Using genome-wide transcriptional profiling to elucidate small-molecule mechanism

Using genome-wide transcriptional profiling to elucidate small-molecule mechanism Rebecca A Butcher1 and Stuart L Schreiber2 Transcriptional profiling with DNA microarrays can be used to measure the genome-wide transcriptional response to small molecules. Recent progress in the analysis of gene-expression data has relied on the generation of databases of profiles documenting the transcriptional effects of various compound treatments and genetic perturbations. A positive correlation between the transcriptional response induced by a novel small molecule and a database profile can provide insight into the molecule’s mechanism. Transcriptional profiling can also be used to assess a small molecule’s specificity for its target and to facilitate analysis of pathways downstream of the target. Addresses 1 Howard Hughes Medical Institute, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA 2 Broad Institute, Harvard University and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA Corresponding author: Schreiber, SL ([email protected])

Current Opinion in Chemical Biology 2005, 9:25–30 This review comes from a themed issue on Proteomics and genomics Edited by Benjamin F Cravatt and Thomas Kodadek Available online 10th November 2004 1367-5931/$ – see front matter # 2004 Elsevier Ltd. All rights reserved. DOI 10.1016/j.cbpa.2004.10.009

Abbreviations AML acute myelogenous leukemia RT-PCR reverse transcription polymerase chain reaction SFK suppressor of FK506 TOR target of rapamycin

Introduction The development of tools for genome-wide analysis, such as DNA microarrays, has revolutionized our ability to study the effects of small molecules on biological systems. However, the identification of the precise mechanism of action of a small molecule (e.g. its cellular target) remains a formidable challenge. New bioactive small molecules are identified either on the basis of their ability to produce a specific phenotype (‘forward chemical genetics’) or on the basis of their ability to interact with a specific target (‘reverse chemical genetics’) [1,2]. In the case of forward chemical genetics, the small molecule’s mechanism of action is often completely unknown, whereas in the case of reverse chemical genetics, the small molecule’s target is presumably known, but its in vivo specificity must be www.sciencedirect.com

established. Although the transcriptional profile of smallmolecule treatment provides a wealth of data, extrapolating the mechanism of the small molecule from that data is difficult and, in most cases, has been done on an ad hoc basis. New approaches relying on databases of transcriptional profiles, however, are likely to facilitate this process and make it more systematic [3,4]. By grouping uncharacterized compounds with compounds of known mechanism on the basis of the similarity of their transcriptional profiles, for example, these approaches can be used to generate hypotheses regarding mechanism. Furthermore, as recent work suggests, by comparing the transcriptional profile of a small molecule to that of known therapeutics, predictions can be made regarding the small molecule’s therapeutic class and possibly its therapeutic potential [5].

A compendium approach to target identification To use transcriptional data to understand small-molecule mechanism, several recent studies have shown the utility of a compendium approach. In this approach, transcriptional profiles are analyzed by comparison to a large collection of transcriptional profiles of various smallmolecule treatments and genetic perturbations [3,4,6]. Perturbations that target the same gene product or influence the same biological pathway are likely to induce similar transcriptional fingerprints and, hence, will tend to cluster together based on statistical analysis of the similarity of their profiles [7]. Consequently, correlations between the transcriptional profiles of novel small molecules and database profiles can provide insights into small-molecule mechanism (Figure 1a). Hughes et al. generated a compendium of transcriptional profiles of yeast gene-deletion mutants and clustered smallmolecule profiles with mutant profiles [3]. The high degree of similarity between the profile of wild-type cells treated with the drug dyclonine and the profile of Derg2 mutant cells suggested that the target of dyclonine was Erg2p. One limitation to this approach is that it will only work for mutants/compounds that have a transcriptional fingerprint. Under the single condition employed by Hughes et al., only roughly half of the mutants had a transcriptional phenotype significantly different from wild-type. However, as the authors suggest, the reference database could be expanded to include profiles of the mutants under a panel of different growth conditions in an effort to find conditions in which the mutants do have a transcriptional phenotype. More recently, Boshoff et al. took a similar compendium approach to investigate drug mechanisms in MycobacterCurrent Opinion in Chemical Biology 2005, 9:25–30

