Chemical-genetic profiling of imidazo[1,2-a]pyridines and -pyrimidines reveals target pathways conserved between yeast and human cells. PLoS Genet. 2008;4:e1000284. [PMID: 19043571 PMCID: PMC2583946 DOI: 10.1371/journal.pgen.1000284]

Mitochondrial dysfunction from malathion and chlorpyrifos exposure is associated with degeneration of GABAergic neurons in Caenorhabditis elegans

Toxicity resulting from off-target effects, beyond acetylcholine esterase inhibition, for the commonly used organophosphate (OP) insecticides chlorpyrifos (CPS) and malathion (MA) was investigated using Saccharomyces cerevisiae and Caenorhabditis elegans model systems. Mitochondrial damage and dysfunction were observed in yeast following exposure to CPS and MA, suggesting this organelle is a major target. In the C. elegans model, the mitochondrial unfolded protein response pathway showed the most robust induction from CPS and MA treatment among stress responses examined. GABAergic neurodegeneration was observed with CPS and MA exposure. Impaired movement observed in C. elegans exposed to CPS and MA may be the result of motor neuron damage. Our analysis suggests that stress from CPS and MA results in mitochondrial dysfunction, with GABAergic neurons sensitized to these effects. These findings may aid in the understanding of toxicity from CPS and MA from high concentration exposure leading to insecticide poisoning.

Identification of novel antifungal agents: antimicrobial evaluation, SAR, ADME-Tox and molecular docking studies of a series of imidazole derivatives

Thirty-four imidazole-based compounds synthesized by one-pot catalytic method were evaluated for their antifungal and antibacterial activities against several fungal and bacterial strains. None of the compounds had antibacterial activity. Interestingly, compounds 1, 2, 3, 10 and 15 displayed a strong antifungal activity against all the tested fungal species, while compounds 5, 7, 9, 11, 21 and 27 showed a moderate antifungal activity. To better understand the biological activity of the most active compounds ADME-Tox and molecular docking studies were carried out. Interestingly, compounds 1, 2, 3, 7, 10 and 15 showed excellent bioavailability. In addition, compounds 1, 2 and 3, exhibited good toxicity profiles. Docking studies of the two most active compounds 2 (IC50 of 95 ± 7.07 μM) and 10 (IC50 of 235 ± 7.07 μM) suggested that they might act by inhibiting the fungal lanosterol 14α-demethylase. Therefore, these novel antifungal agents merit further characterization for the development of new antifungal therapeutics.

Epistasis in genomic and survival data of cancer patients

Cancer aggressiveness and its effect on patient survival depends on mutations in the tumor genome. Epistatic interactions between the mutated genes may guide the choice of anticancer therapy and set predictive factors of its success. Inhibitors targeting synthetic lethal partners of genes mutated in tumors are already utilized for efficient and specific treatment in the clinic. The space of possible epistatic interactions, however, is overwhelming, and computational methods are needed to limit the experimental effort of validating the interactions for therapy and characterizing their biomarkers. Here, we introduce SurvLRT, a statistical likelihood ratio test for identifying epistatic gene pairs and triplets from cancer patient genomic and survival data. Compared to established approaches, SurvLRT performed favorable in predicting known, experimentally verified synthetic lethal partners of PARP1 from TCGA data. Our approach is the first to test for epistasis between triplets of genes to identify biomarkers of synthetic lethality-based therapy. SurvLRT proved successful in identifying the known gene TP53BP1 as the biomarker of success of the therapy targeting PARP in BRCA1 deficient tumors. Search for other biomarkers for the same interaction revealed a region whose deletion was a more significant biomarker than deletion of TP53BP1. With the ability to detect not only pairwise but twelve different types of triple epistasis, applicability of SurvLRT goes beyond cancer therapy, to the level of characterization of shapes of fitness landscapes.

