To better understand off-target effects of widely prescribed psychoactive drugs, we performed a comprehensive series of chemogenomic screens using the budding yeast Saccharomyces cerevisiae as a ...model system. Because the known human targets of these drugs do not exist in yeast, we could employ the yeast gene deletion collections and parallel fitness profiling to explore potential off-target effects in a genome-wide manner. Among 214 tested, documented psychoactive drugs, we identified 81 compounds that inhibited wild-type yeast growth and were thus selected for genome-wide fitness profiling. Many of these drugs had a propensity to affect multiple cellular functions. The sensitivity profiles of half of the analyzed drugs were enriched for core cellular processes such as secretion, protein folding, RNA processing, and chromatin structure. Interestingly, fluoxetine (Prozac) interfered with establishment of cell polarity, cyproheptadine (Periactin) targeted essential genes with chromatin-remodeling roles, while paroxetine (Paxil) interfered with essential RNA metabolism genes, suggesting potential secondary drug targets. We also found that the more recently developed atypical antipsychotic clozapine (Clozaril) had no fewer off-target effects in yeast than the typical antipsychotics haloperidol (Haldol) and pimozide (Orap). Our results suggest that model organism pharmacogenetic studies provide a rational foundation for understanding the off-target effects of clinically important psychoactive agents and suggest a rational means both for devising compound derivatives with fewer side effects and for tailoring drug treatment to individual patient genotypes.
Vaccinia virus (VACV) is a large, cytoplasmic, double-stranded DNA virus that requires complex interactions with host proteins in order to replicate. To explore these interactions a functional high ...throughput small interfering RNA (siRNA) screen targeting 6719 druggable cellular genes was undertaken to identify host factors (HF) influencing the replication and spread of an eGFP-tagged VACV. The experimental design incorporated a low multiplicity of infection, thereby enhancing detection of cellular proteins involved in cell-to-cell spread of VACV. The screen revealed 153 pro- and 149 anti-viral HFs that strongly influenced VACV replication. These HFs were investigated further by comparisons with transcriptional profiling data sets and HFs identified in RNAi screens of other viruses. In addition, functional and pathway analysis of the entire screen was carried out to highlight cellular mechanisms involved in VACV replication. This revealed, as anticipated, that many pro-viral HFs are involved in translation of mRNA and, unexpectedly, suggested that a range of proteins involved in cellular transcriptional processes and several DNA repair pathways possess anti-viral activity. Multiple components of the AMPK complex were found to act as pro-viral HFs, while several septins, a group of highly conserved GTP binding proteins with a role in sequestering intracellular bacteria, were identified as strong anti-viral VACV HFs. This screen has identified novel and previously unexplored roles for cellular factors in poxvirus replication. This advancement in our understanding of the VACV life cycle provides a reliable knowledge base for the improvement of poxvirus-based vaccine vectors and development of anti-viral theraputics.
Yeast sulfur metabolism is transcriptionally regulated by the activator Met4. Met4 lacks DNA-binding ability and relies on interactions with Met31 and Met32, paralogous proteins that bind the same ...cis-regulatory element, to activate its targets. Although Met31 and Met32 are redundant for growth in the absence of methionine, studies indicate that Met32 has a prominent role over Met31 when Met30, a negative regulator of Met4 and Met32, is inactive. To characterize different roles of Met31 and Met32 in coordinating Met4-activated transcription, we examined transcription in strains lacking either Met31 or Met32 upon Met4 induction in the absence of Met30. Microarray analysis revealed that transcripts involved in sulfate assimilation and sulfonate metabolism were dramatically decreased in met32Δ cells compared to its wild-type and met31Δ counterparts. Despite this difference, both met31Δ and met32Δ cells used inorganic sulfur compounds and sulfonates as sole sulfur sources in minimal media when Met30 was present. This discrepancy may be explained by differential binding of Met31 to Cbf1-dependent promoters between these two conditions. In the absence of Met30, genome-wide chromatin immunoprecipitation analyses found that Met32 bound all Met4-bound targets, supporting Met32 as the main platform for Met4 recruitment. Finally, Met31 and Met32 levels were differentially regulated, with Met32 levels mimicking the profile for active Met4. These different properties of Met32 likely contribute to its prominent role in Met4-activated transcription when Met30 is absent.
