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Open Access Highly Accessed Research article

Transcriptional profiling of Saccharomyces cerevisiae exposed to propolis

Patrícia Alves de Castro1, Marcela Savoldi1, Diego Bonatto2, Iran Malavazi3, Maria Helena S Goldman4, Andresa A Berretta15 and Gustavo Henrique Goldman16*

Author Affiliations

1 Faculdade de Ciências Farmacêuticas de Ribeirão, Preto Universidade de São Paulo, São Paulo, Brazil

2 Centro de Biotecnologia da UFRGS, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

3 Departamento de Genética e Evolução, Centro de Ciências Biológicas e da Saúde (CCBS), Universidade Federal de São Carlos, Porto Alegre, Brazil

4 Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil

5 Apis Flora Industrial e Comercial, Ribeirão Preto, São Paulo, Brazil

6 Laboratório Nacional de Ciência e Tecnologia do Bioetanol – CTBE, Caixa Postal 6170, Campinas, São Paulo, 13083-970, Brazil

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BMC Complementary and Alternative Medicine 2012, 12:194  doi:10.1186/1472-6882-12-194

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1472-6882/12/194


Received:31 July 2012
Accepted:17 October 2012
Published:24 October 2012

© 2012 de Castro 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

Propolis is a natural product of plant resins collected by honeybees (Apis mellifera) from various plant sources. Our previous studies indicated that propolis sensitivity is dependent on the mitochondrial function and that vacuolar acidification and autophagy are important for yeast cell death caused by propolis. Here, we extended our understanding of propolis-mediated cell death in the yeast Saccharomyces cerevisiae by applying systems biology tools to analyze the transcriptional profiling of cells exposed to propolis.

Methods

We have used transcriptional profiling of S. cerevisiae exposed to propolis. We validated our findings by using real-time PCR of selected genes. Systems biology tools (physical protein-protein interaction [PPPI] network) were applied to analyse the propolis-induced transcriptional bevavior, aiming to identify which pathways are modulated by propolis in S. cerevisiae and potentially influencing cell death.

Results

We were able to observe 1,339 genes modulated in at least one time point when compared to the reference time (propolis untreated samples) (t-test, p-value 0.01). Enrichment analysis performed by Gene Ontology (GO) Term finder tool showed enrichment for several biological categories among the genes up-regulated in the microarray hybridization such as transport and transmembrane transport and response to stress. Real-time RT-PCR analysis of selected genes showed by our microarray hybridization approach was capable of providing information about S. cerevisiae gene expression modulation with a considerably high level of confidence. Finally, a physical protein-protein (PPPI) network design and global topological analysis stressed the importance of these pathways in response of S. cerevisiae to propolis and were correlated with the transcriptional data obtained thorough the microarray analysis.

Conclusions

In summary, our data indicate that propolis is largely affecting several pathways in the eukaryotic cell. However, the most prominent pathways are related to oxidative stress, mitochondrial electron transport chain, vacuolar acidification, regulation of macroautophagy associated with protein target to vacuole, cellular response to starvation, and negative regulation of transcription from RNA polymerase II promoter. Our work emphasizes again the importance of S. cerevisiae as a model system to understand at molecular level the mechanism whereby propolis causes cell death in this organism at the concentration herein tested. Our study is the first one that investigates systematically by using functional genomics how propolis influences and modulates the mRNA abundance of an organism and may stimulate further work on the propolis-mediated cell death mechanisms in fungi.

Background

Propolis is a natural product of plant resins collected by honeybees (Apis mellifera) from various plant sources. It is used by the bees to seal holes in their honeycombs and protect the hive entrance [1-3]. Propolis has been used in folk medicine for centuries. Its chemical composition is quite complex since more than 300 compounds, such as polyphenols, phenolic aldehydes, sequiterpene quinines, coumarins, amino acids, steroids, and inorganic compounds, have been identified in propolis samples. Propolis has cytotoxic [4], anti-herpes virus [5], antitumor [6], radical scavenging [7], antimicrobial [8,9], antiprotozoan [10], and anti-HIV [11] activity and suppressive effects of dioxin toxicity [9]. More recently, evidence has shown that propolis can be used to treat Candida fungal infections [12-16].

Recently, we applied the power of Saccharomyces cerevisiae as a model organism for studies of genetics, cell biology, and genomics to determine how propolis affects eukaryotic cells at the cellular level [17]. Propolis is able to induce an apoptosis cell death response; however, increased exposure to propolis provides a corresponding increase in the necrosis response. We showed that cytochrome c but not endonuclease G (Nuc1p) is involved in propolis-mediated cell death in S. cerevisiae. We also observed that the metacaspase YCA1 gene is important for propolis-mediated cell death. We screened the full collection of about 4,800 haploid S. cerevisiae non-essential deletion mutants for propolis sensitivity, and we were able to identify 138 deletion strains that have different degrees of propolis sensitivity compared to the corresponding wild-type strains. Our studies indicated that propolis sensitivity is dependent on the mitochondrial function and that vacuolar acidification and autophagy are important for yeast cell death caused by propolis. Here, we extended our understanding of propolis-mediated cell death in the yeast Saccharomyces cerevisiae by applying systems biology tools to analyze the transcriptional profiling of cells exposed to propolis.

