Drug target validation and identification of secondary drug target effects using DNA microarrays

Multiplex assay for condition-dependent changes in protein-protein interactions

Changes in protein-protein interactions that occur in response to environmental cues are difficult to uncover and have been poorly characterized to date. Here we describe a yeast-based assay that allows many binary protein interactions to be assessed in parallel and under various conditions. This method combines molecular bar-coding and tag array technology with the murine dihydrofolate reductase-based protein-fragment complementation assay. A total of 238 protein-fragment complementation assay strains, each representing a unique binary protein complex, were tagged with molecular barcodes, pooled, and then interrogated against a panel of 80 diverse small molecules. Our method successfully identified specific disruption of the Hom3:Fpr1 interaction by the immunosuppressant FK506, illustrating the assay's capacity to identify chemical inhibitors of protein-protein interactions. Among the additional findings was specific cellular depletion of the Dst1:Rbp9 complex by the anthracycline drug doxorubicin, but not by the related drug idarubicin. The assay also revealed chemical-induced accumulation of several binary multidrug transporter complexes that largely paralleled increases in transcript levels. Further assessment of two such interactions (Tpo1:Pdr5 and Snq2:Pdr5) in the presence of 1,246 unique chemical compounds revealed a positive correlation between drug lipophilicity and the drug response in yeast.

Examining the interactome of huperzine A by magnetic biopanning

Huperzine A is a bioactive compound derived from traditional Chinese medicine plant Qian Ceng Ta (Huperzia serrata), and was found to have multiple neuroprotective effects. In addition to being a potent acetylcholinesterase inhibitor, it was thought to act through other mechanisms such as antioxidation, antiapoptosis, etc. However, the molecular targets involved with these mechanisms were not identified. In this study, we attempted to exam the interactome of Huperzine A using a cDNA phage display library and also mammalian brain tissue extracts. The drugs were chemically linked on the surface of magnetic particles and the interactive phages or proteins were collected and analyzed. Among the various cDNA expressing phages selected, one was identified to encode the mitochondria NADH dehydrogenase subunit 1. Specific bindings between the drug and the target phages and target proteins were confirmed. Another enriched phage clone was identified as mitochondria ATP synthase, which was also panned out from the proteome of mouse brain tissue lysate. These data indicated the possible involvement of mitochondrial respiratory chain matrix enzymes in Huperzine A's pharmacological effects. Such involvement had been suggested by previous studies based on enzyme activity changes. Our data supported the new mechanism. Overall we demonstrated the feasibility of using magnetic biopanning as a simple and viable method for investigating the complex molecular mechanisms of bioactive molecules.

Yeast toxicogenomics: genome-wide responses to chemical stresses with impact in environmental health, pharmacology, and biotechnology

The emerging transdisciplinary field of Toxicogenomics aims to study the cell response to a given toxicant at the genome, transcriptome, proteome, and metabolome levels. This approach is expected to provide earlier and more sensitive biomarkers of toxicological responses and help in the delineation of regulatory risk assessment. The use of model organisms to gather such genomic information, through the exploitation of Omics and Bioinformatics approaches and tools, together with more focused molecular and cellular biology studies are rapidly increasing our understanding and providing an integrative view on how cells interact with their environment. The use of the model eukaryote Saccharomyces cerevisiae in the field of Toxicogenomics is discussed in this review. Despite the limitations intrinsic to the use of such a simple single cell experimental model, S. cerevisiae appears to be very useful as a first screening tool, limiting the use of animal models. Moreover, it is also one of the most interesting systems to obtain a truly global understanding of the toxicological response and resistance mechanisms, being in the frontline of systems biology research and developments. The impact of the knowledge gathered in the yeast model, through the use of Toxicogenomics approaches, is highlighted here by its use in prediction of toxicological outcomes of exposure to pesticides and pharmaceutical drugs, but also by its impact in biotechnology, namely in the development of more robust crops and in the improvement of yeast strains as cell factories.

CMRF: analyzing differential gene regulation in two group perturbation experiments

BACKGROUND

Microarray experiments often measure expressions of genes taken from sample tissues in the presence of external perturbations such as medication, radiation, or disease. The external perturbation can change the expressions of some genes directly or indirectly through gene interaction network. In this paper, we focus on an important class of such microarray experiments that inherently have two groups of tissue samples. When such different groups exist, the changes in expressions for some of the genes after the perturbation can be different between the two groups. It is not only important to identify the genes that respond differently across the two groups, but also to mine the reason behind this differential response. In this paper, we aim to identify the cause of this differential behavior of genes, whether because of the perturbation or due to interactions with other genes.

RESULTS

We propose a new probabilistic Bayesian method CMRF based on Markov Random Field to identify such genes. CMRF leverages the information about gene interactions as the prior of the model. We compare the accuracy of CMRF with SSEM and Student's t test and our old method SMRF on semi-synthetic dataset generated from microarray data. CMRF obtains high accuracy and outperforms all the other three methods. We also conduct a statistical significance test using a parametric noise based experiment to evaluate the accuracy of our method. In this experiment, CMRF generates significant regions of confidence for various parameter settings.

CONCLUSIONS

In this paper, we solved the problem of finding primarily differentially regulated genes in the presence of external perturbations when the data is sampled from two groups. The probabilistic Bayesian method CMRF based on Markov Random Field incorporates dependency structure of the gene networks as the prior to the model. Experimental results on synthetic and real datasets demonstrated the superiority of CMRF compared to other simple techniques.

Use of Gene Networks for Identifying and Validating Drug Targets

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery.

A searchable cross-platform gene expression database reveals connections between drug treatments and disease

Background

Transcriptional data covering multiple platforms and species is collected and processed into a searchable platform independent expression database (SPIED). SPIED consists of over 100,000 expression fold profiles defined independently of control/treatment assignment and mapped to non-redundant gene lists. The database is thus searchable with query profiles defined over genes alone. The motivation behind SPIED is that transcriptional profiles can be quantitatively compared and ranked and thus serve as effective surrogates for comparing the underlying biological states across multiple experiments.