The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical ...combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug discovery.
Abstract
Summary
In many areas of biological research, hypotheses are tested in a sequential manner, without having access to future P-values or even the number of hypotheses to be tested. A key ...setting where this online hypothesis testing occurs is in the context of publicly available data repositories, where the family of hypotheses to be tested is continually growing as new data is accumulated over time. Recently, Javanmard and Montanari proposed the first procedures that control the FDR for online hypothesis testing. We present an R package, onlineFDR, which implements these procedures and provides wrapper functions to apply them to a historic dataset or a growing data repository.
Availability and implementation
The R package is freely available through Bioconductor (http://www.bioconductor.org/packages/onlineFDR).
Supplementary information
Supplementary data are available at Bioinformatics online.
The visual detection, classification, and differentiation of cancers within tissues of clinical patients is an extremely difficult and time-consuming process with severe diagnosis implications. To ...this end, many computational approaches have been developed to analyse tissue samples to supplement histological cancer diagnoses. One approach is the interrogation of the chemical composition of the actual tissue samples through the utilisation of vibrational spectroscopy, specifically Infrared (IR) spectroscopy. Cancerous tissue can be detected by analysing the molecular vibration patterns of tissues undergoing IR irradiation, and even graded, with multivariate and Machine Learning (ML) techniques. This publication serves to review and highlight the potential for the application of infrared microscopy techniques such as Fourier Transform Infrared Spectroscopy (FTIR) and Quantum Cascade Laser Infrared Spectroscopy (QCL), as a means to improve diagnostic accuracy and allow earlier detection of human neoplastic disease. This review provides an overview of the detection and classification of different cancerous tissues using FTIR spectroscopy paired with multivariate and ML techniques, using the F1-Score as a quantitative metric for direct comparison of model performances. Comparisons also extend to data handling techniques, with a provision of a suggested pre-processing protocol for future studies alongside suggestions as to reporting standards for future publication.
A meta-analysis of various multivariate/Machine Learning (ML) classifiers trained on IR Micro-spectroscopy tissue datasets for cancer classification are directly compared using a calculated F
1
-Score metric alongside study pre-processing techniques.
Multiple signalling pathways maintain human embryonic stem cells (hESC) in an undifferentiated state. Here we sought to define the significance of G protein signal transduction in the preservation of ...this state distinct from other cellular processes. Continuous treatment with drugs targeting Gαs-, Gα-i/o- and Gα-q/11-subunit signalling mediators were assessed in independent hESC lines after 7days to discern effects on normalised alkaline phosphatase positive colony frequency vs total cell content. This identified PLCβ, intracellular free calcium and CAMKII kinase activity downstream of Gα-q/11 as of particular importance to the former. To confirm the significance of this finding we generated an agonist-responsive hESC line transgenic for a Gα-q/11 subunit-coupled receptor and demonstrated that an undifferentiated state could be promoted in the presence of an agonist without exogenously supplied bFGF and that this correlated with elevated intracellular calcium. Similarly, treatment of unmodified hESCs with a range of intracellular free calcium-modulating drugs in biologically defined mTESR culture system lacking exogenous bFGF promoted an hESC phenotype after 1week of continuous culture as defined by co-expression of OCT4 and NANOG. At least one of these drugs, lysophosphatidic acid significantly elevates phosphorylation of calmodulin and STAT3 in this culture system (p<0.05). These findings substantiate a role for G-protein and calcium signalling in undifferentiated hESC culture.
► Drug screening implicates G-subunit-mediated signalling in hESC self-renewal. ► Activation of Gα-q/11 coupled GPCR raises free Ca++ and substitutes for bFGF. ► Small molecule modulators of free calcium substitutes for bFGF. ► Lysophosphatidic acid treatment results in phosphorylation of Calmodulin and STAT3.