Methods

Propolis tandardized extract

Propolis Standardized Extract - (EPP-AF®) (Batch 010/08) were industrially produced and kindly provided by Apis Flora Company (RibeirãoPreto/SP – Brazil). The extract was standardized using a propolis blend composed by raw material obtained from several sites of Brazil (Patent number PI 0405483–0, published in Revista de Propriedade Industrial n. 1778 from 01/02/2005). Propolis (blend of raw material) was kept in a freezer for 12 h, ground to a fine powder in a blender. It was then extracted using hydroalcoolic solution (7:3), with dinamic maceration, during 72 hours in ambient conditions (25° ± 5°C), followed by percolation process (1L/min.) and finally by a filtration process using in the first step the propolis biomass like a filter and secondly a 220 mesh stainless steel industrial line filter. Propolis extract obtained presents 11% w/v of dry matter and chemical composition standardized qualitatively and quantitatively by RP-HPLC (C18 Shim-pack, CLC-ODS (M), 25 cm x 4,6 column -with gradient elution with methanol and acidic water pH=2,7, plotted at 275 nm) into compounds: caffeic, p-coumaric and cinnamic acids, aromadendrin, isosakuranetin and artepillin C.

Yeast strain, media and culture methods

The assays were carried out with S. cerevisiae yeast strain BY4742 (MATα; his3Δ1; leu2Δ0; lys2Δ0; ura3Δ0) [18]. The culture medium used was complete medium YPD (2% w/v glucose, 1% w/v yeast extract, 2% w/v peptone). For the microarray assay, the yeast cells were grown for 9 hours (mid-exponential phase) in 50mL of liquid YPD at 30°C with mechanical shaking (200rpm). After this, the cells (~ 2 x 107 cells ml-1) were exposed to propolis 0.125% for 5 or 10 minutes. The control for the experiment using propolis 0.125% as a treatment has 0.68% ethanol. Two independent experiments were performed to each array using two different biological samples and dye swap analysis. Cell viability was determined by plating appropriate cell concentrations and counting the number of colonies in comparison to propolis-untreated controls.

RNA isolation and real-time PCR

For total RNA isolation, the yeast cells were disrupted by vortexing with glass beads and total RNA was extracted with Trizol reagent (Invitrogen, USA). Ten micrograms of RNA from each treatment were then fractionated in 2.2 M formaldehyde, 1.2% w/v agarose gel, stained with ethidium bromide, and then visualized with UV-light. The presence of intact 25S and 18S ribosomal RNA bands was used as a criterion to assess the integrity of the RNA. RNAse free DNAse treatment was carried out as previously described [19]. After this, the total RNA was purification by RNeasy® Mini Kit (Qiagen) and the purified samples were measured in the NanoDrop® 2000 (Thermo Scientific).

Microarray hybridization

For gene expression analysis commercially-available Agilent whole genome S. cerevisiae microarray [Yeast (V2) Gene Expression Microarray, 8x15K] was used. The microarray slides contain 15,208 probes for S.cerevisiae (BY4742 strain). The RNA samples obtained under the conditions above described were subjected to cRNA fluorescent labeling. cRNA labeling was performed according to the standard protocol described by Agilient using Two-Color Microarray-Based Gene Expression Analysis (Agilent Technologies, USA). Briefly, for cRNA synthesis and labeling 5 μg of total RNA were used. After labeling, 300 ng of Cy3 and Cy5-labelled cRNAs (specific activity > 8.0 pmol Cy3-Cy5/μg cRNA) was fragmented at 60°C for 30 minutes in a reaction volume of 25 μl containing 1x Agilent fragmentation buffer and 2x Agilent blocking agent following the manufacturer’s instructions. On completion of the fragmentation reaction, 25 μl of 2x Agilent hybridization buffer was added to the fragmentation mixture and hybridized to the S. cerevisiae microarrays slides for 17 hours at 65°C in an Agilent G2545A Hybridization Oven and on Agilent Rotator Rack. After hybridization, microarrays were sequentially washed: 1 minute at room temperature with GE Wash Buffer 1 (Agilent) and 1 minute with 37°C GE Wash buffer 2 (Agilent), then a 10 seconds Acetonitrile Wash (Agilent) followed by a 30 seconds Stabilization and Drying Solution wash (Agilent). Slides were immediately subjected to fluorescent detection using fluorescent detection with a GenePix 4000B (Molecular Devices, USA) with simultaneously scanning the Cy3 and Cy5 channels at a resolution of 5 μm. Laser was set at 100% and PMT gain was adjusted automatically for each slide using the program GenePix Pro (Molecular Device) according to the signal intensity of each array. Merged Cy3 and Cy5 TIFF images generated by the GenePix Pro were used to analysis in the Agilent Feature Extraction software (version 9.5.3.1, Agilent) using Linear Lowess algorithm to obtain background subtracted and normalized intensity values. The dye-normalyzed values generated in the Feature Extraction data files were used to upload the software Express Converter (version 2.1, TM4 available at http://www.tm4.org/utilities.html webcite) which conveniently converts the Agilent file format to mev (multi experiment view) file format compatible to the TM4 softwares for microarray analysis (available at http://www.tm4.org/ webcite). The mev files were then uploaded in the MIDAS software where the resulting data were averaged from replicated genes on each array, from dye-swap hybridizations for each experiment and from two biological replicates using the tools “flip dye consistency cheking” and “in slides replicates analysis” implemented in MIDAS software. The mev files generated were then loaded in MEV software (MultiExperiment Viewer) where differentially expressed genes were identified using one-class t-test (p>0.01). Significantly different genes were those whose mean log2 expression ratio over all included samples was statistically different from 0 which indicates the absence of gene modulation. The genes significantly up- or down-regulated in the microarray analysis was subjected to Gene Ontology analysis using the GO Term Finder tool available at the Saccharomyces Genome Database (SGD < http://www.yeastgenome.org webcite>).