Results

Drug perturbation, cancer and neurodegenerative disease derived transcriptional profiles are shown to be effective descriptors of the underlying biology as they return related drugs and pathologies from SPIED. In the case of Alzheimer's disease there is high transcriptional overlap with other neurodegenerative conditions and rodent models of neurodegeneration and nerve injury. Combining the query signature with correlating profiles allows for the definition of a tight neurodegeneration signature that successfully highlights many neuroprotective drugs in the Broad connectivity map.

Conclusions

Quantitative querying of expression data from across the totality of deposited experiments is an effective way of discovering connections between different biological systems and in particular that between drug action and biological disease state. Examples in cancer and neurodegenerative conditions validate the utility of SPIED.

Barcode sequencing for understanding drug-gene interactions

With the advent of next-generation sequencing (NGS) technology, methods previously developed for microarrays have been adapted for use by NGS. Here we describe in detail a protocol for Barcode analysis by sequencing (Bar-seq) to assess pooled competitive growth of individually barcoded yeast deletion mutants. This protocol has been optimized on two sequencing platforms: Illumina's Genome Analyzer IIx/HiSeq2000 and Life Technologies SOLiD3/5500. In addition, we provide guidelines for assessment of human knockdown cells using short-hairpin RNAs (shRNA) and an Illumina sequencing readout.

Nonlinear response of gene expression to chemical perturbations: a noise-detector model and its predictions

The widespread use of microarrays provided a first glimpse at some simple laws and organizing principles that govern the transcriptome. Previous analyses have shown that the transcriptional organization is very heterogeneous and characterized by a power-law decay for gene expression levels. Moreover, a simple law was unveiled suggesting that gene expression dynamic changes under stress are proportional to their initial expression values. However, to elucidate and assess the underlying governing principles of transcriptional organization, we do not only need to identify them, but also provide theoretical models that are able to faithfully capture and reproduce them. Here we present a method to investigate the gene expression dynamics inspired by the theory of nonlinear transformation of random signals and noise. The model is able to explain not only the well-known power-law decay for abundance of expression levels, but also to reproduce the linear dependence of the standard deviation of gene expression change with respect to the initial expression value (also known as rich-travels-more dynamics). To our knowledge, this is the first model applied to gene expression dynamics that is able to simultaneously predict both statistical features. The theoretical framework derives an indicator to measure the coupling between gene expression and specific perturbations. Using genome-wide transcriptional data, this indicator identifies genes strongly coupled to specific inflammatory responses to different pathogens. The novel application of signal and noise theory to study intracellular responses and gene expression changes offers not only a new theoretical avenue to study transcriptional responses to environmental stresses and chemical signals but also provides predictive capability at the genome scale.

The end game of chemical genetics: target identification

The use of classical genetic and molecular biology methods along with the sequencing of many genomes has proven crucial for elucidating complex biological processes. Despite being invaluable tools, their limitations have led to a search for more versatile alternatives and, thus, to the use of small molecules. Chemical genetics is a rapidly emerging field that uses small-molecule techniques to probe biological systems and is composed of three parts: natural product or small-molecule libraries, phenotypic screening and target identification. Currently, the biggest hurdle in the overall process of chemical genetics is target identification. Efforts to overcome this obstacle have led to advances in the areas of affinity chromatography, yeast haploinsufficiency, complementary DNA (cDNA) overexpression, DNA microarray, small-molecule microarray and RNA interference (RNAi) technologies. While these technologies continue to undergo further optimization, they have been integral in the identification and/or confirmation of many cellular targets and have seen an increase in applications to the drug-development process.

Discovering the targets of drugs via computational systems biology

Computational systems biology is empowering the study of drug action. Studies on biological effects of chemical compounds have increased in scale and accessibility, allowing integration with other large-scale experimental data types. Here, we review computational approaches for elucidating the mechanisms of both intended and undesirable effects of drugs, with the collective potential to change the nature of drug discovery and pharmacological therapy.

Expression signatures of the lipid-based Akt inhibitors phosphatidylinositol ether lipid analogues in NSCLC cells

Activation of the serine/threonine kinase Akt contributes to the formation, maintenance, and therapeutic resistance of cancer, which is driving development of compounds that inhibit Akt. Phosphatidylinositol ether lipid analogues (PIA) are analogues of the products of phosphoinositide-3-kinase (PI3K) that inhibit Akt activation, translocation, and the proliferation of a broad spectrum of cancer cell types. To gain insight into the mechanism of PIAs, time-dependent transcriptional profiling of five active PIAs and the PI3K inhibitor LY294002 (LY) was conducted in non-small cell lung carcinoma cells using high-density oligonucleotide arrays. Gene ontology analysis revealed that genes involved in apoptosis, wounding response, and angiogenesis were upregulated by PIAs, whereas genes involved in DNA replication, repair, and mitosis were suppressed. Genes that exhibited early differential expression were partitioned into three groups; those induced by PIAs only (DUSP1, KLF6, CENTD2, BHLHB2, and PREX1), those commonly induced by PIAs and LY (TRIB1, KLF2, RHOB, and CDKN1A), and those commonly suppressed by PIAs and LY (IGFBP3, PCNA, PRIM1, MCM3, and HSPA1B). Increased expression of the tumor suppressors RHOB (RhoB), KLF6 (COPEB), and CDKN1A (p21Cip1/Waf1) was validated as an Akt-independent effect that contributed to PIA-induced cytotoxicity. Despite some overlap with LY, active PIAs have a distinct expression signature that contributes to their enhanced cytotoxicity.

