Detection of changes in gene regulatory patterns, elicited by perturbations of the Hsp90 molecular chaperone complex, by visualizing multiple experiments with an animation
© Echeverría et al; licensee BioMed Central Ltd. 2011
Received: 8 March 2011
Accepted: 14 June 2011
Published: 14 June 2011
To make sense out of gene expression profiles, such analyses must be pushed beyond the mere listing of affected genes. For example, if a group of genes persistently display similar changes in expression levels under particular experimental conditions, and the proteins encoded by these genes interact and function in the same cellular compartments, this could be taken as very strong indicators for co-regulated protein complexes. One of the key requirements is having appropriate tools to detect such regulatory patterns.
We have analyzed the global adaptations in gene expression patterns in the budding yeast when the Hsp90 molecular chaperone complex is perturbed either pharmacologically or genetically. We integrated these results with publicly accessible expression, protein-protein interaction and intracellular localization data. But most importantly, all experimental conditions were simultaneously and dynamically visualized with an animation. This critically facilitated the detection of patterns of gene expression changes that suggested underlying regulatory networks that a standard analysis by pairwise comparison and clustering could not have revealed.
The results of the animation-assisted detection of changes in gene regulatory patterns make predictions about the potential roles of Hsp90 and its co-chaperone p23 in regulating whole sets of genes. The simultaneous dynamic visualization of microarray experiments, represented in networks built by integrating one's own experimental with publicly accessible data, represents a powerful discovery tool that allows the generation of new interpretations and hypotheses.
Keywordsgene expression microarray analysis visualization yeast stress response molecular chaperones Hsp90 inhibitor gene deletion
In the current post-genomic era, an increasing amount of data is generated by the application of high-throughput technologies. Expression profiling analyses using DNA microarray approaches are extensively used to study global changes in gene expression patterns of multiple cell types and tissues under different conditions. Moreover, there are publicly accessible databases containing the experimentally established DNA binding sequences for transcription factors (TF). To identify interaction partners of a protein of interest, several large-scale methods such as yeast two-hybrid screens, tandem affinity purification followed by mass spectrometric analyses, and protein chips are commonly used. Proteome-wide studies to identify protein-protein interaction (PPI) partners for many proteins have resulted in several publicly available and well-curated databases, which can be used to elucidate and to explore PPI networks. The integration of this large amount of data from many sources in a comprehensive and insightful way requires the use of computational tools to combine, to manipulate and to visualize the information [1–3]. Visualization constitutes in itself a challenge. This is particularly true for the multivariate type of data that omics approaches generate. A multitude of tools have been developed that allow both still and animated data visualization [4, 5]. However, it is increasingly recognized that visualizing data is more than just presenting it; it also constitutes an exploration tool . It could even be argued that the characteristics of human pattern recognition  make the human subject a powerful ally of mathematical algorithms for the discovery of principles in complex data.
Hsp90 is an abundant molecular chaperone that is essential for many cellular regulatory and signal transduction systems by promoting the functionally competent state of a large list of client proteins. Hsp90 recruits a cohort of associated partners or co-chaperones, which form a variety of multiprotein complexes with Hsp90. These co-chaperones assist Hsp90 by modulating its ATPase cycle and by facilitating its interactions with various client/substrate proteins [8–10]. An extensively studied Hsp90 co-chaperone is p23 (known as Sba1 in the budding yeast Saccharomyces cerevisiae) [11, 12]. This ubiquitous acidic protein binds and stabilizes the ATP-bound dimeric form of Hsp90 [10, 13, 14], inhibits ATP hydrolysis and thereby traps Hsp90 in a state with high affinity for client proteins [15, 16]. Hsp90 inhibitory drugs such as geldanamycin and radicicol, and post-translational modifications, such as the hyperacetylation of Hsp90, lead to the release of p23 from Hsp90, which destabilizes the Hsp90-substrate interaction [17, 18]. Moreover, it was found that in the absence of p23, yeast and mammalian cells are viable [19, 20], but become hypersensitive to Hsp90 inhibitors . Considering the biochemically established important role of p23 in regulating Hsp90, we wanted to investigate the transcriptional adaptations of budding yeast when p23 is removed from the Hsp90 complex either genetically or pharmacologically. We carried out microarray experiments with wild-type and p23-deficient S. cerevisiae strains, in which Hsp90 function was additionally impaired with the Hsp90 inhibitor radicicol.