Physical protein-protein (PPPI) network design and global topological analysis

The transcriptomic data gathered from yeast BY4742 strain submitted to propolis treatment was used to obtain information about how the under- and overexpressed genes and their products interact in the context of physical protein-protein interactions (PPPI networks) in S. cerevisiae. In this sense, the data mining screening and network design of repressed or induced genes-associated PPPI networks was performed using Cytoscape software, version 2.6.3 [20]. For this purpose, we used the PPPI data of S. cerevisiae available in the Saccharomyces Genome Database ( http://www.yeastgenome.org webcite). The induced and repressed PPPI networks obtained from this first screening were then combined in a unique PPPI network by employing the union function of the Cytoscape core plugin Merge Networks. The union PPPI network was then analyzed with molecular complex detection (MCODE) software [21], a Cytoscape plug-in (at http://apps.cytoscape.org/apps/mcode webcite) in order to detect clusters of proteins that could represent distinct biologic processes. The parameters used for MCODE to generate the sub networks were as follows: loops included; degree cutoff of 2; deletion of single connected nodes from cluster (haircut option enabled); expansion of cluster by one neighbor shell allowed (fluff option enable); node density cutoff of 0.1; node score cutoff of 0.2; k-core of 2; and maximum depth of network of 100. The degree of data overlapping between induced- and repressed-associated PPPI networks was obtained from an area-proportional Venn diagram analysis, available at < http://bioinforx.com/free/bxarrays/overlap.php webcite>.

Network centralities and local topological analyses

Two major network centralities (node degree and betweenness) were computed from the merged network and clusters using the Cytoscape plugin CentiScaPe 1.0 [22]. The local topology of the network, defined as bottlenecks, was obtained from the threshold generated by each centrality calculated by CentiScape 1.0. In this sense, bottlenecks were defined as nodes with a value above the threshold calculated for node degree and betweenness.

Gene ontology analysis

Gene ontology (GO) clustering analysis was performed using Biological Network Gene Ontology (BiNGO) [23] software, a Cytoscape plugin available at http://chianti.ucsd.edu/cyto_web/plugins/index.php webcite. The degree of functional enrichment for a given cluster and category was quantitatively assessed (p value) by hypergeometric distribution [24] and a multiple test correction was applied using the false discovery rate (FDR) [25] algorithm, fully implemented in BiNGO software. Overrepresented biological process categories were generated after FDR correction, with a significance level of 0.05.

Results and discussion

Microarray hybridization analysis

To our knowledge, previous to our work there is only a single study in the literature reporting transcriptional profiling for eukaryotic cells exposed to propolis [26]. In this study, propolis was applied for 24 hours to normal human dermal fibroblast and keratinocytes. These authors were able to identify 205 genes important for skin and only 5 (ATP citrate synthase, aquaporin 3, cytochrome c oxidase 1, nitric oxide synthase 3, and hydroxylase 3) and 1 (progestone receptor) that appear to be up- and down regulated in both cell lines, respectively. We have been using S. cerevisiae as an eukaryotic model system to identify genes that are important for propolis-mediated cell death. As previously shown, when S. cerevisiae exponential cells are exposed to propolis 0.125% for 5 and 10 minutes, there is a decreased survival of 24.1 and 6.3%, respectively in comparison to the propolis-untreated control containing only 0,68% ethanol [17]. To gain an insight on which pathways are modulated during S. cerevisiae exposure to propolis, we determined its transcriptional profiling. Total RNA extracted from these cultures was used to generate fluorescent-labeled cRNAs for competitive microarray hybridizations. All the controls for further experiments using propolis 0.125% as a treatment have 0.68% ethanol (reference samples). We have compared the mRNA expression of the S. cerevisiae BY4742 strain grown for 9 hours and exposed to 0.125% propolis for 5 and 10 minutes with yeast cells exposed to 5 and 10 minutes 0.68% ethanol. In these experiments, the main aim was to focus on genes that have increased or decreased mRNA expression. The full dataset was deposited in the Gene Expression Omnibus (GEO) from the National Center of Biotechnology Information (NCBI) with the number GSE33971 ( http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE33971 webcite). We were able to observe 1,399 genes modulated in at least one time point (p-value 0.01, calculated FDR is 2.1%) when compared to the respective reference time (reference untreated samples obtained under the control experimental conditions, i. e., 2 x 107 cells ml-1 exposed only to 0.68% ethanol for 5 or 10 minutes). We have used Gene Ontology (GO) Term Finder analysis aiming to classify the main biological processes associated to the list of the up- and down-regulated genes identified in the microarray hybridizations. In addition, we assessed the probability values of the over-abundance of the GO groups compared to the genomic average in order to gain information about the statistical significance of overrepresented processes (p<0.05). Table  1 shows the adjusted p-values indicating the categories of genes overrepresented in the microarray analysis which are involved in a variety of cellular processes. To have a broader view of the most significant modulated genes found in the microarray analysis, we listed the genes having increased or decreased mRNA expression with log ratios ≥ 1 (203 genes) or ≤ 1 (136 genes). These genes were grouped according to the GO identity obtained in Table  1 [Additional file 1: Table A1 shows the genes with log ratios ≥ 1 (203 genes) or ≤ 1 (136 genes), respectively]. The Table  2 shows a list of chosen genes presenting higher level of up-regulation in the microarray which were grouped into categories of significantly overrepresented biological process according to the gene ontology ID shown in Table  1. Interestingly, inside the enriched category “transmembrane transport and localization” (GO:0055085 and GO:0051179; p<0.01), we have observed several genes encoding transporters reported as involved in multidrug resistance (MDR; for reviews see [27-30], suggesting propolis can activate at the transcriptional level the complex set of genes responsible for MDR in S. cerevisiae (Table  2 and Additional file 1: Table A1). In addition, we have also observed enrichment for genes encoding proteins important for the assembly of the vacuolar ATPase and the endocytic pathway (Table  2 and Additional file 1: Table A1). None of the deletion mutants for these transporter encoding genes shown in Additional file 1: Table A1 were observed as more sensitive to propolis, except for TPO1[17], indicating a redundant transcriptional response of these genes to propolis. However, we have previously observed that when several genes involved in the assembly of the yeast V-ATPase such as VPH1, VMA3, 4, 5, 11, 22, RAV1 and SOP4 were deleted, the corresponding yeast deletion strains became more sensitive to propolis [17].