Label-free DNA detection with a nanogap embedded complementary metal oxide semiconductor

A nanogap embedded complementary metal oxide semiconductor (NeCMOS) is demonstrated as a proof-of-concept for label-free detection of DNA sequence. When a partially carved nanogap between a gate and a silicon channel is filled with charged biomolecules, the gate dielectric constant and charges are changed. When the gate oxide thickness reduces, the threshold voltage is significantly affected by a change of the charges, whereas it is scarcely influenced by a change of the dielectric constant. In the case of DNA, those two factors act on the threshold voltage oppositely in an n-channel NeCMOS but collaboratively in a p-channel NeCMOS because of the negative charges of DNA. Hence, a p-channel NeCMOS with a thin gate oxide is more attractive for DNA detection because it enhances the shift of threshold voltage; that is, it improves the sensitivity of DNA detection. In addition, the shift of threshold voltage according to the nanogap length is also investigated and the longer nanogap shows more shift of the threshold voltage.

AtCAST, a tool for exploring gene expression similarities among DNA microarray experiments using networks

The comparison of gene expression profiles among DNA microarray experiments enables the identification of unknown relationships among experiments to uncover the underlying biological relationships. Despite the ongoing accumulation of data in public databases, detecting biological correlations among gene expression profiles from multiple laboratories on a large scale remains difficult. Here, we applied a module (sets of genes working in the same biological action)-based correlation analysis in combination with a network analysis to Arabidopsis data and developed a 'module-based correlation network' (MCN) which represents relationships among DNA microarray experiments on a large scale. We developed a Web-based data analysis tool, 'AtCAST' (Arabidopsis thaliana: DNA Microarray Correlation Analysis Tool), which enables browsing of an MCN or mining of users' microarray data by mapping the data into an MCN. AtCAST can help researchers to find novel connections among DNA microarray experiments, which in turn will help to build new hypotheses to uncover physiological mechanisms or gene functions in Arabidopsis.

Fibromodulin suppresses nuclear factor-kappaB activity by inducing the delayed degradation of IKBA via a JNK-dependent pathway coupled to fibroblast apoptosis

Fibulin-5 (FBLN5) belongs to the Fibulin family of secreted extracellular matrix proteins, and our laboratory first established FBLN5 as a novel target for TGF-β in fibroblasts and endothelial cells. To better understand the pathophysiology of FBLN5, we carried out microarray analysis to identify fibroblast genes whose expressions were regulated by FBLN5 and TGF-β. In doing so, we identified fibromodulin (Fmod) as a novel target gene of FBLN5, and we validated the differential expression of Fmod and 12 other FBLN5-regulated genes by semi-quantitative real time PCR. Fmod belongs to the small leucine-rich family of proteoglycans, which are important constituents of mammalian extracellular matrices. Interestingly, parental 3T3-L1 fibroblasts displayed high levels of nuclear factor-κB (NF-κB) activity, although those engineered to express Fmod constitutively exhibited significantly reduced NF-κB activity, suggesting that Fmod functions to inhibit NF-κB signaling. By monitoring alterations in the activation of NF-κB and the degradation of its inhibitor, IκBα, we demonstrate for the first time that Fmod contributes to the constitutive degradation of IκBα protein in 3T3-L1 fibroblasts. Mechanistically, we observed Fmod to delay the degradation of IκBα by promoting the following: (i) activation of c-Jun N-terminal kinase; (ii) inhibition of calpain and casein kinase 2 activity; and (iii) induction of fibroblast apoptosis. Taken together, our study identified a novel function for Fmod in directing extracellular signaling, particularly the regulation of NF-κB activity and cell survival.

A 4-gene signature predicts survival of patients with resected adenocarcinoma of the esophagus, junction, and gastric cardia

BACKGROUND & AIMS

The incidence of esophageal and junctional adenocarcinoma has increased 6-fold in the past 30 years and 5-year survival remains approximately 20%. Current staging is limited in its ability to predict survival which has ramifications for treatment choices. The aim of this study was to generate and validate a molecular prognostic signature for esophageal adenocarcinoma.

METHODS

Gene expression profiling was performed and the resulting 42,000 gene signatures correlated with clinical and pathologic features for 75 snap-frozen esophageal and junctional resection specimens. External validation of selected targets was performed on 371 independent cases using immunohistochemistry to maximize clinical applicability.

RESULTS

A total of 119 genes were associated significantly with survival and 270 genes with the number of involved lymph nodes. Filtering of these lists resulted in a shortlist of 10 genes taken forward to validation. Four genes proved to be prognostic at the protein level (deoxycytidine kinase [DCK], 3'-phosphoadenosine 5'-phosphosulfate synthase 2 [PAPSS2], sirtuin 2 [SIRT2], and tripartite motif-containing 44 [TRIM44]) and were combined to create a molecular prognostic signature. This 4-gene signature was highly predictive of survival in the independent external validation cohort (0/4 genes dysregulated 5-year survival, 58%; 95% confidence interval [CI], 36%-80%; 1-2/4 genes dysregulated 5-year survival, 26%; 95% CI, 20%-32%; and 3-4/4 genes dysregulated 5-year survival, 14%; 95% CI, 4%-24% (P = .001). Furthermore, this 4-gene signature was independently prognostic in a multivariable model together with the existing clinical TNM staging system (P = .013).

CONCLUSIONS

This study has generated a clinically applicable prognostic gene signature that independently predicts survival in an external validation cohort and may inform management decisions.

Toxicogenomics using yeast DNA microarrays

Development of genomics and bioinformatics enable us to analyze the global gene expression profiles of cells by DNA microarray. Changes in gene expression patterns indicate changes in its physiological conditions. Following the exposure of an organism or cell to toxic chemicals or other environmental stresses, the global genetic responses can be expeditiously and easily analyzed. Baker's yeast, Saccharomyces cerevisiae, is one of the most studied and useful model eukaryotes. The biggest advantage of yeast genomics is the available functional information for each gene and a considerable number of data are accumulating in the field of toxicity assessment using yeast DNA microarray. In this review, we discuss the toxicogenomics of metal ions, alcohols and aldehydes, and other chemicals.