Genomic alterations in tumors affect the fitness of tumor cells, controlling how well they replicate and survive compared to other cells. The landscape of tumor fitness is shaped by epistasis. Epistasis occurs when the contribution of gene alterations to the total fitness is non-linear. The type of epistatic genetic interactions with great potential for cancer therapy is synthetic lethality. Inhibitors targeting synthetic lethal partners of genes mutated in tumors can selectively kill tumor and not normal cells. Therapy based on synthetic lethality is, however, context dependent, and it is crucial to identify its biomarkers. Unfortunately, the space of possible interactions and their biomarkers is overwhelming for experimental validation. Computational pre-selection methods are required to limit the experimental effort. Here, we introduce a statistical approach called SurvLRT, for the identification of epistatic gene pairs and triplets based on patient genomic and survival data. First, we show that using SurvLRT, we can deliver synthetic lethal interactions of pairs of genes that are specific to cancer. Second, we demonstrate the applicability of SurvLRT to identify biomarkers for synthetic lethality, such as mutational status of other genes that can alleviate the synthetic effect.

Open Source Drug Discovery: Highly Potent Antimalarial Compounds Derived from the Tres Cantos Arylpyrroles

The development of new antimalarial compounds remains a pivotal part of the strategy for malaria elimination. Recent large-scale phenotypic screens have provided a wealth of potential starting points for hit-to-lead campaigns. One such public set is explored, employing an open source research mechanism in which all data and ideas were shared in real time, anyone was able to participate, and patents were not sought. One chemical subseries was found to exhibit oral activity but contained a labile ester that could not be replaced without loss of activity, and the original hit exhibited remarkable sensitivity to minor structural change. A second subseries displayed high potency, including activity within gametocyte and liver stage assays, but at the cost of low solubility. As an open source research project, unexplored avenues are clearly identified and may be explored further by the community; new findings may be cumulatively added to the present work.

Identification of a Mitochondrial DNA Polymerase Affecting Cardiotoxicity of Sunitinib Using a Genome-Wide Screening on S. pombe Deletion Library

Drug toxicity is a key issue for drug R&D, a fundamental challenge of which is to screen for the targets genome-wide. The anticancer tyrosine kinase inhibitor sunitinib is known to induce cardiotoxicity. Here, to understand the molecular insights of cardiotoxicity by sunitinib at the genome level, we used a genome-wide drug target screening technology (GPScreen) that measures drug-induced haploinsufficiency (DIH) in the fission yeast Schizosaccharomyces pombe genome-wide deletion library and found a mitochondrial DNA polymerase (POG1). In the results, sunitinib induced more severe cytotoxicity and mitochondrial damage in POG1-deleted heterozygous mutants compared to wild type (WT) of S. pombe. Furthermore, knockdown of the human ortholog POLG of S. pombe POG1 in human cells significantly increased the cytotoxicity of sunitinib. Notably, sunitinib dramatically decreased the levels of POLG mRNAs and proteins, of which downregulation was already known to induce mitochondrial damage of cardiomyocytes, causing cardiotoxicity. These results indicate that POLG might play a crucial role in mitochondrial damage as a gene of which expressional pathway is targeted by sunitinib for cardiotoxicity, and that genome-wide drug target screening with GPScreen can be applied to drug toxicity target discovery to understand the molecular insights regarding drug toxicity.

Building high-resolution synthetic lethal networks: a 'Google map' of the cancer cell

The most commonly used therapies for cancer involve delivering high doses of radiation or toxic chemicals to the patient that also cause substantial damage to normal tissue. To overcome this, researchers have recently resorted to a basic biological concept called 'synthetic lethality' (SL) that takes advantage of interactions between gene pairs. The identification of SL interactions is of considerable therapeutic interest because if a particular gene is SL with a tumor-causing mutation, then the targeting that gene carries therapeutic advantages. Mapping these interactions in the context of human cancer cells could hold the key to effective, targeted cancer treatments. In this review, we cover the recent advances that aim to identify these SL interactions using unbiased genetic screens.