Additional file 1. Table A1. Genes with increased or repressed mRNA expression and grouped according to the GO identity.

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Table 1. Overrepresented categories of the significantly modulated (up- and down-regulated) genes found in the microarray hybridization based on the genome coverage (p<0.05)

Table 2. Selected genes more expressed (log2 ≥ 1.0) during S. cerevisiae exposure to propolis according to the GO term finder enrichment analysis (for a complete list of the genes more expressed, see Additional file 1: Table A1)

Earlier, by screening a non essential yeast deletion library, we observed that most of the proteins whose deletion increases the sensitivity of yeast strains to propolis are involved in cell division mechanisms, mitochondrial electron transport chain, vacuolar acidification, regulation of macroautophagy associated with protein target to vacuole, cellular response to starvation, and negative regulation of transcription from RNA polymerase II promoter [17]. We have shown that propolis induces vacuolar acidification and translocation of Atg8p to the vacuoles, one of the hallmarks of autophagy [17]. In S. cerevisiae, the vacuole is very important for keeping cellular homeostasis comprising the regulation of intracellular pH and degradation mainly during nutrient limitation of proteins and organelles by autophagy (for reviews, see [31-33]). Cell death induced by acetic acid is increased in S. cerevisiae VPS gene deletion mutants (VPS genes are involved in homotypic vacuole fusion, vacuolar protein sorting and are essential for normal vacuolar function) [34]. It has been observed that the intracellular pH was acidified in VPS mutant cells upon treatment with acetic acid [34]. It is possible the disturbance of the homeostatic pH control may trigger necrosis by release of pro-necrotic proteases, which would find an optimal pH for their enzymatic activity in the acidified cytosol [35]. We have observed genes encoding proteins important for ROS detoxification in S. cerevisiae significantly enriched in the microarray hybridization (GO: GO:0006950; Response to Stress and (GO:0050896) Response to Stimulus; Table  2 and Additional file 1: A1; p<0.003) such as GRX4, GTT2, GTT3, TSA2, DFM1, and TRX1 with increased mRNA accumulation when S. cerevisiae is exposed to 0.125% propolis. Interestingly, there is also an increased mRNA accumulation of genes encoding proteins involved in the generation of ATP into the mitochondria, such as ATP17, ATP18, ATP19, ATP20, ATP21, and COX8 grouped in the category of cellular component organization or biogenesis at cellular level (GO:0071841; Table  2 and Additional file 1: Table A1; p < 2.66 x10-6). There are several conditions where mitochondria-produced ROS have been associated to yeast apoptosis (for reviews, see [35-38]). Propolis at 0.125% can induce ROS formation and it is more lethal when S. cerevisiae grows in the presence of glycerol and ethanol as carbon sources [17], suggesting that respiration increases propolis lethality. Interestingly, it was observed as up-regulated into the same enriched category the gene OYE3 (old yellow enzyme) which was described as involved in increased resistance to H2O2-induced programmed cell death in yeast [39].

Moreover, there are several genes related to cell cycle and cell cycle process and chromosome distribution and chromatin silencing that have decreased mRNA accumulation when S. cerevisiae is exposed to propolis (Table  3 and Additional file 1: Table A1). The reduced mRNA abundance of these genes suggested propolis is activating transcriptional checkpoint controls involved in the S- and M-phases important for DNA replication and proper chromosome segregation.

Table 3. Selected genes less expressed (log2 ≤ −1.0) during S. cerevisiae exposure to propolis according to the GO term finder enrichment analysis (for a complete list of the genes less expressed, see Additional file1: Table A1)

Validation of the microarray hybridization analysis

To validate some of our findings, we have chosen six different genes from our microarray analysis whose mRNA has either increased or decreased abundance when S. cerevisiae is exposed to 0.125% propolis. We designed Lux fluorescent probes and used real-time RT-PCR analysis to quantify their expression in a new set of biological replicate of the mRNA isolated from 5 and 10 minutes exposure to 0.125% propolis and compared them with the corresponding 5 and 10 minutes exposure to 0.68% ethanol. We have used as a normalizer control, TAF10, a gene encoding a subunit (145 kDa) of TFIID and SAGA complexes, involved in RNA polymerase II transcription initiation and in chromatin modification. Recently, this gene was shown as an appropriate reference gene for quantitative gene expression analysis by real-time RT-PCR [40]. In addition, TAF10 was not shown as modulated in our microarray hybridization experiments (data not shown). Thus, the measured quantity of a specific gene mRNA in each of the treated samples was normalized using the CT values obtained for the TAF10 mRNA amplifications run in the same plate. The results were expressed as the number of times the genes have increased or decreased abundance when the yeast strains were exposed to propolis compared to the ethanol treatment (Figure  1).