Individualized monitoring of nuclear factor of activated T cells-regulated gene expression in FK506-treated kidney transplant recipients

BACKGROUND

The suggested key mechanism of both cyclosporine A (CsA) and FK506 is the inhibition of calcineurin phosphatase activity, preventing nuclear factor of activated T cells (NFAT)-translocation into the nucleus of T cells, with a subsequent transcriptional block of crucial cytokine genes. However, the two drugs exert different clinical activities as exemplified by the ability of FK506 to treat acute rejections. Inhibition of calcineurin activity by FK506 occurs in vitro at the same or even higher dose as for CsA; however, the magnitude of clinical and experimental immunosuppression is higher, indicating that FK506 may act in a calcineurin-independent way.

METHODS

To test this hypothesis, we measured the inhibition of NFAT-regulated gene expression in 262 stable kidney transplanted patients after FK506 intake.

RESULTS

Previously, we showed that the optimal degree of NFAT inhibition in patients treated with CsA is between 15% and 30% residual gene expression. A considerable number of patients treated with FK506 do not achieve this level of immunosuppression despite therapeutic drug concentrations. Importantly, FK506 does inhibit protein translation. This insufficient degree of NFAT inhibition was associated with a higher rate of biopsy-proven acute rejection but also with a lower incidence of recurrent infections. Conversion of CsA to FK506 causes immediately reduced inhibition of NFAT-regulated gene expression.

CONCLUSION

We could demonstrate that a considerable number of FK506-treated patients benefit from the drug, irrespective of the potency of NFAT inhibition in T cells by a yet unknown mechanism. Nevertheless, residual expression of NFAT-regulated genes seems to be a useful pharmacodynamic method to monitor FK506 therapy in renal transplant patients.

A survey of yeast genomic assays for drug and target discovery

Over the past decade, the development and application of chemical genomic assays using the model organism Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of known drugs and novel small molecules in vivo. These assays identify drug target candidates, genes involved in buffering drug target pathways and also help to define the general cellular response to small molecules. In this review, we examine current yeast chemical genomic assays and summarize the potential applications of each approach.

Terpestacin inhibits tumor angiogenesis by targeting UQCRB of mitochondrial complex III and suppressing hypoxia-induced reactive oxygen species production and cellular oxygen sensing

Cellular oxygen sensing is required for hypoxia-inducible factor-1alpha stabilization, which is important for tumor cell survival, proliferation, and angiogenesis. Here we find that terpestacin, a small molecule previously identified in a screen of microbial extracts, binds to the 13.4-kDa subunit (UQCRB) of mitochondrial Complex III, resulting in inhibition of hypoxia-induced reactive oxygen species generation. Consequently, such inhibition blocks hypoxia-inducible factor activation and tumor angiogenesis in vivo, without inhibiting mitochondrial respiration. Overexpression of UQCRB or its suppression using RNA interference demonstrates that it plays a crucial role in the oxygen sensing mechanism that regulates responses to hypoxia. These findings provide a novel molecular basis of terpestacin targeting UQCRB of Complex III in selective suppression of tumor progression.

Real-time PCR methods to study expression of genes related to hypermutability

Pathogenic bacteria can have sub-populations of hypermutable bacteria. This sub-population has a higher spontaneous mutation rate than the majority of the population which can be attributed to defects in proofreading and repair mechanisms. This leads to the evolution of drug-resistant strains of bacteria through genetic change. It is important to study the expression of genes involved in, for example, mismatch repair and the SOS system by real-time PCR to determine hypermutability and therefore provide an indicator of the mutagenic ability of certain strains of pathogenic bacteria.

Probing differentially expressed genes against a microarray database for in silico suppressor/enhancer and inhibitor/activator screens

High-density oligonucleotide arrays are widely used for analysis of gene expression on a genomic scale, but the generated data remain largely inaccessible for comparative analysis purposes. Similarity searches in databases with differentially expressed gene (DEG) lists may be used to assign potential functions to new genes and to identify potential chemical inhibitors/activators and genetic suppressors/enhancers. Although this is a very promising concept, it requires the compatibility and validity of the DEG lists to be significantly improved. Using Arabidopsis and human datasets, we have developed guidelines for the performance of similarity searches against databases that collect microarray data. We found that, in comparison with many other methods, a rank-product analysis achieves a higher degree of inter- and intra-laboratory consistency of DEG lists, and is advantageous for assessing similarities and differences between them. To support this concept, we developed a tool called MASTA (microarray overlap search tool and analysis), and re-analyzed over 600 Arabidopsis microarray expression datasets. This revealed that large-scale searches produce reliable intersections between DEG lists that prove to be useful for genetic analysis, thus aiding in the characterization of cellular and molecular mechanisms. We show that this approach can be used to discover unexpected connections and to illuminate unanticipated interactions between individual genes.

Drug target identification, validation, characterisation and exploitation for treatment of Acanthamoeba (species) infections

New more efficacious antimicrobials as required for the treatment of Acanthamoeba infections as those currently available require arduous treatment regimes, are not always effective and are poorly active against the cystic stages. Herein, we review potential drug targets including tubulin, alternative oxidase, amino acid biosynthesis and myosin. In addition, we review the literature for current missing tools and resources for the identification, validation and development of new antimicrobials for this organism. Additional targets should come to light through a concerted genome sequencing effort.

De novo discovery of a gamma-secretase inhibitor response signature using a novel in vivo breast tumor model

Notch pathway signaling plays a fundamental role in normal biological processes and is frequently deregulated in many cancers. Although several hypotheses regarding cancer subpopulations most likely to respond to therapies targeting the Notch pathway have been proposed, clinical utility of these predictive markers has not been shown. To understand the molecular basis of gamma-secretase inhibitor (GSI) sensitivity in breast cancer, we undertook an unbiased, de novo responder identification study using a novel genetically engineered in vivo breast cancer model. We show that tumors arising from this model are heterogeneous on the levels of gene expression, histopathology, growth rate, expression of Notch pathway markers, and response to GSI treatment. In addition, GSI treatment of this model was associated with inhibition of Hes1 and proliferation markers, indicating that GSI treatment inhibits Notch signaling. We then identified a pretreatment gene expression signature comprising 768 genes that is significantly associated with in vivo GSI efficacy across 99 tumor lines. Pathway analysis showed that the GSI responder signature is enriched for Notch pathway components and inflammation/immune-related genes. These data show the power of this novel in vivo model system for the discovery of biomarkers predictive of response to targeted therapies, and provide a basis for the identification of human breast cancers most likely to be sensitive to GSI treatment.

