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.     

Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks

A major challenge in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets. Here, we present an integrated computational-experimental approach for computing the likelihood that gene products and associated pathways are targets of a compound. This is achieved by filtering the mRNA expression profile of compound-exposed cells using a reverse-engineered model of the cell's gene regulatory network. We apply the method to a set of 515 whole-genome yeast expression profiles resulting from a variety of treatments (compounds, knockouts and induced expression), and correctly enrich for the known targets and associated pathways in the majority of compounds examined. We demonstrate our approach with PTSB, a growth inhibitory compound with a previously unknown mode of action, by predicting and validating thioredoxin and thioredoxin reductase as its target.     

Functional gene expression profiling in yeast implicates translational dysfunction in mutant huntingtin toxicity

Huntington disease (HD) is a neurodegenerative disorder caused by the expansion of a polyglutamine tract in the huntingtin (htt) protein. To uncover candidate therapeutic targets and networks involved in pathogenesis, we integrated gene expression profiling and functional genetic screening to identify genes critical for mutant htt toxicity in yeast. Using mRNA profiling, we have identified genes differentially expressed in wild-type yeast in response to mutant htt toxicity as well as in three toxicity suppressor strains: bna4Δ, mbf1Δ, and ume1Δ. BNA4 encodes the yeast homolog of kynurenine 3-monooxygenase, a promising drug target for HD. Intriguingly, despite playing diverse cellular roles, these three suppressors share common differentially expressed genes involved in stress response, translation elongation, and mitochondrial transport. We then systematically tested the ability of the differentially expressed genes to suppress mutant htt toxicity when overexpressed and have thereby identified 12 novel suppressors, including genes that play a role in stress response, Golgi to endosome transport, and rRNA processing. Integrating the mRNA profiling data and the genetic screening data, we have generated a robust network that shows enrichment in genes involved in rRNA processing and ribosome biogenesis. Strikingly, these observations implicate dysfunction of translation in the pathology of HD. Recent work has shown that regulation of translation is critical for life span extension in Drosophila and that manipulation of this process is protective in Parkinson disease models. In total, these observations suggest that pharmacological manipulation of translation may have therapeutic value in HD.     

Linking yeast Gcn5p catalytic function and gene regulation using a quantitative, graded dominant mutant approach

Establishing causative links between protein functional domains and global gene regulation is critical for advancements in genetics, biotechnology, disease treatment, and systems biology. This task is challenging for multifunctional proteins when relying on traditional approaches such as gene deletions since they remove all domains simultaneously. Here, we describe a novel approach to extract quantitative, causative links by modulating the expression of a dominant mutant allele to create a function-specific competitive inhibition. Using the yeast histone acetyltransferase Gcn5p as a case study, we demonstrate the utility of this approach and (1) find evidence that Gcn5p is more involved in cell-wide gene repression, instead of the accepted gene activation associated with HATs, (2) identify previously unknown gene targets and interactions for Gcn5p-based acetylation, (3) quantify the strength of some Gcn5p-DNA associations, (4) demonstrate that this approach can be used to correctly identify canonical chromatin modifications, (5) establish the role of acetyltransferase activity on synthetic lethal interactions, and (6) identify new functional classes of genes regulated by Gcn5p acetyltransferase activity--all six of these major conclusions were unattainable by using standard gene knockout studies alone. We recommend that a graded dominant mutant approach be utilized in conjunction with a traditional knockout to study multifunctional proteins and generate higher-resolution data that more accurately probes protein domain function and influence.     