thumbnailFigure 1. Real-time RT-PCR for selected genes from the microarray hybridization analysis. S. cerevisiae was grown for 9 hours in liquid YPD at 30°C and the cells (~ 2 x 107 cells ml-1) were transferred to fresh liquid YPD and exposed to propolis 0.125% for 5 or 10 minutes. The relative quantitation of RLF2 (A), PDR15 (B), TIM10 (C), SNQ2 (D), VMA7 (E), VMA21 (F) was performed using TAF10 as normalizer. Gene expression was determined by a standard curve (i.e., CT –values plotted against logarithm of the DNA copy number). The results are the means ± standard deviation of four sets of experiments using completely independent biological replicates. The values above the bars are mean of the log2-ratio obtained in the microarray hybridization experiments.

Due to the apparent importance of the enriched genes in the categories of (i) cellular component organization or biogenesis; (ii) transmembrane transport; (iii) response to stress/stimulus and (iv) cellular component organization or biogenesis, we evaluated the mRNA abundance of (i) PDR15 (YDR406W) encoding a transporter involved in multidrug resistance; (ii) SNQ2 (YDR011W) encoding a plasma membrane ATP-binding cassette (ABC) transporter, multidrug transporter involved in multidrug resistance and resistance to singlet oxygen species; (iii) TIM10 (YHR005C-A) encoding and essential protein of the mitochondrial intermembrane space, that forms a complex with Tim9p (TIM10 complex) and delivers hydrophobic proteins to the TIM22 complex for insertion into the inner membrane; (iv) VMA7 (YGR020C) and (v) VMA21 (YGR105W). These genes respectively encodes the subunit F of the eight-subunit V1 peripheral membrane domain of vacuolar H+-ATPase (V-ATPase) and Integral membrane protein that is required for vacuolar H+−ATPase (V-ATPase) function. The gene RLF2 (YPR018W) was also analyzed but different from the previous chosen genes, RLF2 was observed as down-regulated in the microarray analysis (Additional file 1: Table A1; Chromosome organization (GO:0051276; p<1.3 x10-6). RLF2 encodes the largest subunit (p90) of the Chromatin Assembly Complex (CAF-1) with Cac2p and Msi1p that assembles newly synthesized histones onto recently replicated DNA.

As expected, all five genes that showed increased mRNA abundance (PDR15, TIM10, SNQ2, VMA7, and VMA21) and decreased mRNA abundance (RLF2) in the microarray hybridization analysis grouped in its respective enrichment categories showed corresponding increase and decreased expression in the real-time PCR experiments (Figure  1A-F). This behavior is in accordance with the normalized mean values obtained in the microarray analysis (see values above the Figure  1 graphs for comparison). Thus, it seems that our microarray hybridization approach is capable of providing information about S. cerevisiae gene expression modulation with a considerably high level of confidence and is an open source of data for further investigation for the mechanisms of propolis-mediated cell death in all susceptive organisms.

Systems analysis for propolis exposure

The transcriptomics data obtained submitted to the treatment conditions described in this work prompt us to ask how the underexpressed or overexpressed genes affect different biological processes that are altered during the exposure to 0.125% propolis. In this sense, a search for potential proteins and/or mechanisms and their associated biological processes that are affected by the conditions treatment was initiated. To achieve this goal, two different PPPI networks using yeast transcriptomics data were retrieved from Saccharomyces Genome Database (SGD): one associated to repressed genes (136 genes; repressed genes-associated PPPI network) and one associated to induced genes (203 genes; induced genes-associated network). The induced genes-associated PPPI network obtained from SGD contains 1,226 nodes and 2,854 connectors while the repressed-associated gene PPPI network contains 1,412 nodes and 2,782 connectors (Additional file 2: Table A2). Both induced- and repressed-genes associated PPPI networks were analyzed in order to observe the degree of network overlapping by means of an Area proportional Venn diagram. This analysis indicated that the repressed-genes associated PPPI network contains 919 unique proteins, while the induced-genes associated network contains 733 unique proteins, and there are 493 overlapping proteins. Although the degree of network overlapping was not elevated, we decided to merge both networks in a unique graph, containing 2,158 nodes and 5,655 connectors (Figure  2 and Additional file 2: Table A2). Sub networks (clusters) present in the union PPPI network were identified and retrieved using the Cytoscape-associated plugin MCODE and subjected to a Gene Ontology (GO) analysis in order to obtain information about the nature and number of sub graphs belonging to the network and their associated biological processes. The union PPPI network contains eleven interconnected clusters, each comprising different biological processes. GO analysis of the obtained clusters indicated the participation of important biological processes that can be seen in Table  4 and Additional file 3: Table A3. Many of these processes are reflecting the results previously found in the yeast library screening such as cell division mechanisms, mitochondria, vacuolar acidification, negative regulation of transcription from RNA polymerase II promoter (17). In addition, other important processes such as protein transport and membrane organization and biogenesis (Table  4, cluster 3); transcription from RNA polymerase II promoter and transcription, DNA dependent (Table  4, cluster 4), regulation of cell cycle (Table  4, cluster 7); ATP metabolic process, proton transport and vacuolar acidification (Table  4, cluster 9) and response to stress (Table  4, cluster 10) were categories also observed in the microarray hybridization analysis (see Additional file 3: Table A3 for a complete description of genes). It is also important to mention that statistically significant modulated genes could be observed in each of these clusters indicating that the PPPI network could link the transcriptomic analysis to potential biological processes affected by propolis. These genes can be seen in Additional file 3: Table A3 [written in red (repressed) or green (induced)].