Kinetic cell-based morphological screening: prediction of mechanism of compound action and off-target effects

We describe a cell-based kinetic profiling approach using impedance readout for monitoring the effect of small molecule compounds. This noninvasive readout allows continuous sampling of cellular responses to biologically active compounds and the ensuing kinetic profile provides information regarding the temporal interaction of compounds with cells. The utility of this approach was tested by screening a library containing FDA approved drugs, experimental compounds, and nature compounds. Compounds with similar activity produced similar impedance-based time-dependent cell response profiles (TCRPs). The compounds were clustered based on TCRP similarity. We identified novel mechanisms for existing drugs, confirmed previously reported calcium modulating activity for COX-2 inhibitor celecoxib, and identified an additional mechanism for the experimental compound monastrol. We also identified and characterized a new antimitotic agent. Our findings indicate that the TCRP approach provides predictive mechanistic information for small molecule compounds.

The molecular characterization and clinical management of multiple myeloma in the post-genome era

Cancer-causing mutations disrupt coordinated, precise programs of gene expression that govern cell growth and differentiation. Microarray-based gene-expression profiling (GEP) is a powerful tool to globally analyze these changes to study cancer biology and clinical behavior. Despite overwhelming genomic chaos in multiple myeloma (MM), expression patterns within tumor samples are remarkably stable and reproducible. Unique expression patterns associated with recurrent chromosomal translocations and ploidy changes defined molecular classes with differing clinical features and outcomes. Combined molecular techniques also dissected two distinct, reproducible forms of hyperdiploid disease and have molecularly defined MM with high risk for poor clinical outcome. GEP is now used to risk-stratify patients with newly diagnosed MM. Groups with high-risk features are evident in all GEP-defined MM classes, and GEP studies of serial samples showed that risk increases over time, with relapsed disease showing dramatic GEP shifts toward a signature of poor outcomes. This suggests a common mechanism of disease evolution and potentially reflects preferential expansion of therapy-resistant cells. Correlating GEP-defined disease class and risk with outcomes of therapeutic regimens reveals class-specific benefits for individual agents, as well as mechanistic insights into drug sensitivity and resistance. Here, we review modern genomics contributions to understanding MM pathogenesis, prognosis, and therapy.

HORMONOMETER: a tool for discerning transcript signatures of hormone action in the Arabidopsis transcriptome

Plant hormones regulate growth and responses to environmental change. Hormone action ultimately modifies cellular physiological processes and gene activity. To facilitate transcriptome evaluation of novel mutants and environmental responses, there is a need to rapidly assess the possible contribution of hormone action to changes in the levels of gene transcripts. We developed a vector-based algorithm that rapidly compares lists of transcripts yielding correlation values. The application as described here, called HORMONOMETER, was used to analyze hormone-related activity in a transcriptome of Arabidopsis (Arabidopsis thaliana). The veracity of the resultant analysis was established by comparison with cognate and noncognate hormone transcriptomes as well as with mutants and selected plant-environment interactions. The HORMONOMETER accurately predicted correlations between hormone action and biosynthetic mutants for which transcriptome data are available. A high degree of correlation was detected between many hormones, particularly at early time points of hormone action. Unforeseen complexity was detected in the analysis of mutants and in plant-herbivore interactions. The HORMONOMETER provides a diagnostic tool for evaluating the physiological state of being of the plant from the point of view of transcripts regulated by hormones and yields biological insight into the multiple response components that enable plant adaptation to the environment. A Web-based interface has been developed to facilitate external interfacing with this platform.

Development of a microarray platform for FFPET profiling: application to the classification of human tumors

Background

mRNA profiling has become an important tool for developing and validating prognostic assays predictive of disease treatment response and outcome. Archives of annotated formalin-fixed paraffin-embedded tissues (FFPET) are available as a potential source for retrospective studies. Methods are needed to profile these FFPET samples that are linked to clinical outcomes to generate hypotheses that could lead to classifiers for clinical applications.

Methods

We developed a two-color microarray-based profiling platform by optimizing target amplification, experimental design, quality control, and microarray content and applied it to the profiling of FFPET samples. We profiled a set of 50 fresh frozen (FF) breast cancer samples and assigned class labels according to the signature and method by van 't Veer et al [1] and then profiled 50 matched FFPET samples to test how well the FFPET data predicted the class labels. We also compared the sorting power of classifiers derived from FFPET sample data with classifiers derived from data from matched FF samples.

Results

When a classifier developed with matched FF samples was applied to FFPET data to assign samples to either "good" or "poor" outcome class labels, the classifier was able to assign the FFPET samples to the correct class label with an average error rate = 12% to 16%, respectively, with an Odds Ratio = 36.4 to 60.4, respectively. A classifier derived from FFPET data was able to predict the class label in FFPET samples (leave-one-out cross validation) with an error rate of ~14% (p-value = 3.7 × 10^-7). When applied to the matched FF samples, the FFPET-derived classifier was able to assign FF samples to the correct class labels with 96% accuracy. The single misclassification was attributed to poor sample quality, as measured by qPCR on total RNA, which emphasizes the need for sample quality control before profiling.

