-2.0

7.56 × 10^-4

3'-phosphoadenylylsulfate reductase

YOL064C

MET22

-2.5

1.68 × 10^-4

Bisphosphate-3'-nucleotidase,

YLL062C

MHT1

-3.4

6.69 × 10^-4

S-methylmethionine-homocysteine methyltransferase

YLR180W

SAM1

-2.1

6.39 × 10^-5

S-adenosylmethionine synthetase

b. Sulfur and aspartate family amino acid metabolic processes (in addition to a)

YNL103W

MET4

3.0

4.01 × 10^-4

Leucine zipper transcriptional activator

YPL038W

MET31

2.2

2.45 × 10^-3

Transcriptional regulator highly homologous to Met32p

YJR130C

STR2

2.3

2.97 × 10^-3

Cystathionine gamma-synthase

YGL184C

STR3

-3.6

4.1 × 10^-3

Cystathionine beta-lyase

YJR060W

CBF1

-3.0

1.11 × 10^-3

Basic helix-loop-helix protein

YHR025W

THR1

-2.2

2.05 × 10^-4

Homoserine kinase

c. All other genes affected under the term "sulfur metabolism"