Additional file 2. Table A2. Induced- and repressed- genes associated PPPI networks.

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Additional file 3. Table A2. Sub networks present in the union PPPI network and their GO analysis.

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thumbnailFigure 2. Design of the union network generated from repressed- and induced-associated gene PPPI networks. Green nodes indicate proteins of genes that were induced in the array, while red nodes are associated to proteins of genes that were repressed in the array.

Table 4. Main specific gene ontology categories observed in clusters derived from union PPPI network

Based on data gathered from this initial systems biology tools analysis, we decided to get more information about the major nodes involved in the information of the network using network centralities. Network centralities allow us to identify nodes (and the consequent biological processes) that have a relevant position in the overall network architecture [41]. Centralities have been recently applied to quantify the centrality and prestige of actors in social networks [41] and to understand the structure and properties of complex biological, technological and infrastructural networks [42,43]. Many of the nodes in a given network that show elevated values of centrality are important points of vulnerability, indicating that any attack to these nodes could introduce strong perturbations in the network. Node degree represents the simplest centrality measure in a given network, corresponding to the number of nodes adjacent to a given node, where adjacent means directly connected [22]. The node degree represents the “popularity” of a given node, and highly connected nodes in a network are termed hubs. Next, betweenness is a measure that indicates to what extent a specific node is between all other nodes within the network [44]. In a general sense, betweenness show the influence of a node over the spread of information throughout the network. On the other hand, bottleneck is a local topologic data that is defined as all nodes with high betweenness values and different nodes degrees, indicating that those nodes are central points that control the communication between other nodes within the network [45,46]. The measures of betweenness and node degree allow us to define the bottleneck nodes. Bottleneck nodes correspond to highly central proteins that connect several complexes or are peripheral members of central complexes, being important communication points between two complexes [46]. Mostly of bottleneck nodes tend to be essential proteins in a network [46].

The centrality analysis of union network indicated the presence of 419 bottleneck nodes; 99 of these bottleneck nodes correspond to proteins of genes that were induced in array and 92 that were repressed in array (see Additional file 4: Table A4, spreadsheet 1). The centrality analysis was also made for the 11 clusters (Additional file 4: Table A4, spreadsheet 2). This analysis showed the presence of important bottleneck nodes in the clusters. Several genes that encode many of these proteins characterized as Bottleneck nodes (i. e. essential proteins in a network) were previously described in Tables  2, 3 and Additional file 1: Table A1 once again highlighting the significance of the biological process identified as involved in response and cell tolerance to propolis. Thus, the main bottleneck nodes can be observed in Table  5 and are related to the following biological processes: cellular component organization or biogenesis, transmembrane transport, response to stress (referring to genes induced in the microarray analysis) and chromosome organization, cell cycle, RNA metabolic process (referring to genes repressed in the microarray analysis). To verify the biological processes associated with these genes, see Additional file 1: Table A1.

Additional file 4. Table A4. Centrality analysis.

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Table 5. Selected bottleneck nodes observed in the union PPPI network

These data express the importance of these pathways in response of S. cerevisiae to propolis and confirm a high degree of overlapping in gene function among the microarray hybridization and the system biology analysis. Furthermore, the data obtained here confirms the results achieved through the identification of genes involved in propolis sensitivity by the screening of the S. cerevisiae non-essential deletion library previously reported [17].

Conclusions

Propolis is a complex product derived from plant resins and bee’s saliva. There are several chemical compounds present in this natural product that could potentially be responsible for its antibiotic properties. However, taking into consideration the fact that the cell death effects of propolis could be due to a great combination of chemical compounds and concentrations, we decided to investigate the cell death effects of propolis by concentrating our experiments on alcoholic extracts of propolis In summary, our data indicate that propolis is largely affecting several pathways in the eukaryotic cell. However, the most prominent pathways are related to oxidative stress, mitochondrial electron transport chain, vacuolar acidification, regulation of macroautophagy associated with protein target to vacuole, cellular response to starvation, and negative regulation of transcription from RNA polymerase II promoter. Our work emphasizes again the importance of S. cerevisiae as a model system to understand at molecular level the mechanism whereby propolis causes cell death in this organism at the concentration herein tested. Our study is the first one that investigates systematically by using functional genomics how propolis influences and modulates the mRNA abundance of an organism and may stimulate further work on the propolis-mediated cell death mechanisms in fungi.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

PAC and MS performed most of the experiments. PAC, DB, AB, IM, MHSG and GHG performed data analysis. GHG wrote the manuscript; conceived, designed and coordinated this study; and is the principal investigator of this work. All authors read and approved final manuscript.

Acknowledgements

This research was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and Financiadora de Estudos e Projetos (FINEP), Brazil.

References

  1. Ghisalberti EL: Propolis: a review.

    Bee World 1979, 60:59-84. OpenURL

  2. Greenaway W, Scaysbrook T, Whatley FR: The composition and plant origins of propolis.

    Bee World 1990, 71:107-118. OpenURL

  3. Viuda-Martos M, Ruiz-Navajas Y, Fernandez-Lopez J, Perez A, Alvarez AJ: Functional properties of honey, propolis, and royal jelly.