Conclusion

We have optimized a platform for expression analyses and have shown that our profiling platform is able to accurately sort FFPET samples into class labels derived from FF classifiers. Furthermore, using this platform, a classifier derived from FFPET samples can reliably provide the same sorting power as a classifier derived from matched FF samples. We anticipate that these techniques could be used to generate hypotheses from archives of FFPET samples, and thus may lead to prognostic and predictive classifiers that could be used, for example, to segregate patients for clinical trial enrollment or to guide patient treatment.

Macromolecular scaffolds for immobilizing small molecule microarrays in label-free detection of protein-ligand interactions on solid support

We explored two macromolecular scaffolds, bovine serum albumin (BSA) and polyvinyl alcohol (PVA), as chemically complementary platforms for immobilizing small molecule compounds on functionalized glass slides. We conjugated biotin molecules to BSA and amine-derivatized PVA and subsequently immobilized the conjugates on epoxy-functionalized glass slides through reaction of free amine residues on BSA and PVA with surface-bound epoxy groups. We studied binding reactions of such immobilized small molecule targets with solution-phase protein probes using an oblique-incidence reflectivity difference scanning optical microscope. The results showed that both BSA and amine-derivatized PVA were effective and efficient as carriers of small molecules with NHS residues and fluoric residues and for immobilization on epoxy-coated solid surfaces. A significant fraction of the conjugated small molecules retain their innate chemical activity.

Analysis of differential gene expression in colorectal cancer and stroma using fluorescence-activated cell sorting purification

Tumour stroma gene expression in biopsy specimens may obscure the expression of tumour parenchyma, hampering the predictive power of microarrays. We aimed to assess the utility of fluorescence-activated cell sorting (FACS) for generating cell populations for gene expression analysis and to compare the gene expression of FACS-purified tumour parenchyma to that of whole tumour biopsies. Single cell suspensions were generated from colorectal tumour biopsies and tumour parenchyma was separated using FACS. Fluorescence-activated cell sorting allowed reliable estimation and purification of cell populations, generating parenchymal purity above 90%. RNA from FACS-purified and corresponding whole tumour biopsies was hybridised to Affymetrix oligonucleotide microarrays. Whole tumour and parenchymal samples demonstrated differential gene expression, with 289 genes significantly overexpressed in the whole tumour, many of which were consistent with stromal gene expression (e.g., COL6A3, COL1A2, POSTN, TIMP2). Genes characteristic of colorectal carcinoma were overexpressed in the FACS-purified cells (e.g., HOX2D and RHOB). We found FACS to be a robust method for generating samples for gene expression analysis, allowing simultaneous assessment of parenchymal and stromal compartments. Gross stromal contamination may affect the interpretation of cancer gene expression microarray experiments, with implications for hypotheses generation and the stability of expression signatures used for predicting clinical outcomes.

Uncovering multiple molecular targets for caffeine using a drug target validation strategy combining A 2A receptor knockout mice with microarray profiling

Caffeine is the most widely consumed psychoactive substance and has complex pharmacological actions in brain. In this study, we employed a novel drug target validation strategy to uncover the multiple molecular targets of caffeine using combined A(2A) receptor (A(2A)R) knockouts (KO) and microarray profiling. Caffeine (10 mg/kg) elicited a distinct profile of striatal gene expression in WT mice compared with that by A(2A)R gene deletion or by administering caffeine into A(2A)R KO mice. Thus, A(2A)Rs are required but not sufficient to elicit the striatal gene expression by caffeine (10 mg/kg). Caffeine (50 mg/kg) induced complex expression patterns with three distinct sets of striatal genes: 1) one subset overlapped with those elicited by genetic deletion of A(2A)Rs; 2) the second subset elicited by caffeine in WT as well as A(2A)R KO mice; and 3) the third subset elicited by caffeine only in A(2A)R KO mice. Furthermore, striatal gene sets elicited by the phosphodiesterase (PDE) inhibitor rolipram and the GABA(A) receptor antagonist bicucullin, overlapped with the distinct subsets of striatal genes elicited by caffeine (50 mg/kg) administered to A(2A)R KO mice. Finally, Gene Set Enrichment Analysis reveals that adipocyte differentiation/insulin signaling is highly enriched in the striatal gene sets elicited by both low and high doses of caffeine. The identification of these distinct striatal gene populations and their corresponding multiple molecular targets, including A(2A)R, non-A(2A)R (possibly A(1)Rs and pathways associated with PDE and GABA(A)R) and their interactions, and the cellular pathways affected by low and high doses of caffeine, provides molecular insights into the acute pharmacological effects of caffeine in the brain.

Biomarker discovery: identification of a growth factor gene signature

Gene expression signatures can be developed as comprehensive pathway readouts and used as pharmacodynamic or patient-stratification biomarkers. While a consensus on the best practices for selecting gene expression signatures from microarray data is evolving, we have developed basic guidelines to ensure consistency and quality. Here we illustrate these guidelines through the identification of a growth factor gene expression signature that is responsive to phosphatidylinositol 3-kinase (PI3K) pathway perturbations in vitro and related to phosphatase and tensin homolog (PTEN) deregulation in vivo.

Phase II trial of vorinostat in recurrent glioblastoma multiforme: a north central cancer treatment group study

PURPOSE

Vorinostat, a histone deacetylase inhibitor, represents a rational therapeutic target in glioblastoma multiforme (GBM).

PATIENTS AND METHODS

Patients with recurrent GBM who had received one or fewer chemotherapy regimens for progressive disease were eligible. Vorinostat was administered at a dose of 200 mg orally twice a day for 14 days, followed by a 7-day rest period.