PKA and Sch9 control a molecular switch important for the proper adaptation to nutrient availability

In the yeast Saccharomyces cerevisiae, PKA and Sch9 exert similar physiological roles in response to nutrient availability. However, their functional redundancy complicates to distinguish properly the target genes for both kinases. In this article, we analysed different phenotypic read-outs. The data unequivocally showed that both kinases act through separate signalling cascades. In addition, genome-wide expression analysis under conditions and with strains in which either PKA and/or Sch9 signalling was specifically affected, demonstrated that both kinases synergistically or oppositely regulate given gene targets. Unlike PKA, which negatively regulates stress-responsive element (STRE)- and post-diauxic shift (PDS)-driven gene expression, Sch9 appears to exert additional positive control on the Rim15-effector Gis1 to regulate PDS-driven gene expression. The data presented are consistent with a cyclic AMP (cAMP)-gating phenomenon recognized in higher eukaryotes consisting of a main gatekeeper, the protein kinase PKA, switching on or off the activities and signals transmitted through primary pathways such as, in case of yeast, the Sch9-controlled signalling route. This mechanism allows fine-tuning various nutritional responses in yeast cells, allowing them to adapt metabolism and growth appropriately.     

NOTUNG: a program for dating gene duplications and optimizing gene family trees.

Large scale gene duplication is a major force driving the evolution of genetic functional innovation. Whole genome duplications are widely believed to have played an important role in the evolution of the maize, yeast, and vertebrate genomes. The use of evolutionary trees to analyze the history of gene duplication and estimate duplication times provides a powerful tool for studying this process. Many studies in the molecular evolution literature have used this approach on small data sets, using analyses performed by hand. The rapid growth of genetic sequence data will soon allow similar studies on a genomic scale, but such studies will be limited unless the analysis can be automated. Even existing data sets admit alternative hypotheses that would be too tedious to consider without automation. In this paper, we describe a program called NOTUNG that facilitates large scale analysis, using both rooted and unrooted trees. When tested on trees analyzed in the literature, NOTUNG consistently yielded results that agree with the assessments in the original publications. Thus, NOTUNG provides a basic building block for inferring duplication dates from gene trees automatically and can also be used as an exploratory analysis tool for evaluating alternative hypotheses.     

Bayesian inference of MicroRNA targets from sequence and expression data.

MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although much work has been done in the genome-wide computational prediction of miRNA genes and their target mRNAs, an open question is how to efficiently obtain functional miRNA targets from a large number of candidate miRNA targets predicted by existing computational algorithms. In this paper, we propose a novel Bayesian model and learning algorithm, GenMiR++ (Generative model for miRNA regulation), that accounts for patterns of gene expression using miRNA expression data and a set of candidate miRNA targets. A set of high-confidence functional miRNA targets are then obtained from the data using a Bayesian learning algorithm. Our model scores 467 high-confidence miRNA targets out of 1,770 targets obtained from TargetScanS in mouse at a false detection rate of 2.5%: several confirmed miRNA targets appear in our high-confidence set, such as the interactions between miR-92 and the signal transduction gene MAP2K4, as well as the relationship between miR-16 and BCL2, an anti-apoptotic gene which has been implicated in chronic lymphocytic leukemia. We present results on the robustness of our model showing that our learning algorithm is not sensitive to various perturbations of the data. Our high-confidence targets represent a significant increase in the number of miRNA targets and represent a starting point for a global understanding of gene regulation.     

Yeast as a tool to uncover the cellular targets of drugs

Knowledge of the spectrum of cellular proteins targeted by experimental therapeutic agents would greatly facilitate drug development. However, identifying the targets of drugs is a daunting challenge. The yeast Saccharomyces cerevisiae is a valuable model organism for human diseases and pathways because it is genetically tractable and shares many functional homolog with humans. In yeast, it is possible to increase or decrease the expression level of essentially every gene and measure changes in drug sensitivity to uncover potential targets. It is also possible to infer mechanism of action from comparing the changes in mRNA expression elicited by drug treatment with those induced by gene deletions or by other drugs. Proteins that bind drugs directly can be identified using yeast protein chips. This review of the use of yeast for discovering targets of drugs discusses the advantages and drawbacks of each approach and how combining methods may reveal targets more efficiently.     