    J Food Sci 2008, 73:R117-R124. PubMed Abstract | Publisher Full Text OpenURL

  4. Matsuno T, Matsumoto Y, Saito N, Morikawa J: Isolation and characterization of cytotoxic diterpenoid isomers from propolis.

    Z Naturforsch 1997, C 52:702-704. OpenURL

  5. Vynograd N, Vynograd I, Sosnowski Z: A comparative multicenterstudy of the efficacy of propolis, acyclovir and placebo in the treatment of genital herpes (HSV).

    Phytomedicine 2000, 7:1-6. PubMed Abstract | Publisher Full Text OpenURL

  6. Kimoto T, Arai S, Kohguchi M, Nomura Y, Micallef MJ, Kurimoto M, Mito K: Apoptosis and suppression of tumor growth by artepillin C extracted from Brazilian propolis.

    Cancer Detect Prev 1998, 22:506-515. PubMed Abstract | Publisher Full Text OpenURL

  7. Cigut T, Polak T, Gasperlin L, Raspor P, Jamnik P: Antopxidative activity of propolis extract in yeast cells.

    J Agric Food Chem 2011, 9:11449-11455. OpenURL

  8. Koru O, Toksoy F, Acikel CH, Tunca YM, Baysallar M, Uskudar Guclu A, Akca E, Ozkok Tuylu A, Sorkun K, Tanyuksel M, Salih B: Oral and dental bacteriology and infection. In vitro antimicrobial activity of propolis samples from different geographical origins against certain oral pathogens.

    Anaerobe 2007, 13:140-145. PubMed Abstract | Publisher Full Text OpenURL

  9. Park YK, Fukuda I, Ashida H, Nishiumi S, Yoshida K, Daugsch A, Sato HH, Pastore GM: Suppressive effects of ethanolic extracts from propolis and its main botanical origin on dioxin toxicity.

    J Agric Food Chem 2005, 53:10306-10309. PubMed Abstract | Publisher Full Text OpenURL

  10. Menna-Barreto RF, Salomão K, Dantas AP, Santa-Rita RM, Soares MJ, Barbosa HS, de Castro SL: Different cell death pathways induced by drugs in Trypanosoma cruzi: an ultrastructural study.

    Micron 2009, 40:157-168. PubMed Abstract | Publisher Full Text OpenURL

  11. Ito J, Chang FR, Wang HK, Park YK, Ikegaki M, Kilgore N, Lee KH: Anti-AIDS agents. 48.(1) Anti-HIV activity of moronic acid derivatives and the new melliforone-related triterpenoid isolated from Brazilian propolis.

    J Nat Prod 2001, 64:1278-1281. PubMed Abstract | Publisher Full Text OpenURL

  12. Agüero MB, Gonzalez M, Lima B, Svetaz L, Sánchez M, Zacchino S, Feresin GE, Schmeda-Hirschmann G, Palermo J, Wunderlin D, Tapia A: Argentinean propolis from Zuccagnia punctata Cav. (Caesalpinieae) exudates: phytochemical characterization and antifungal activity.

    J Agric Food Chem 2010, 58:194-201. PubMed Abstract | Publisher Full Text OpenURL

  13. Dalben-Dota KF, Faria MG, Bruschi ML, Pelloso SM, Lopes-Consolaro ME, Svidzinski TI: Antifungal activity of propolis extract against yeasts isolated from vaginal exudates.

    J Altern Complement Med 2010, 16:285-290. PubMed Abstract | Publisher Full Text OpenURL

  14. Osiewacz HD, Scheckhuber CQ: Impact of ROS on ageing of two fungal model systems: Saccharomyces cerevisiae and Podospora anserina.

    Free Radic Res 2006, 40:1350-1358. PubMed Abstract | Publisher Full Text OpenURL

  15. Salomão K, Pereira PR, Campos LC, Borba CM, Cabello PH, Marcucci MC, de Castro SL: Brazilian propolis: correlation between chemical composition and antimicrobial activity.

    Evid Based Complement Alternat Med 2008, 5:317-324. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  16. Siqueira AB, Gomes BS, Cambuim I, Maia R, Abreu S, Souza-Motta CM, de Queiroz LA, Porto AL: Trichophyton species susceptibility to green and red propolis from Brazil.

    Lett Appl Microbiol 2009, 48:90-96. PubMed Abstract | Publisher Full Text OpenURL

  17. de Castro PA, Savoldi M, Bonatto D, Barros MH, Goldman MH, Berretta AA, Goldman GH: Molecular characterization of propolis-induced cell death in Saccharomyces cerevisiae.

    Eukaryot Cell 2011, 10(3):398-411. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  18. Brachmann CB, Davies A, Cost GJ, Caputo E, Li J, Hieter P, Boeke JD: Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR mediated gene disruption and other applications.

    Yeast 1998, 14:115-132. PubMed Abstract | Publisher Full Text OpenURL

  19. Semighini CP, Marins M, Goldman MHS, Goldman GH: Quantitative analysis of the relative transcript levels of ABC transporter Atr genes in Aspergillus nidulans by real-time reverse transcription-PCR assay.

    Appl Environ Microbiol 2002, 68:1351-1357. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  20. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks.

    Genome Res 2003, 13:2498-2504. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  21. Bader GD, Hogue CW: An automated method for finding molecular complexes in large protein interaction networks.

    BMC Bioinformatics 2003, 4:2. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  22. Scardoni G, Petterlini M, Laudanna C: Analyzing biological network parameters with CentiScaPe.