Early on, it became apparent that the classical pairwise comparison and subsequent clustering of the microarray data, based on changes in gene expression alone, would be insufficient to reveal more complex underlying regulatory patterns. We therefore linked and grouped the genes according to their relationships in expression across experiments, their interactions at the protein level, and their cellular localization. Moreover, we explored the possibility that some genes might be coregulated by the same TFs by searching for the presence of enriched TF binding sites in their promoters. All these approaches combined together allowed us to visualize and to detect regulatory patterns of genes affected (directly or indirectly) simultaneously by Hsp90 and p23. The results of these analyses are of heuristic value to build new hypotheses for further validation experiments.
RNA sample preparation and microarray analysis
The strain BY4741 (Mata his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) was used as the wild-type strain and its derivative BYP2  as the Δsba1 strain. The strains were grown in YEPD with 10 μM radicicol or with the same volume of the vehicle DMSO for about 14 hours to reach the desired OD600 ≈ 0.6-1.0. RNA was extracted from triplicate cultures using the hot-phenol method and subsequently cleaned up further on columns with the QiaGen RNA purification Kit. RNA quality was assessed with a BioAnalyzer (Bio-Rad). Expression profiles for these four experimental conditions using triplicate samples were determined using the YG_S98 gene chips from Affymetrix at the genomics platform of the University of Geneva. The data are available from ArrayExpress at EBI (access code E-TABM-573). The raw data obtained from the expression profiling was analyzed with the software GeneSpring 7.3. The samples were subjected to a 2-way Anova statistical test and a cut-off of p-value < 0.05 with the volcano plot for pair-wise comparisons between two experimental conditions.
TF binding site analysis
Sequence retrieval: 1000 nucleotides upstream of the transcription start sites were retrieved from the S. cerevisiae Refseq genomic DNA sequence available at NCBI for the set of genes characterized by a 1.5 fold change in expression when Hsp90 was inhibited with radicicol in wild-type cells (Additional File 1 Table S1). Similarly, 1000 nucleotides upstream of the transcription start sites for all Refseq annotated S. cerevisiae genes were retrieved to be used as the background/control sequences. TF identification: TRANSFAC Professional 11.4 was used as described previously  except that it was done with the set of all fungal TF binding sites retrieved from matrix.dat in TRANSFAC. The over-represented TF binding sites were sorted by their relative over-representation in the target sequence set (see Additional File 2 Table S2).
Yeast reporter gene assays
The reporter gene assays were done with yeast strain YNK100 (relevant genotype: pdr5-101)  transformed with the plasmids pSTRE-LacZ(TRP1)  and pLG/Z , which contain the STRE from the CTT1 promoter and the UAS of the GAL1 promoter, respectively, upstream of a minimal CYC1 promoter driving β-galactosidase expression. The transformants with pSTRE-LacZ(TRP1) and pLG/Z were grown in YEP complemented with 2% glucose and 2% raffinose/2% glycerol, respectively. Overnight cultures were diluted to a density of OD600 = 0.3, incubated for 4 hours before adding 30 μM radicicol in ethanol or vehicle alone. In the case of the pLG/Z transformants, galactose was added at the same time to induce reporter gene expression. After an additional 3 hours of incubation, β-galactosidase activities were measured using standard protocols and normalized to cell densities (OD600). For all growth conditions and strains, the growth temperature was 25°C. The data points represent the averages of three and two independent experiments with replicates for the STRE and GAL reporters, respectively.
Integration of microarray data with PPI networks
PPI network generation and expression data integration using Cytoscape (http://www.cytoscape.org) has been extensively described by Cline and colleagues . Briefly, all the physical interactions annotated in the BioGrid database for S. cerevisiae (release 2.0.49) were formatted into an Excel spreadsheet and then uploaded into Cytoscape using the function "Import Network from a table". From this network, the first level of PPIs was retrieved for the list of genes showing significant fold change in all microarray experiments. One "node" in this network corresponds to a gene/protein and the connection between them is called "edge" and it refers to the interaction between these two nodes. The expression data was then loaded onto this network, importing the fold change values in a tab-delimited format. This allows one later to compute a correlation network using the Cytoscape plugin ExpressionCorrelation (http://www.baderlab.org/Software/ExpressionCorrelation). This plugin facilitates the assembly of a co-expression network, integrated into the PPI network, from microarray data, by computing the Pearson correlation coefficient for all pairwise comparisons. We used a cutoff of 0.9 in this analysis, and so, any correlations above these threshold values are displayed again as an edge between two nodes. Network images were generated using Cytoscape version 2.6.1 and each node was placed according to its annotated intracellular localization (Gene Ontology [GO] cellular component) in cellular compartment layers using the plugin Cerebral . All the schemes were first exported as Cerebral views from Cytoscape and then loaded into Adobe Illustrator for editing. The animation was generated in Adobe Illustrator and exported as a Flash file.