Acquisition of a potential marker for insect transformation: isolation of a novel alcohol dehydrogenase gene from Bactrocera oleae by functional complementation in yeast

The alcohol dehydrogenase genes make up one of the best studied gene families in Drosophila, both in terms of expression and evolution. Moreover, alcohol dehydrogenase genes constitute potential versatile markers in insect transformation experiments. However, due to their rapid evolution, these genes cannot be cloned from other insect genera by DNA hybridization or PCR-based strategies. We have therefore explored an alternative strategy: cloning by functional complementation of appropriate yeast mutants. Here we report that two alcohol dehydrogenase genes from the medfly Ceratitis capitata can functionally replace the yeast enzymes, even though the medfly and yeast genes have evolved independently, acquiring their enzymatic function convergently. Using this method, we have cloned an alcohol dehydrogenase gene from the olive pest Bactrocera oleae. We conclude that functional complementation in yeast can be used to clone alcohol dehydrogenase genes that are unrelated in sequence to those of yeast, thus providing a powerful tool for isolation of dominant insect transformation marker genes. Received: 29 June 1999 / Accepted: 27 October 1999     

Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae

As we are moving into the post genome-sequencing era, various high-throughput experimental techniques have been developed to characterize biological systems on the genomic scale. Discovering new biological knowledge from the high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a Bayesian statistical method together with Boltzmann machine and simulated annealing for protein functional annotation in the yeast Saccharomyces cerevisiae through integrating various high-throughput biological data, including yeast two-hybrid data, protein complexes and microarray gene expression profiles. In our approach, we quantified the relationship between functional similarity and high-throughput data, and coded the relationship into ‘functional linkage graph’, where each node represents one protein and the weight of each edge is characterized by the Bayesian probability of function similarity between two proteins. We also integrated the evolution information and protein subcellular localization information into the prediction. Based on our method, 1802 out of 2280 unannotated proteins in yeast were assigned functions systematically.     

The anti-mitotic drug griseofulvin induces apoptosis of human germ cell tumor cells through a connexin 43-dependent molecular mechanism

Griseofulvin, a widely used antifungal antimitotic drug has been proposed as an anti-tumoral treatment by way of in vitro experiments. Recently, in vivo demonstration of griseofulvin efficacy against multiple myeloma in mice argues for its potential as therapeutics for cancer. Nevertheless, the molecular mechanisms by which griseofulvin disrupts cancerous cell progression are far from being understood. In the present study, we found that griseofulvin inhibits human germ cell tumor cell growth through activation of mitochondrial caspase pathway (caspase 9 and 3) leading to the activation of apoptosis rather than an alteration of cell proliferation. Strikingly, we demonstrated that griseofulvin triggered the expression level of connexin 43 (mRNA and protein), a well described tumor-suppressor gene, known to participate in apoptosis regulation. Consistently, together with microtubule instability, a mechanism classically associated with cell death in response to griseofulvin, we observed a disruption of connexin 43/tubulin association concomitant of an enhanced translocation of connexin 43, or an immunoreactive fragment of the protein, from the cytoplasm to the nucleus. Finally, by using siRNA approaches we demonstrated the requirement of connexin 43 in the apoptotic induction of griseofulvin on our tumor cell model. Altogether, these results described a new molecular mechanism connexin 43-dependent targeted by griseofulvin leading to apoptosis of human germ cell tumor cells.     

Hierarchical multi-label prediction of gene function

MOTIVATION: Assigning functions for unknown genes based on diverse large-scale data is a key task in functional genomics. Previous work on gene function prediction has addressed this problem using independent classifiers for each function. However, such an approach ignores the structure of functional class taxonomies, such as the Gene Ontology (GO). Over a hierarchy of functional classes, a group of independent classifiers where each one predicts gene membership to a particular class can produce a hierarchically inconsistent set of predictions, where for a given gene a specific class may be predicted positive while its inclusive parent class is predicted negative. Taking the hierarchical structure into account resolves such inconsistencies and provides an opportunity for leveraging all classifiers in the hierarchy to achieve higher specificity of predictions. RESULTS: We developed a Bayesian framework for combining multiple classifiers based on the functional taxonomy constraints. Using a hierarchy of support vector machine (SVM) classifiers trained on multiple data types, we combined predictions in our Bayesian framework to obtain the most probable consistent set of predictions. Experiments show that over a 105-node subhierarchy of the GO, our Bayesian framework improves predictions for 93 nodes. As an additional benefit, our method also provides implicit calibration of SVM margin outputs to probabilities. Using this method, we make function predictions for multiple proteins, and experimentally confirm predictions for proteins involved in mitosis. SUPPLEMENTARY INFORMATION: Results for the 105 selected GO classes and predictions for 1059 unknown genes are available at: http://function.princeton.edu/genesite/ CONTACT: ogt@cs.princeton.edu.     