Multidimensional profiling in the investigation of small-molecule-induced cell death

Numerous morphological variations of cell death have been described. These processes depend on a complex and overlapping cellular signaling network, making molecular definition of the pathways challenging. This review describes one solution to this problem for small-molecule-induced death, the creation of high-dimensionality profiles for compounds that can be used to define and compare pathways. Such profiles have been assembled from gene expression measurements, protein quantification, chemical-genetic interactions, chemical combination interactions, cancer cell line sensitivity profiling, quantitative imaging, and modulatory profiling. We discuss the advantages and limitations of these techniques in the study of cell death.

Exploring mitochondrial system properties of neurodegenerative diseases through interactome mapping

Mitochondria are double membraned, dynamic organelles that are required for a large number of cellular processes, and defects in their function have emerged as causative factors for a growing number of human disorders and are highly associated with cancer, metabolic, and neurodegenerative (ND) diseases. Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in ND disease, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. However, high-throughput proteomic and genomic approaches developed in genetically tractable model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins, including cytosolic and membrane proteins. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We discuss how the knowledge from the resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus further clarify the role of mitochondrial biology and the complex etiologies of ND disease.

BIOLOGICAL SIGNIFICANCE

Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in neurodegenerative (ND) diseases, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. Large-scale proteomic and genomic approaches developed in model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins. Extension of this new framework to the mitochondrial sub-system in human will likewise provide a universally informative systems-level view of the physical and functional landscape for exploring the evolutionary principles underlying mitochondrial function. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We anticipate that the knowledge from these resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus foster a deeper molecular understanding of mitochondrial biology as well as the etiology of mitochondrial diseases. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

Intramolecular H-bonding interaction in angular 3-π-EWG substituted imidazo[1,2-a]pyridines contributes to conformational preference

Background

The proton at position 5 of imidazo[1,2-a]pyridines substituted with an angular electron withdrawing group (EWG) at position 3, shows an unusual downfield chemical shift, which is usually explained in terms of a peri effect. However usage of this term is sometimes confusing. In this investigation, it is proposed that the aforementioned shift is in fact a combination of several factors: Anisotropy, long-distance mesomerism and an attractive intramolecular interaction of the electrostatic hydrogen bond type.

Results

Theoretical calculations were performed aimed to obtain evidence of the existence of an intramolecular non-bonding interaction between H-5 and the oxygen atom of the EWG. Results derived from conformational and vibrational analysis at the DFT B3LYP/6-311++G(d,p) level of theory, the determination of Bond Critical Points derived from AIM theory, and the measurement of some geometrical parameters, support the hypothesis that the higher stability of the prevailing conformation in these molecules (that in which the oxygen of the EWG is oriented towards H-5) has its origin in an intramolecular interaction.

Conclusion

Computational calculations predicted correctly the conformational preferences in angular 3-π-EWG-substituted imidazo[1,2-a]pyridines. The existence of an electrostatic hydrogen bond between H-5 and the oxygen atom of the π-EWG was supported by several parameters, including X-ray crystallography. The existence of such structural array evidently impacts the H-5 chemical shift.

Genetic interactions in translational research on cancer

Genetic interactions are functional crosstalk among different genetic loci that lead to phenotypic changes, such as health or viability alterations. A disease or lethal phenotype that results from the combined effects of gene mutations at different loci is termed a synthetic sickness or synthetic lethality, respectively. Studies of genetic interaction have provided insight on the relationships among biochemical processes or pathways. Cancer results from genetic interactions and is a major focus of current studies in genetic interactions. Various basic and translational cancer studies have explored the concept of genetic interactions, including studies of the mechanistic characterization of genes, drug discovery, biomarker identification and the rational design of combination therapies. This review discusses the implications of genetic interactions in the development of personalized cancer therapies, the identification of treatment-responsive genes, the delineation of mechanisms of chemoresistance and the rational design of combined therapeutic strategies to overcome drug resistance.