The biological response to DNA double-strand breaks acts to preserve genome integrity. Individuals bearing inactivating mutations in components of this response exhibit clinical symptoms that include ...cellular radiosensitivity, immunodeficiency, and cancer predisposition. The archetype for such disorders is Ataxia-Telangiectasia caused by biallelic mutation in ATM, a central component of the DNA damage response. Here, we report that the ubiquitin ligase RNF168 is mutated in the RIDDLE syndrome, a recently discovered immunodeficiency and radiosensitivity disorder. We show that RNF168 is recruited to sites of DNA damage by binding to ubiquitylated histone H2A. RNF168 acts with UBC13 to amplify the RNF8-dependent histone ubiquitylation by targeting H2A-type histones and by promoting the formation of lysine 63-linked ubiquitin conjugates. These RNF168-dependent chromatin modifications orchestrate the accumulation of 53BP1 and BRCA1 to DNA lesions, and their loss is the likely cause of the cellular and developmental phenotypes associated with RIDDLE syndrome.
Resistance to widely used fungistatic drugs, particularly to the ergosterol biosynthesis inhibitor fluconazole, threatens millions of immunocompromised patients susceptible to invasive fungal ...infections. The dense network structure of synthetic lethal genetic interactions in yeast suggests that combinatorial network inhibition may afford increased drug efficacy and specificity. We carried out systematic screens with a bioactive library enriched for off‐patent drugs to identify compounds that potentiate fluconazole action in pathogenic Candida and Cryptococcus strains and the model yeast Saccharomyces. Many compounds exhibited species‐ or genus‐specific synergism, and often improved fluconazole from fungistatic to fungicidal activity. Mode of action studies revealed two classes of synergistic compound, which either perturbed membrane permeability or inhibited sphingolipid biosynthesis. Synergistic drug interactions were rationalized by global genetic interaction networks and, notably, higher order drug combinations further potentiated the activity of fluconazole. Synergistic combinations were active against fluconazole‐resistant clinical isolates and an in vivo model of Cryptococcus infection. The systematic repurposing of approved drugs against a spectrum of pathogens thus identifies network vulnerabilities that may be exploited to increase the activity and repertoire of antifungal agents.
The authors screen for compounds that show synergistic antifungal activity when combined with the widely‐used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.
Synopsis
The authors screen for compounds that show synergistic antifungal activity when combined with the widely‐used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.
Rising fungal infection rates, especially among immune‐suppressed individuals, represent a serious clinical challenge (Gullo, 2009). Cancer, organ transplant and HIV patients, for example, often succumb to opportunistic fungal pathogens. The limited repertoire of approved antifungal agents and emerging drug resistance in the clinic further complicate the effective treatment of systemic fungal infections. At the molecular level, the paucity of fungal‐specific essential targets arises from the conserved nature of cellular functions from yeast to humans, as well as from the fact that many essential yeast genes can confer viability at a fraction of wild‐type dosage (Yan et al, 2009). Although only ∼1100 of the ∼6000 genes in yeast are essential, almost all genes become essential in specific genetic backgrounds in which another non‐essential gene has been deleted or otherwise attenuated, an effect termed synthetic lethality (Tong et al, 2001). Genome‐scale surveys suggest that over 200 000 binary synthetic lethal gene combinations dominate the yeast genetic landscape (Costanzo et al, 2010). The genetic buffering phenomenon is also manifest as a plethora of differential chemical–genetic interactions in the presence of sublethal doses of bioactive compounds (Hillenmeyer et al, 2008). These observations frame the difficulty of interdicting network functions in eukaryotic pathogens with single agent therapeutics. At the same time, however, this genetic network organization suggests that judicious combinations of small molecule inhibitors of both essential and non‐essential targets may elicit additive or synergistic effects on cell growth (Sharom et al, 2004; Lehar et al, 2008). Unbiased screens for drugs that synergistically enhance a specific bioactive effect, but which are not themselves individually active—termed a syncretic combination—are one means to substantially elaborate chemical space (Keith et al, 2005). Indeed, compounds that enhance the activity of known agents in model yeast and cancer cell line systems have been identified both by focused small molecule library screens and by computational methods (Borisy et al, 2003; Lehar et al, 2007; Nelander et al, 2008; Jansen et al, 2009; Zinner et al, 2009).