Microarray analysis and data organization in a graph
Finding biologically relevant co-regulation patterns
Whereas the more or less multidimensional dependency graphs generated by these binary comparisons are useful and yield interesting results, some patterns might only become apparent by investigating the data of all performed experiments simultaneously as highlighted in Figure 1. Specifically, looking at all experiments at once may facilitate the detection of genes or sets of genes with similar or particular expression patterns across the different genetic and pharmacological conditions (Figure 1B). To this end, all the networks generated from the different pairwise comparisons were superimposed on each other as a stack and converted into a dynamic graph or animation (Additional File 3 Movie S1). This animation then served as a discovery tool to extract novel patterns of behavior. By visualizing the recurring patterns of colors (red or green according to their expression values), genes could be assigned to different clusters (Figure 1B), which are coordinately or simultaneously regulated by Hsp90 and p23 (Figure 2C and Additional File 3 Movie S1). In Figure 2C and Additional File 3 Movie S1, highlighted regions indicate the positions in the graph where most of the nodes belonging to each pattern were visually identified. For example, by watching the animation (Additional File 3 Movie S1), one can observe the following pattern of node color changes in the central/upper left region of the graph: red, green, green and red (arbitrarily referred to as pattern A). Similarly, there is another pattern in the central/upper right region: green, red, red and green (pattern B). The classification of all genes in our study according to these patterns is given in column "Pattern" of the Additional File 1 Table S1.
Inference of functions for patterns differentially regulated by Hsp90 and p23
Following the identification of different patterns by this animation, we extracted the genes corresponding to each pattern (Additional File 1 Table S1). Full GO data for genes belonging to each pattern were retrieved to address the potential functions of these groups. Genes from pattern A (Hsp90act, p23rep) showed enrichment in functional terms associated with iron and ion homeostasis, whereas functions of those belonging to pattern B (Hsp90rep, p23act) fell into a mitochondrial function (respiration), kinase activity dependent on cyclins and progression through the cell cycle. Genes from pattern C (Hsp90rep, p23rep) exhibit GO terms related with conjugation processes, catabolism of nitrogen sources and response to stress. The concomitant absence of Hsp90 and p23 induces the expression of stress-related proteins like Ssa4 (encoding one of the cytosolic Hsp70 isoforms), Hsp26, Hsp31, Ddr2. Interestingly, both chaperones might have a positive influence on the expression of genes (pattern D) related with mitochondrial protein import and carbon source metabolism (TOM70, OAC1, MDH2 and DIC1), a phosphatase that positively regulates the Hsp90 chaperone machinery (Ppt1) , or chromatin remodelers like Nhp6 and Imd2. Furthermore, the genes lacking any particular pattern (group E), do not show significant enrichment for any functional annotations. Interestingly, the genes for Hsp90 (HSP82 encoding one of the two cytosolic isoforms) and for its co-chaperones Aha1, Cpr6 and Sti1 belong to this group of genes, thus implying that their regulation is not affected by the interplay between Hsp90 and p23.
Here we have presented a novel approach to integrate experimental gene expression profiles with already available data. We were able to combine our own data from microarray experiments and data from public databases of PPIs, TF, and GO in a rational manner to address the global biological functions of Hsp90 and p23 both individually and, perhaps more interestingly, concomitantly.
Several key features of our working pipeline (Figure 1) allowed us to process and to understand certain aspects of our data. First, we worked with genes showing a significant fold change in at least one, often several pairwise comparisons that were analyzed (Figure 1A). Second, we organized them into a graph establishing connections between nodes according to their co-expression levels and their ability to interact at the protein level (Figure 1A). Third, we organized the nodes in the graph according to their annotated intracellular localization. Using the Cerebral layout, highly interconnected nodes with the same intracellular localization get grouped in the final individual graphs  (Figure 1A). And finally, a novel and essential feature of the analysis was to convert all the generated graphs into an animation to facilitate the visualization of similar expression patterns of genes topologically grouped in the individual graphs (Figures 1B and 2A, and Additional File 3 Movie S1). It is important to emphasize that this pattern detection was only possible by this method and that we failed to detect these patterns with more standard clustering algorithms (data not shown). The latter may have discarded or "undervalued" nodes with only marginal changes in gene expression whereas a human subject's pattern recognition would have incorporated some of these as well. While small fold changes may not have any significant value by themselves, as part of a larger animated pattern, they attract the attention of a human observer and they may become corroborative. The interpretation of these patterns matched the information contained in the pairwise comparisons of the microarray data (Figure 5), which finally allowed us to uncover the genes that are regulated by both Hsp90 and p23. Thus, the use of an animation to visualize patterns was a key tool to connect the data of the different microarray experiments.