Differential involvement of mitochondrial dysfunction, cytochrome P450 activity, and active transport in the toxicity of structurally related NSAIDs

Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used in the treatment of pain and inflammation. However, this group of drugs is associated with serious adverse drug reactions. Previously, we studied the mechanisms underlying toxicity of the NSAID diclofenac using Saccharomycescerevisiae as model system. We identified the involvement of several mitochondrial proteins, a transporter and cytochrome P450 activity in diclofenac toxicity. In this study, we investigated if these processes are also involved in the toxicity of other NSAIDs. We divided the NSAIDs into three classes based on their toxicity mechanisms. Class I consists of diclofenac, indomethacin and ketoprofen. Mitochondrial respiration and reactive oxygen species (ROS) play a major role in the toxicity of this class. Metabolism by cytochrome P450s further increases their toxicity, while ABC-transporters decrease the toxicity. Mitochondria and oxidative metabolism also contribute to toxicity of class II drugs ibuprofen and naproxen, but another cellular target dominates their toxicity. Interestingly, ibuprofen was the only NSAID that was unable to induce upregulation of the multidrug resistance response. The class III NSAIDs sulindac, ketorolac and zomepirac were relatively non-toxic in yeast. In conclusion, we demonstrate the use of yeast to investigate the mechanisms underlying the toxicity of structurally related drugs.

The Budding Yeast "Saccharomyces cerevisiae" as a Drug Discovery Tool to Identify Plant-Derived Natural Products with Anti-Proliferative Properties

The budding yeast Saccharomyces cerevisiae is a valuable system to study cell-cycle regulation, which is defective in cancer cells. Due to the highly conserved nature of the cell-cycle machinery between yeast and humans, yeast studies are directly relevant to anticancer-drug discovery. The budding yeast is also an excellent model system for identifying and studying antifungal compounds because of the functional conservation of fungal genes. Moreover, yeast studies have also contributed greatly to our understanding of the biological targets and modes of action of bioactive compounds. Understanding the mechanism of action of clinically relevant compounds is essential for the design of improved second-generation molecules. Here we describe our methodology for screening a library of plant-derived natural products in yeast in order to identify and characterize new compounds with anti-proliferative properties.

The synthetic genetic interaction network reveals small molecules that target specific pathways in Sacchromyces cerevisiae

High-throughput elucidation of synthetic genetic interactions (SGIs) has contributed to a systems-level understanding of genetic robustness and fault-tolerance encoded in the genome. Pathway targets of various compounds have been predicted by comparing chemical-genetic synthetic interactions to a network of SGIs. We demonstrate that the SGI network can also be used in a powerful reverse pathway-to-drug approach for identifying compounds that target specific pathways of interest. Using the SGI network, the method identifies an indicator gene that may serve as a good candidate for screening a library of compounds. The indicator gene is selected so that compounds found to produce sensitivity in mutants deleted for the indicator gene are likely to abrogate the target pathway. We tested the utility of the SGI network for pathway-to-drug discovery using the DNA damage checkpoint as the target pathway. An analysis of the compendium of synthetic lethal interactions in yeast showed that superoxide dismutase 1 (SOD1) has significant SGI connectivity with a large subset of DNA damage checkpoint and repair (DDCR) genes in Saccharomyces cerevisiae, and minimal SGIs with non-DDCR genes. We screened a sod1Δ strain against three National Cancer Institute (NCI) compound libraries using a soft agar high-throughput halo assay. Fifteen compounds out of ∼3100 screened showed selective toxicity toward sod1Δ relative to the isogenic wild type (wt) strain. One of these, 1A08, caused a transient increase in growth in the presence of sublethal doses of DNA damaging agents, suggesting that 1A08 inhibits DDCR signaling in yeast. Genome-wide screening of 1A08 against the library of viable homozygous deletion mutants further supported DDCR as the relevant targeted pathway of 1A08. When assayed in human HCT-116 colorectal cancer cells, 1A08 caused DNA-damage resistant DNA synthesis and blocked the DNA-damage checkpoint selectively in S-phase.