To extend the stratagem of chemical synthetic lethality to clinically relevant fungal pathogens, we screened a bioactive library of known drugs for synergistic enhancers of the widely used fungistatic drug fluconazole against the clinically relevant pathogens C. albicans, C. neoformans and C. gattii, as well as the genetically tractable budding yeast S. cerevisiae. Fluconazole is an azole drug that inhibits lanosterol 14α‐demethylase, the gene product of ERG11, an essential cytochrome P450 enzyme in the ergosterol biosynthetic pathway (Groll et al, 1998). We identified 148 drugs that potentiate the antifungal action of fluconazole against the four species. These syncretic compounds had not been previously recognized in the clinic as antifungal agents, and many acted in a species‐specific manner, often in a potent fungicidal manner.
To understand the mechanisms of synergism, we interrogated six syncretic drugs—trifluoperazine, tamoxifen, clomiphene, sertraline, suloctidil and L‐cycloserine—in genome‐wide chemogenomic profiles of the S. cerevisiae deletion strain collection (Giaever et al, 1999). These profiles revealed that membrane, vesicle trafficking and lipid biosynthesis pathways are targeted by five of the synergizers, whereas the sphingolipid biosynthesis pathway is targeted by L‐cycloserine. Cell biological assays confirmed the predicted membrane disruption effects of the former group of compounds, which may perturb ergosterol metabolism, impair fluconazole export by drug efflux pumps and/or affect active import of fluconazole (Kuo et al, 2010; Mansfield et al, 2010). Based on the integration of chemical–genetic and genetic interaction space, a signature set of deletion strains that are sensitive to the membrane active synergizers correctly predicted additional drug synergies with fluconazole. Similarly, the L‐cycloserine chemogenomic profile correctly predicted a synergistic interaction between fluconazole and myriocin, another inhibitor of sphingolipid biosynthesis. The structure of genetic networks suggests that it should be possible to devise higher order drug combinations with even greater selectivity and potency (Sharom et al, 2004). In an initial test of this concept, we found that the combination of a non‐synergistic pair drawn from the membrane active and sphingolipid target classes exhibited potent three‐way synergism with a low dose of fluconazole. Finally, the combination of sertraline and fluconazole was active in a G. mellonella model of Cryptococcal infection, and was also efficacious against fluconazole‐resistant clinical isolates of C. albicans and C. glabrata.
Collectively, these results demonstrate that the combinatorial redeployment of known drugs defines a powerful antifungal strategy and establish a number of potential lead combinations for future clinical assessment.
Chemical screens with a library enriched for known drugs identified a diverse set of 148 compounds that potentiated the action of the antifungal drug fluconazole against the fungal pathogens Cryptococcus neoformans, Cryptococcus gattii and Candida albicans, and the model yeast Saccharomyces cerevisiae, often in a species‐specific manner.
Chemogenomic profiles of six confirmed hits in S. cerevisiae revealed different modes of action and enabled the prediction of additional synergistic combinations; three‐way synergistic interactions exhibited even stronger synergies at low doses of fluconazole.
The synergistic combination of fluconazole and the antidepressant sertraline was active against fluconazole‐resistant clinical fungal isolates and in an in vivo model of Cryptococcal infection.
The MolClass toolkit and data portal generate computational models from user-defined small molecule datasets based on structural features identified in hit and non-hit molecules in different screens. ...Each new model is applied to all datasets in the database to classify compound specificity. MolClass thus defines a likelihood value for each compound entry and creates an activity fingerprint across diverse sets of screens. MolClass uses a variety of machine-learning methods to find molecular patterns and can therefore also assign a priori predictions of bioactivities for previously untested molecules. The power of the MolClass resource will grow as a function of the number of screens deposited in the database.
The MolClass webportal, software package and source code are freely available for non-commercial use at http://tyerslab.bio.ed.ac.uk/molclass. A MolClass tutorial and a guide on how to build models from datasets can also be found on the web site. MolClass uses the chemistry development kit (CDK), WEKA and MySQL for its core functionality. A REST service is available at http://tyerslab.bio.ed.ac.uk/molclass/api based on the OpenTox API 1.2.
PDF