Once the networks had been built, we were able to perform two kinds of analyses, single pairwise ones and an animation-assisted one that takes advantage of all pairwise comparisons. With the single analysis we found that 114 genes were up-regulated upon pharmacological Hsp90 inhibition (Figure 2B, red nodes and Additional File 1 Table S1, genes boxed in blue). Of these, 26 presented an enrichment of GO terms related with stress response (Additional File 1 Table S1, genes highlighted in orange), in accordance with the finding that these genes contained an increased occurrence of STREs in their promoters. The stress response in yeast is regulated by the TFs Hsf1 and Msn2/Msn4, which bind to promoters containing heat-shock response elements and STREs, respectively [34, 35]. Some of these promoters could also contain both elements [36, 37]. From our study, it appears that the sole inhibition of Hsp90 is sufficient to up-regulate stress-related genes with STREs in their promoters. This would indicate that Hsp90 influences the Msn2/Msn4 system. Analyzing the yeast PPI networks extracted from public repositories and the literature, we can infer that Hsp90 (and co-chaperones) interacts with kinases known to repress the Msn2/Msn4 system (and the stress response) [38–41] (Additional File 5 Table S4). Since many kinases are known Hsp90 clients (see regularly updated list at http://www.picard.ch/downloads/downloads.htm), we can speculate that Hsp90 and its co-chaperones maintain kinases responsible for Msn2/Msn4 repression in a functional state thereby regulating the stress-response (Additional File 6 Figure S1). Interestingly, with animation-assisted analysis, we noticed that not all stress-related genes belong to the same expression pattern. Most of these genes belong to patterns B and C, indicating that they are at the same time affected by Hsp90 and p23 (Additional File 3 Movie S1). It is also clear that Hsp90 and other co-chaperones (Sti1, Aha1, Cpr6) are up-regulated upon pharmacological Hsp90 inhibition, and yet they do not show any particular pattern. Hsf1 is the only stress-inducible transcriptional regulator of Hsp90 protein expression in yeast [42, 43]. Conversely, Hsp90 represses gene expression from Hsf1-dependent promoters , establishing a negative feedback loop for its own expression. Conceivably, the genes for Hsp90 and at least some of its co-chaperones are regulated only by Hsf1, while the other stress-related genes exhibit a more complex regulation involving the concurrent action of Hsp90 and p23, potentially through the Msn2/Msn4 system.
The animation-assisted analysis involving the visualization of complex connection maps with an animation uniquely allowed us to integrate the information from all the pairwise comparisons that were possible with the microarray data obtained in this study. We were able to address the potential actions of Hsp90 and p23 for the expression of a large list of genes. Moreover, we could gain insights about groups of genes that are regulated together or individually by these two proteins. Even though Hsp90 and p23 seem to be essential partners for most of their functions, it is becoming clear that they can exert both coordinated as well as opposite influences on some of their clients, as previously described for example for the regulation of the telomerase complex or for recycling of nuclear receptors at chromatin targets [[45, 46], and reviewed in ref. ]. Independent or even opposite influences are suggested by gene expression patterns A and B. It is conceivable, for example, that Hsp90, in association with other co-chaperones, is required to assist a particular transcription factor or complex at a set of target genes while the primary role of p23 for the same set of genes might be to disrupt transcription factor complexes. Taking into account indirect pathways, many more schemes are possible as well. At this point, we can only speculate about the underlying mechanisms. It will be both challenging and interesting to dissect them experimentally both in yeast and in other organisms.
Our new approach might be appropriate to process a variety of microarray data. Following enrichment with biological annotations from several other sources such as PPI, TF, GO and localization databases, the dynamic visualization represents an additional powerful discovery tool. It remains to be tested to what extent it can be more generally applied, notably to even larger sets of genes or different experimental conditions. Although it may ultimately be possible to replace the reiterated visual inspection of the animation with an algorithm that would automatically identify patterns in multiple dependency graphs, we contend that the human eye and brain are currently simpler clustering tools that are also more accessible to a wide range of wet bench scientists.
This work was supported by the Canton de Genève, the Swiss National Science Foundation, and the Fondation Medic.
We thank Brian Freeman for providing the yeast strain YNK100 and David Engelberg for plasmid pSTRE-LacZ(TRP1). We are grateful to the collaborators of the genomics platform of the NCCR Frontiers-in-Genetics and the University of Geneva for their assistance with the microarray experiments.
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