Acquisition of a potential marker for insect transformation: isolation of a novel alcohol dehydrogenase gene from <Emphasis Type="Italic">Bactrocera oleae</Emphasis> by functional complementation in yeast

The alcohol dehydrogenase genes make up one of the best studied gene families in Drosophila, both in terms of expression and evolution. Moreover, alcohol dehydrogenase genes constitute potential versatile markers in insect transformation experiments. However, due to their rapid evolution, these genes cannot be cloned from other insect genera by DNA hybridization or PCR-based strategies. We have therefore explored an alternative strategy: cloning by functional complementation of appropriate yeast mutants. Here we report that two alcohol dehydrogenase genes from the medfly Ceratitis capitata can functionally replace the yeast enzymes, even though the medfly and yeast genes have evolved independently, acquiring their enzymatic function convergently. Using this method, we have cloned an alcohol dehydrogenase gene from the olive pest Bactrocera oleae. We conclude that functional complementation in yeast can be used to clone alcohol dehydrogenase genes that are unrelated in sequence to those of yeast, thus providing a powerful tool for isolation of dominant insect transformation marker genes.     

Identification and characterization of glucocorticoid receptor-binding sites in the human genome

Glucocorticoids regulate gene expression via binding of the ligand-activated glucocorticoid receptor (GR) to glucocorticoid-responsive elements (GRE). To identify GR-binding sites, we developed a modified yeast one-hybrid system which enables rapid and efficient identification of genomic targets for DNA-binding proteins. The human GR expression vector was transformed into yeast cells containing a library of human genomic fragments cloned upstream of the reporter gene URA3. The genomic fragments with GR-binding sites were identified by growth of yeast clones in media lacking uracil but containing dexamethasone. DNA fragments were recovered by colony-direct PCR and GRE sequences were predicted by in silico analysis. Using electrophoretic mobility shift assay and fluorescence correlation spectroscopy, we demonstrated that 314 predicted GREs could directly interact with recombinant human GR proteins. In addition, when the genomic fragments were inserted in front of the heterologous SV40 promoter, at least 150 fragments could function as GREs in HEK293 cells. Furthermore, we identified four functional regulatory polymorphisms which may influence individual variation in sensitivity to glucocorticoids. These results provide insights into the molecular mechanisms underlying the physiological and pathological actions of glucocorticoid.     

Yeast Asc1p and mammalian RACK1 are functionally orthologous core 40S ribosomal proteins that repress gene expression

Translation of mRNA into protein is a fundamental step in eukaryotic gene expression requiring the large (60S) and small (40S) ribosome subunits and associated proteins. By modern proteomic approaches, we previously identified a novel 40S-associated protein named Asc1p in budding yeast and RACK1 in mammals. The goals of this study were to establish Asc1p or RACK1 as a core conserved eukaryotic ribosomal protein and to determine the role of Asc1p or RACK1 in translational control. We provide biochemical, evolutionary, genetic, and functional evidence showing that Asc1p or RACK1 is indeed a conserved core component of the eukaryotic ribosome. We also show that purified Asc1p-deficient ribosomes have increased translational activity compared to that of wild-type yeast ribosomes. Further, we demonstrate that asc1Delta null strains have increased levels of specific proteins in vivo and that this molecular phenotype is complemented by either Asc1p or RACK1. Our data suggest that one of Asc1p's or RACK1's functions is to repress gene expression.