Yeast systems biology: the challenge of eukaryotic complexity

In this chapter, we present an up-to-date view of the optimal characteristics of the yeast Saccharomyces cerevisiae as a model eukaryote for systems biology studies, with main molecular mechanisms, biological networks, and sub-cellular organization essentially conserved in all eukaryotes, derived from a complex common ancestor. The existence of advanced tools for molecular studies together with high-throughput experimental and computational methods, most of them being implemented and validated in yeast, with new ones being developed, is opening the way to the characterization of the core modular architecture and complex networks essential to all eukaryotes. Selected examples of the latest discoveries in eukaryote complexity and systems biology studies using yeast as a reference model and their applications in biotechnology and medicine are presented.

Contributions of Saccharomyces cerevisiae to understanding mammalian gene function and therapy

Due to its genetic tractability and ease of manipulation, the yeast Saccharomyces cerevisiae has been extensively used as a model organism to understand how eukaryotic cells grow, divide, and respond to environmental changes. In this chapter, we reasoned that functional annotation of novel genes revealed by sequencing should adopt an integrative approach including both bioinformatics and experimental analysis to reveal functional conservation and divergence of complexes and pathways. The techniques and resources generated for systems biology studies in yeast have found a wide range of applications. Here we focused on using these technologies in revealing functions of genes from mammals, in identifying targets of novel and known drugs and in screening drugs targeting specific proteins and/or protein-protein interactions.

Phenocopy--a strategy to qualify chemical compounds during hit-to-lead and/or lead optimization

A phenocopy is defined as an environmentally induced phenotype of one individual which is identical to the genotype-determined phenotype of another individual. The phenocopy phenomenon has been translated to the drug discovery process as phenotypes produced by the treatment of biological systems with new chemical entities (NCE) may resemble environmentally induced phenotypic modifications. Various new chemical entities exerting inhibition of the kinase activity of Transforming Growth Factor β Receptor I (TGF-βR1) were qualified by high-throughput RNA expression profiling. This chemical genomics approach resulted in a precise time-dependent insight to the TGF-β biology and allowed furthermore a comprehensive analysis of each NCE's off-target effects. The evaluation of off-target effects by the phenocopy approach allows a more accurate and integrated view on optimized compounds, supplementing classical biological evaluation parameters such as potency and selectivity. It has therefore the potential to become a novel method for ranking compounds during various drug discovery phases.

Functional toxicogenomics: mechanism-centered toxicology

Traditional toxicity testing using animal models is slow, low capacity, expensive and assesses a limited number of endpoints. Such approaches are inadequate to deal with the increasingly large number of compounds found in the environment for which there are no toxicity data. Mechanism-centered high-throughput testing represents an alternative approach to meet this pressing need but is limited by our current understanding of toxicity pathways. Functional toxicogenomics, the global study of the biological function of genes on the modulation of the toxic effect of a compound, can play an important role in identifying the essential cellular components and pathways involved in toxicity response. The combination of the identification of fundamental toxicity pathways and mechanism-centered targeted assays represents an integrated approach to advance molecular toxicology to meet the challenges of toxicity testing in the 21^st century.

Synthetic lethality: general principles, utility and detection using genetic screens in human cells

Synthetic lethality occurs when the simultaneous perturbation of two genes results in cellular or organismal death. Synthetic lethality also occurs between genes and small molecules, and can be used to elucidate the mechanism of action of drugs. This area has recently attracted attention because of the prospect of a new generation of anti-cancer drugs. Based on studies ranging from yeast to human cells, this review provides an overview of the general principles that underlie synthetic lethality and relates them to its utility for identifying gene function, drug action and cancer therapy. It also identifies the latest strategies for the large-scale mapping of synthetic lethalities in human cells which bring us closer to the generation of comprehensive human genetic interaction maps.