26 Proteomics and genomics

Figure 1

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the authors were able to group compounds with similar known mechanisms of action and were also able to suggest mechanisms of action for compounds with unknown targets. For example, although the natural product ascididemin is thought to inhibit growth in eukaryotic cells through inhibition of DNA topoisomerase and DNA cleavage, its profile clustered with the profiles of known iron-scavenging agents. The authors were able to verify subsequently that ascididemin does, indeed, inhibit growth in M. tuberculosis through iron depletion.

Using transcriptional profiling to measure small-molecule specificity Mutant 3

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Many off-target effects Current Opinion in Chemical Biology

Using transcriptional profiling to understand small-molecule mechanism and target specificity. (a) Insights into the mechanism of a small molecule can be gained by comparing its transcriptional profile to a database of profiles of various compound treatments and genetic perturbations. In this hypothetical example, the compound with unknown mechanism probably influences the same protein or pathway as does Mutant 2 or Compound 3. (b) The transcriptional profile of cells treated with a small molecule that is specific for its target should resemble the transcriptional profile of cells deleted for the target (assuming the small molecule causes a loss-of-function).

ium tuberculosis [4]. A dataset of profiles was generated of the transcriptional response of M. tuberculosis to 75 drugs and growth inhibitory conditions. By clustering the data, Current Opinion in Chemical Biology 2005, 9:25–30

For small molecules that were identified through reverse chemical genetics, or for small molecules for which the target is otherwise known, transcriptional profiling can help establish the in vivo specificity of a small molecule for its target (Figure 1b) [8–16]. If a small molecule is indeed specific to a particular protein target, then the transcriptional profile of cells treated with the small molecule should resemble that of cells depleted in the putative target (assuming that the small molecule causes a loss-of-function in its target). In addition, if the small molecule has only one primary target, then the small molecule should have very few transcriptional effects in cells lacking the putative target (‘targetless’ cells). Those genes that are modulated by the small molecule in the absence of the known target are considered offtarget effects and can reveal other previously unknown targets of the small molecule [8]. Bedalov et al. identified splitomycin, a small-molecule inhibitor of the yeast histone deacetylase Sir2p, through a forward chemical genetic screen, and showed that the transcriptional profile of splitomycin-treated wild-type cells resembled that of Dsir2 cells [10]. The splitomycin profile also resembled, to a lesser extent, the profile of cells lacking another histone deacetylase, Hst1p, suggesting that splitomycin might inhibit not only Sir2p, but also Hst1p. Hence, transcriptional profiling was used as a measure of the in vivo selectivity of the small molecule. Going one step further, Hirao et al. developed derivatives of splitomycin that showed selectivity towards either Sir2p or Hst1p and then verified this selectivity by transcriptionally profiling wild-type cells treated with the inhibitors and comparing the profiles to those of the Dsir2 and Dhst1 mutant cells [11]. Small molecules may not necessarily induce a simple lossof-function, but can instead induce a more subtle phenotype. The small molecule uretupamine was discovered in a reverse chemical genetic screen for small molecules that bind to the yeast protein Ure2p [12]. However, comparison of the transcriptional profile of wild-type cells treated with uretupamine to that of Dure2 cells revealed that uretupamine influences the expression of only a www.sciencedirect.com

Using genome-wide transcriptional profiling to elucidate small-molecule mechanism Butcher and Schreiber 27

subset of genes downstream of Ure2p. This evidence suggests a model in which uretupamine does not simply mimic a Dure2 deletion mutant and block all Ure2p functions, but rather blocks specific Ure2p functions. Given that no target identification method can unequivocally identify a small molecule’s target, it is often beneficial to use several methods in conjunction. Using a variety of techniques including transcriptional profiling, we identified the protein target of several small-molecule suppressors of the drug FK506 (SFKs) [13]. These suppressors had been discovered in a phenotypic screen for small molecules that suppress growth inhibition by FK506 in yeast. In order to identify potential targets of the SFKs, we screened the set of yeast gene-deletion mutants for mutants that mimicked the effect of the small molecules (e.g. showed resistance to FK506). By comparing the transcriptional profile of cells treated with the SFKs to the profile of one of the candidate mutants, we were able to establish tentatively and verify subsequently the protein target of the SFKs.