RESULTS

A total of 66 patients were treated. Grade 3 or worse nonhematologic toxicity occurred in 26% of patients and consisted mainly of fatigue (17%), dehydration (6%), and hypernatremia (5%); grade 3 or worse hematologic toxicity occurred in 26% of patients and consisted mainly of thrombocytopenia (22%). Pharmacokinetic analysis showed lower vorinostat maximum concentration and area under the curve (0 to 24 hours) values in patients treated with enzyme-inducing anticonvulsants, although this did not reach statistical significance. The trial met the prospectively defined primary efficacy end point, with nine of the first 52 patients being progression-free at 6 months. Median overall survival from study entry was 5.7 months (range, 0.7 to 28+ months). Immunohistochemical analysis performed in paired baseline and post-vorinostat treatment samples in a separate surgical subgroup of five patients with recurrent GBM showed post treatment increase in acetylation of histones H2B and H4 (four of five patients) and of histone H3 (three of five patients). Microarray RNA analysis in the same samples showed changes in genes regulated by vorinostat, such as upregulation of E-cadherin (P = .02).

CONCLUSION

Vorinostat monotherapy is well tolerated in patients with recurrent GBM and has modest single-agent activity. Histone acetylation analysis and RNA expression profiling indicate that vorinostat in this dose and schedule affects target pathways in GBM. Additional testing of vorinostat in combination regimens is warranted.

Factors influencing the statistical power of complex data analysis protocols for molecular signature development from microarray data

BACKGROUND

Critical to the development of molecular signatures from microarray and other high-throughput data is testing the statistical significance of the produced signature in order to ensure its statistical reproducibility. While current best practices emphasize sufficiently powered univariate tests of differential expression, little is known about the factors that affect the statistical power of complex multivariate analysis protocols for high-dimensional molecular signature development.

METHODOLOGY/PRINCIPAL FINDINGS

We show that choices of specific components of the analysis (i.e., error metric, classifier, error estimator and event balancing) have large and compounding effects on statistical power. The effects are demonstrated empirically by an analysis of 7 of the largest microarray cancer outcome prediction datasets and supplementary simulations, and by contrasting them to prior analyses of the same data.

CONCLUSIONS/SIGNIFICANCE

THE FINDINGS OF THE PRESENT STUDY HAVE TWO IMPORTANT PRACTICAL IMPLICATIONS: First, high-throughput studies by avoiding under-powered data analysis protocols, can achieve substantial economies in sample required to demonstrate statistical significance of predictive signal. Factors that affect power are identified and studied. Much less sample than previously thought may be sufficient for exploratory studies as long as these factors are taken into consideration when designing and executing the analysis. Second, previous highly-cited claims that microarray assays may not be able to predict disease outcomes better than chance are shown by our experiments to be due to under-powered data analysis combined with inappropriate statistical tests.

Drug discovery in psychiatric illness: mining for gold

The discovery of truly efficacious treatments that lead to full recovery is a daunting task in psychiatric illness. A systems-based orientation to in vivo pharmacology has been suggested as a way to transform psychiatric drug discovery and development. A critical catalyst in the success of recent systems biology efforts has been the incorporation of data mining strategies. Our approach to the drug discovery problem has been to utilize the whole animal to provide a systems response that is subsequently mined for predictive attributes with known psychopharmacological value. Our in vivo data mining approach, termed Pattern Array, establishes a framework for screening novel chemical entities based upon a response that represents the net pharmacological effect on the system of interest, namely the central nervous system (CNS). Large scale screening of small molecules by non-conventional approaches such as this at a systems level may improve the identification of novel chemical entities with psychiatric utility. This type of approach will compliment the more labor-intensive models based upon construct validity. It will take the collective effort of many disciplines and numerous strategies in close association with clinical colleagues to address quality of life issues and breakthrough treatment barriers in psychiatric illness.

Diurnal variation of the human adipose transcriptome and the link to metabolic disease

Background

Circadian (diurnal) rhythm is an integral part of the physiology of the body; specifically, sleep, feeding behavior and metabolism are tightly linked to the light-dark cycle dictated by earth's rotation.

Methods

The present study examines the effect of diurnal rhythm on gene expression in the subcutaneous adipose tissue of overweight to mildly obese, healthy individuals. In this well-controlled clinical study, adipose biopsies were taken in the morning, afternoon and evening from individuals in three study arms: treatment with the weight loss drug sibutramine/fasted, placebo/fed and placebo/fasted.

Results

The results indicated that diurnal rhythm was the most significant driver of gene expression variation in the human adipose tissue, with at least 25% of the genes having had significant changes in their expression levels during the course of the day. The mRNA expression levels of core clock genes at a specific time of day were consistent across multiple subjects on different days in all three arms, indicating robust diurnal regulation irrespective of potential confounding factors. The genes essential for energy metabolism and tissue physiology were part of the diurnal signature. We hypothesize that the diurnal transition of the expression of energy metabolism genes reflects the shift in the adipose tissue from an energy-expending state in the morning to an energy-storing state in the evening. Consistent with this hypothesis, the diurnal transition was delayed by fasting and treatment with sibutramine. Finally, an in silico comparison of the diurnal signature with data from the publicly-available Connectivity Map demonstrated a significant association with transcripts that were repressed by mTOR inhibitors, suggesting a possible link between mTOR signaling, diurnal gene expression and metabolic regulation.

Conclusion

Diurnal rhythm plays an important role in the physiology and regulation of energy metabolism in the adipose tissue and should be considered in the selection of novel targets for the treatment of obesity and other metabolic disorders.

Analysis of endocrine disruption in Southern California coastal fish using an aquatic multispecies microarray

BACKGROUND

Endocrine disruptors include plasticizers, pesticides, detergents, and pharmaceuticals. Turbot and other flatfish are used to characterize the presence of chemicals in the marine environment. Unfortunately, there are relatively few genes of turbot and other flatfish in GenBank, which limits the use of molecular tools such as microarrays and quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) to study disruption of endocrine responses in sentinel fish captured by regulatory agencies.

OBJECTIVES

We fabricated a multigene cross-species microarray as a diagnostic tool to screen the effects of environmental chemicals in fish, for which there is minimal genomic information. The array included genes that are involved in the actions of adrenal and sex steroids, thyroid hormone, and xenobiotic responses. This microarray will provide a sensitive tool for screening for the presence of chemicals with adverse effects on endocrine responses in coastal fish species.