Combined zebrafish-yeast chemical-genetic screens reveal gene-copper-nutrition interactions that modulate melanocyte pigmentation

Hypopigmentation is a feature of copper deficiency in humans, as caused by mutation of the copper (Cu(2+)) transporter ATP7A in Menkes disease, or an inability to absorb copper after gastric surgery. However, many causes of copper deficiency are unknown, and genetic polymorphisms might underlie sensitivity to suboptimal environmental copper conditions. Here, we combined phenotypic screens in zebrafish for compounds that affect copper metabolism with yeast chemical-genetic profiles to identify pathways that are sensitive to copper depletion. Yeast chemical-genetic interactions revealed that defects in intracellular trafficking pathways cause sensitivity to low-copper conditions; partial knockdown of the analogous Ap3s1 and Ap1s1 trafficking components in zebrafish sensitized developing melanocytes to hypopigmentation in low-copper environmental conditions. Because trafficking pathways are essential for copper loading into cuproproteins, our results suggest that hypomorphic alleles of trafficking components might underlie sensitivity to reduced-copper nutrient conditions. In addition, we used zebrafish-yeast screening to identify a novel target pathway in copper metabolism for the small-molecule MEK kinase inhibitor U0126. The zebrafish-yeast screening method combines the power of zebrafish as a disease model with facile genome-scale identification of chemical-genetic interactions in yeast to enable the discovery and dissection of complex multigenic interactions in disease-gene networks.

Robustness and evolvability in natural chemical resistance: identification of novel systems properties, biochemical mechanisms and regulatory interactions

A vast amount of data on the natural resistance of Saccharomyces cerevisiae to a diverse array of chemicals has been generated over the past decade (chemical genetics). We endeavored to use this data to better characterize the "systems" level properties of this phenomenon. By collating data from over 30 different genome-scale studies on growth of gene deletion mutants in presence of diverse chemicals, we assembled the largest currently available gene-chemical network. We also derived a second gene-gene network that links genes with significantly overlapping chemical-genetic profiles. We analyzed properties of these networks and investigated their significance by overlaying various sources of information, such as presence of TATA boxes in their promoters (which typically correlate with transcriptional noise), association with TFIID or SAGA, and propensity to function as phenotypic capacitors. We further combined these networks with ubiquitin and protein kinase-substrate networks to understand chemical tolerance in the context of major post-translational regulatory processes. Hubs in the gene-chemical network (multidrug resistance genes) are notably enriched for phenotypic capacitors (buffers against phenotypic variation), suggesting the generality of these players in buffering mechanistically unrelated deleterious forces impinging on the cell. More strikingly, analysis of the gene-gene network derived from the gene-chemical network uncovered another set of genes that appear to function in providing chemical tolerance in a cooperative manner. These appear to be enriched in lineage-specific and rapidly diverging members that also show a corresponding tendency for SAGA-dependent regulation, evolutionary divergence and noisy expression patterns. This set represents a previously underappreciated component of the chemical response that enables cells to explore alternative survival strategies. Thus, systems robustness and evolvability are simultaneously active as general forces in tolerating environmental variation. We also recover the actual genes involved in the above-discussed network properties and predict the biochemistry of their products. Certain key components of the ubiquitin system (e.g. Rcy1, Wss1 and Ubp16), peroxisome recycling (e.g. Irs4) and phosphorylation cascades (e.g. NPR1, MCK1 and HOG) are major participants and regulators of chemical resistance. We also show that a major sub-network boosting mitochondrial protein synthesis is important for exploration of alternative survival strategies under chemical stress. Further, we find evidence that cellular exploration of survival strategies under chemical stress and secondary metabolism draw from a common pool of biochemical players (e.g. acetyltransferases and a novel NTN hydrolase).

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.