Transcriptional profiling and pathway analysis Transcriptional profiling can also be used to dissect the signaling pathways downstream of a small molecule’s protein target. For example, the small molecule rapamycin, which targets the Target of Rapamycin (TOR) proteins, coupled with genome-wide microarrays, has enabled a detailed understanding of the downstream transcriptional effects of TOR inhibition [17–19]. Because many of the direct effects of rapamycin in yeast

are transcriptional, rather than post-transcriptional, rapamycin makes an ideal small molecule to study using microarrays. Using epistasis analysis, effector proteins of the TOR pathway have been implicated in different arms of the rapamycin-induced transcriptional program [20,21]. Individual effectors have been deleted and the transcriptional effects of rapamycin in these deletion backgrounds has been monitored to identify what aspects of the transcriptional program the effectors mediate (Figure 2a–c). Given that the mRNA expression level of the effectors themselves is often not modulated in response to small-molecule treatment, identification of the effectors solely from transcriptional data is usually difficult [22–25]. A similar epistasis approach has been taken in several other recent studies. Yoshimoto et al. have used FK506, which inhibits the Ca2+/calmodulin-dependent phosphatase calcineurin, to study the role of the transcription factor Tcn1p/Crz1p in calcineurin-dependent transcription in yeast [26]. Fleming et al. have shown that the transcriptional response of yeast to the proteasome inhibitor PS-341 requires the transcription factor Rpn4p [27].

Using gene expression to predict biological responses to small molecules Gene-expression data can be used to develop models to predict the biological effects of a small molecule in a given cell type or organism. Here, the immediate goal is not identification of a precise molecular mechanism, but rather an ability to anticipate, for example, the potential

Figure 2

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Current Opinion in Chemical Biology

Using transcriptional profiling and epistasis analysis to understand the transcriptional network downstream of a small molecule’s target. (a) Rapamycin inhibits TOR and results in a transcriptional program mediated by downstream effectors. (b) Treating Effector #1-deleted cells with rapamycin identifies transcriptional changes mediated by Effector #1. (c) Treating Effector #2-deleted cells with rapamycin identifies transcriptional changes mediated by Effector #2. www.sciencedirect.com

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Several studies have shown that the transcriptional response to different therapeutic small molecules can be used to distinguish the therapeutics as well as to classify them into different categories [5,33,34]. Databases containing the transcriptional responses to known therapeutics are being used to construct models that could potentially predict the efficacy of candidate molecules in the treatment of disease. Gunther et al. measured the transcriptional response of neurons in cell culture to members of three different classes of drugs (antidepressants, antipsychotics and opioid receptor agonists) and used this dataset to develop statistical models to distinguish between the three classes [5]. Marker genes were identified that could accurately predict a drug’s class and hence accurately predict its therapeutic efficacy. The fact that this prediction could be made on the basis of a few marker genes suggests that although many of the compounds within each class operate through different targets in different pathways, they share a common downstream mechanism of action leading to their clinical efficacy. In the future, transcriptional profiling may facilitate lead prioritization in drug discovery by identifying candidate compounds that are likely to be ‘effective’ based on the similarity of their transcriptional profile to that of other known drugs in a particular therapeutic class. This approach may become particularly important for complex diseases such as psychiatric diseases (e.g. depression) for which there is no cellular assay to facilitate screening for therapeutic compounds [35]. Gene-expression data can be used not only to determine whether a candidate molecule might have a biological activity of interest, but also to help predict whether a molecule will have adverse biological effects. In the field of toxicology where traditional predictors of toxicity (e.g. molecular structure, in vitro cytotoxicity assays) have shown limited success, genomic approaches to monitoring the cellular response to small-molecule agents, such as transcriptional profiling, may provide a more accurate Current Opinion in Chemical Biology 2005, 9:25–30