METHODS

We used a custom multispecies microarray to study gene expression in wild hornyhead turbot (Pleuronichthys verticalis) collected from polluted and clean coastal waters and in laboratory male zebrafish (Danio rerio) after exposure to estradiol and 4-nonylphenol. We measured gene-specific expression in turbot liver by qRT-PCR and correlated it to microarray data.

RESULTS

Microarray and qRT-PCR analyses of livers from turbot collected from polluted areas revealed altered gene expression profiles compared with those from nonaffected areas.

CONCLUSIONS

The agreement between the array data and qRT-PCR analyses validates this multispecies microarray. The microarray measurement of gene expression in zebrafish, which are phylogenetically distant from turbot, indicates that this multispecies microarray will be useful for measuring endocrine responses in other fish.

DrugBank and its relevance to pharmacogenomics

DrugBank is a freely available web-enabled database that combines detailed drug data with comprehensive drug-target and drug-action information. It was specifically designed to facilitate in silico drug-target discovery, drug design, drug-metabolism prediction, drug-interaction prediction, and general pharmaceutical education. One of the most unique and useful components of the DrugBank database is the information it contains on drug metabolism, drug-metabolizing enzymes and drug-target polymorphisms. As pharmacogenomics is fundamentally concerned with the role of genes and genetic variation of how an individual responds to a drug, DrugBank is able to offer a convenient venue to explore pharmacogenomic questions in silico. This paper provides a brief overview on DrugBank and how it can facilitate pharmacogenomic research.

Cyclosporin A and Tacrolimus (FK506) Differentially Alter T-cell Receptor ExpressionIn Vivo

Cyclosporin A (CSA) and tacrolimus (FK506) are two common immunosuppressive agents used post blood and marrow transplantation. Despite similarity in their accepted modes of action, we observed polarized effects of CSA and FK506 on the in vivo human T cell repertoire. To determine the possible mechanism for this difference, the effects of CSA and FK506 on cell viability, cell proliferation, interleukin-2 production, and calcineurin inhibition were determined in vitro. Our data suggest that a secondary mechanism of action exists for the different T-cell repertoire induced by exposure to CSA and FK506.

Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia

MOTIVATION

DNA microarrays are routinely applied to study diseased or drug-treated cell populations. A critical challenge is distinguishing the genes directly affected by these perturbations from the hundreds of genes that are indirectly affected. Here, we developed a sparse simultaneous equation model (SSEM) of mRNA expression data and applied Lasso regression to estimate the model parameters, thus constructing a network model of gene interaction effects. This inferred network model was then used to filter data from a given experimental condition of interest and predict the genes directly targeted by that perturbation.

RESULTS

Our proposed SSEM-Lasso method demonstrated substantial improvement in sensitivity compared with other tested methods for predicting the targets of perturbations in both simulated datasets and microarray compendia. In simulated data, for two different network types, and over a wide range of signal-to-noise ratios, our algorithm demonstrated a 167% increase in sensitivity on average for the top 100 ranked genes, compared with the next best method. Our method also performed well in identifying targets of genetic perturbations in microarray compendia, with up to a 24% improvement in sensitivity on average for the top 100 ranked genes. The overall performance of our network-filtering method shows promise for identifying the direct targets of genetic dysregulation in cancer and disease from expression profiles.

AVAILABILITY

Microarray data are available at the Many Microbe Microarrays Database (M3D, http://m3d.bu.edu). Algorithm scripts are available at the Gardner Lab website (http://gardnerlab.bu.edu/SSEMLasso).

Development and application of a microarray meter tool to optimize microarray experiments

Background

Successful microarray experimentation requires a complex interplay between the slide chemistry, the printing pins, the nucleic acid probes and targets, and the hybridization milieu. Optimization of these parameters and a careful evaluation of emerging slide chemistries are a prerequisite to any large scale array fabrication effort. We have developed a 'microarray meter' tool which assesses the inherent variations associated with microarray measurement prior to embarking on large scale projects.

Findings

The microarray meter consists of nucleic acid targets (reference and dynamic range control) and probe components. Different plate designs containing identical probe material were formulated to accommodate different robotic and pin designs. We examined the variability in probe quality and quantity (as judged by the amount of DNA printed and remaining post-hybridization) using three robots equipped with capillary printing pins.

Discussion

The generation of microarray data with minimal variation requires consistent quality control of the (DNA microarray) manufacturing and experimental processes. Spot reproducibility is a measure primarily of the variations associated with printing. The microarray meter assesses array quality by measuring the DNA content for every feature. It provides a post-hybridization analysis of array quality by scoring probe performance using three metrics, a) a measure of variability in the signal intensities, b) a measure of the signal dynamic range and c) a measure of variability of the spot morphologies.

The incredible shrinking world of DNA microarrays

The efficacy of microarrays in examining gene expression, gene and genome structure, protein-DNA interactions, whole-genome similarities and differences, microRNA expression, methylation (and more) is no longer in question. It is a fast-developing, cutting edge technology that has grown up along with massive sequence databases and is likely to become part of everyday patient care. Many advances have recently expanded the power and utility of microarrays; among them is our development of a new array tiling technique that dramatically increases the scope of coverage of an oligonucleotide tiling array without substantially increasing its cost.

Applications of microarrays and biochips in pharmacogenomics

The complete sequence of the human genome and subsequent intensive searches for polymorphic variations are providing the prerequisite markers necessary to facilitate elucidation of the genetic variability in drug responses. Improvements in the sensitivity and precision of DNA microarrays permit a detailed and accurate scrutiny of the human genome. These advances have the potential to significantly improve health care management by improving disease diagnosis and targeting molecular therapy. Pharmacogenetic approaches, in limited use today, will become an integral part of therapeutic monitoring and health management, permitting patient stratification in advance of treatments, with the potential to eliminate adverse drug reactions. In this chapter, the current state of biochip technology is discussed, and recent applications in the arena of clinic diagnostics are explored.