    Bioinformatics 2009, 25:2857-2859. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  23. Maere S, Heymans K, Kuiper M: BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks.

    Bioinformatics 2005, 21:3448-3449. PubMed Abstract | Publisher Full Text OpenURL

  24. Rivals I, Personnaz L, Taing L, Potier M-C: Enrichment or depletion of a GO category within a class of genes: which test?

    Bioinformatics 2007, 23:401-407. PubMed Abstract | Publisher Full Text OpenURL

  25. Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.

    J R Stat Soc Series B Stat Methodol 1995, 57:289-300. OpenURL

  26. Gruber JV, Holtz R: Examining the genomic influence of skin antioxidants in vitro.

    Mediators Inflamm 2010, pii:230450. OpenURL

  27. Ernst R, Kueppers P, Stindt J, Kuchler K, Schmitt L: Multidrug efflux pumps: substrate selection in ATP-binding cassette multidrug efflux pumps–first come, first served?

    FEBS J 2010, 277(3):540-549. PubMed Abstract | Publisher Full Text OpenURL

  28. Shahi P, Moye-Rowley WS: Coordinate control of lipid composition and drug transport activities is required for normal multidrug resistance in fungi.

    Biochim Biophys Acta 2009, 1794(5):852-859. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  29. Sá-Correia I, dos Santos SC, Teixeira MC, Cabrito TR, Mira NP: Drug:H+ antiporters in chemical stress response in yeast.

    Trends Microbiol 2009, 17(1):22-31. PubMed Abstract | Publisher Full Text OpenURL

  30. Sipos G, Kuchler K: Fungal ATP-binding cassette (ABC) transporters in drug resistance & detoxification.

    Curr Drug Targets 2006, 7(4):471-481. PubMed Abstract | Publisher Full Text OpenURL

  31. Ariño J, Ramos J, Sychrová H: Alkali metal cation transport and homeostasis in yeasts.

    Microbiol Mol Biol 2010, 74:95-120. Publisher Full Text OpenURL

  32. Inoue Y, Klionsky DJ: Regulation of macroautophagy in Saccharomyces cerevisiae.

    Semin Cell Dev Biol 2010, 21:664-670. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  33. Lynch-Day MA, Klionsky DJ: The Cvt pathway as a model for selective autophagy.

    FEBS Lett 2010, 584:1359-1366. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  34. Schauer A, Knauer H, Ruckenstuhl C, Fussi H, Durchschlag M, Potocnik U, Fröhlich K-U: Vacuolar functions determine the mode of cell death.

    Biochim Biophys Acta 2009, 1793:540-545. PubMed Abstract | Publisher Full Text OpenURL

  35. Eisenberg T, Knauer H, Schauer A, Büttner S, Ruckenstuhl C, Carmona-Gutierrez D, Ring J, Schroeder S, Magnes C, Antonacci L, Fussi H, Deszcz L, Hartl R, Schraml E, Criollo A, Megalou E, Weiskopf D, Laun P, Heeren G, Breitenbach M, Grubeck-Loebenstein B, Herker E, Fahrenkrog B, Fröhlich KU, Sinner F, Tavernarakis N, Minois N, Kroemer G, Madeo F: Induction of autophagy by spermidine promotes longevity.

    Nat Cell Biol 2009, 11:1305-1314. PubMed Abstract | Publisher Full Text OpenURL

  36. Ota C, Unterkircher C, Fantinato V, Shimizu MT: Antifungal activity of propolis on different species of Candida.

    Mycoses 2001, 44:375-378. PubMed Abstract | Publisher Full Text OpenURL

  37. Perrone GG, Tan SX, Dawes IW: Reactive oxygen species and yeast apoptosis.

    Biochim Biophys Acta 2008, 1783:1354-1368. PubMed Abstract | Publisher Full Text OpenURL

  38. Eisenberg T, Carmona-Gutierrez D, Büttner S, Tavernarakis N, Madeo F: Necrosis in yeast.

    Apoptosis 2010, 15:257-268. PubMed Abstract | Publisher Full Text OpenURL

  39. Odat O, Matta S, Khalil H, Kampranis SC, Pfau R, Tsichlis PN, Makris AM: Old yellow enzymes, highly homologous FMN oxidoreductases with modulating roles in oxidative stress and programmed cell death in yeast.

    J Biol Chem 2007, 282:36010-3623. PubMed Abstract | Publisher Full Text OpenURL

  40. Teste MA, Duquenne M, François JM, Parrou JL: Validation of reference genes for quantitative expression analysis by real-time RT-PCR in Saccharomyces cerevisiae.

    BMC Mol Biol 2009, 10:99. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  41. Borgatti SP: Centrality and network flow.

    Soc Networks 2005, 27:55-71. Publisher Full Text OpenURL

  42. Estrada E: Virtual identification of essential proteins within the protein interaction network of yeast.

    Proteomics 2006, 6:35-40. PubMed Abstract | Publisher Full Text OpenURL

  43. Estrada E, Hatano NA: Vibrational approach to node centrality and vulnerability in complex networks.

    Phys A 2010, 389:3648-3660. Publisher Full Text OpenURL

  44. Newman MEJ: A measure of betweenness centrality based on random walks.

    Soc Networks 2005, 27:39-54. Publisher Full Text OpenURL

  45. Girvan M, Newman ME: Community structure in social and biological networks.

    Proc Natl Acad Sci USA 2002, 99:7821-7826. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  46. Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M: The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics.

    PLoS Comput Biol 2007, 3:e59. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1472-6882/12/194/prepub