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Differentiation signature: Statistical analysis

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effectiveness of the molecule against a particular diseased state. Some of the first attempts to use gene-expression data to predict susceptibility to a small molecule were done by correlating the transcriptional profiles of the National Cancer Institute’s 60 human cancer cell lines to their growth in the presence of chemotherapeutic agents [28–31]. In a few cases, the expression levels of particular genes in the cell lines could be correlated to susceptibility of the cell lines to particular drugs, and hypotheses could be made regarding causality. More recently, Blower et al. have attempted to identify molecular substructures in compounds that inhibit the growth of cell lines overexpressing a particular gene [32]. These analyses, however, were based on the transcriptional profiles of untreated cells and did not incorporate information on the post-treatment transcriptional response.

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Secondary assays Using transcriptional markers of differentiation to screen for small-molecule inducers of differentiation. (a) Statistical methods were used to identify marker genes that were highly predictive of differentiation. (b) Small molecules were screened for their ability to induce the transcriptional differentiation signature using RT–PCR and MS. This figure was adapted in part from reference [41], courtesy of Nature Genetics (http://www.nature.com/ng/) and the authors. www.sciencedirect.com

Using genome-wide transcriptional profiling to elucidate small-molecule mechanism Butcher and Schreiber 29

means of risk assessment [36,37]. Several recent studies have applied transcriptional profiling to toxicology and used databases of profiles to predict compound class and possible toxicity [38–40].

References and recommended reading

Transcriptional profiling as a small-molecule screening tool

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Mayer TU: Chemical genetics: tailoring tools for cell biology. Trends Cell Biol 2003, 13:270-277.

In addition to facilitating the characterization of small molecules identified from screens, transcriptional profiling can also play a role in the screening process itself. In theory, transcriptional profiling could be used to screen for novel small molecules that induce complex phenotypes by first profiling the phenotype and then screening for small molecules that induce a similar profile. Given the current state of microarray technology, however, it would be too inefficient and costly to profile hundreds of thousands of compounds individually in search of the few compounds that induce the transcriptional program of interest. Stegmaier et al. have circumvented this problem by taking a surrogate marker approach, in which a transcriptional program is reduced to a handful of marker genes. To identify small molecules that would induce terminal differentiation in acute myelogenous leukemia (AML) cells, Stegmaier et al. profiled AML cells and their differentiated counterparts and identified a transcriptional signature of differentiation that consisted of four marker genes and a control [41]. They then screened for small molecules that could induce these marker genes using a high-throughput method based on reverse transcription-polymerase chain reaction (RT–PCR) and mass spectrometry (MS) (Figure 3). Several compounds were identified that not only regulated the marker genes, but also were able to produce the full transcriptional profile associated with differentiation and tested positive in several phenotypic assays for differentiation. Hence, the authors were successfully able to reduce the complex phenotype of differentiation to four marker genes for screening purposes. This strategy is powerful in that it can potentially be adapted to screen for small-molecule inducers of any complex phenotype for which marker transcripts can be identified.

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Conclusions Transcriptional profiling offers a means to monitor the genome-wide transcriptional effects of small-molecule treatment. The development of an ever-growing public database of profiles of compound treatments and genetic perturbations will facilitate the discovery of the mechanism of action of novel small molecules, as well as their therapeutic potential.

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We thank Alykhan Shamji for helpful suggestions and for the inspiration for Figure 2. RAB was supported by a fellowship from the National Science Foundation. SLS is an Investigator with the Howard Hughes Medical Institute. Research in this area from our laboratory has been supported by grants from the NCI and NIGMS.

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