Distinctive Behaviors of Druggable Proteins in Cellular Networks

The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/.     

ASDCD: Antifungal Synergistic Drug Combination Database

Finding effective drugs to treat fungal infections has important clinical significance based on high mortality rates, especially in an immunodeficient population. Traditional antifungal drugs with single targets have been reported to cause serious side effects and drug resistance. Nowadays, however, drug combinations, particularly with respect to synergistic interaction, have attracted the attention of researchers. In fact, synergistic drug combinations could simultaneously affect multiple subpopulations, targets, and diseases. Therefore, a strategy that employs synergistic antifungal drug combinations could eliminate the limitations noted above and offer the opportunity to explore this emerging bioactive chemical space. However, it is first necessary to build a powerful database in order to facilitate the analysis of drug combinations. To address this gap in our knowledge, we have built the first Antifungal Synergistic Drug Combination Database (ASDCD), including previously published synergistic antifungal drug combinations, chemical structures, targets, target-related signaling pathways, indications, and other pertinent data. Its current version includes 210 antifungal synergistic drug combinations and 1225 drug-target interactions, involving 105 individual drugs from more than 12,000 references. ASDCD is freely available at http://ASDCD.amss.ac.cn.     

Receptors and Other Signaling Proteins Required for Serotonin Control of Locomotion in Caenorhabditis elegans

A better understanding of the molecular mechanisms of signaling by the neurotransmitter serotonin is required to assess the hypothesis that defects in serotonin signaling underlie depression in humans. Caenorhabditis elegans uses serotonin as a neurotransmitter to regulate locomotion, providing a genetic system to analyze serotonin signaling. From large-scale genetic screens we identified 36 mutants of C. elegans in which serotonin fails to have its normal effect of slowing locomotion, and we molecularly identified eight genes affected by 19 of the mutations. Two of the genes encode the serotonin-gated ion channel MOD-1 and the G-protein-coupled serotonin receptor SER-4. mod-1 is expressed in the neurons and muscles that directly control locomotion, while ser-4 is expressed in an almost entirely non-overlapping set of sensory and interneurons. The cells expressing the two receptors are largely not direct postsynaptic targets of serotonergic neurons. We analyzed animals lacking or overexpressing the receptors in various combinations using several assays for serotonin response. We found that the two receptors act in parallel to affect locomotion. Our results show that serotonin functions as an extrasynaptic signal that independently activates multiple receptors at a distance from its release sites and identify at least six additional proteins that appear to act with serotonin receptors to mediate serotonin response.     

Application of a Drug-Induced Apoptosis Assay to Identify Treatment Strategies in Recurrent or Metastatic Breast Cancer

BackgroundA drug-induced apoptosis assay has been developed to determine which chemotherapy drugs or regimens can produce higher cell killing in vitro. This study was done to determine if this assay could be performed in patients with recurrent or metastatic breast cancer patients, to characterize the patterns of drug-induced apoptosis, and to evaluate the clinical utility of the assay. A secondary goal was to correlate assay use with clinical outcomes.MethodsIn a prospective, non-blinded, multi institutional controlled trial, 30 evaluable patients with recurrent or metastatic breast cancer who were treated with chemotherapy had tumor samples submitted for the MiCK drug-induced apoptosis assay. After receiving results within 72 hours after biopsy, physicians could use the test to determine therapy (users), or elect to not use the test (non-users).ResultsThe assay was able to characterize drug-induced apoptosis in tumor specimens from breast cancer patients and identified which drugs or combinations gave highest levels of apoptosis. Patterns of drug activity were also analyzed in triple negative breast cancer. Different drugs from a single class of agents often produced significantly different amounts of apoptosis. Physician frequently (73%) used the assay to help select chemotherapy treatments in patients, Patients whose physicians were users had a higher response (CR+PR) rate compared to non-users (38.1% vs 0%, p = 0.04) and a higher disease control (CR+PR+Stable) rate (81% vs 25%, p<0.01). Time to relapse was longer in users 7.4 mo compared to non-users 2.2 mo (p<0.01).ConclusionsThe MiCK assay can be performed in breast cancer specimens, and results are often used by physicians in breast cancer patients with recurrent or metastatic disease. These results from a good laboratory phase II study can be the basis for a future larger prospective multicenter study to more definitively establish the value of the assay.

Trial Registration

Clinicaltrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00901264","term_id":"NCT00901264"}}NCT00901264     

Finasteride Concentrations and Prostate Cancer Risk: Results from the Prostate Cancer Prevention Trial

ObjectiveIn the Prostate Cancer Prevention Trial (PCPT), finasteride reduced the risk of prostate cancer by 25%, even though high-grade prostate cancer was more common in the finasteride group. However, it remains to be determined whether finasteride concentrations may affect prostate cancer risk. In this study, we examined the association between serum finasteride concentrations and the risk of prostate cancer in the treatment arm of the PCPT and determined factors involved in modifying drug concentrations.MethodsData for this nested case-control study are from the PCPT. Cases were drawn from men with biopsy-proven prostate cancer and matched controls. Finasteride concentrations were measured using a liquid chromatography-mass spectrometry validated assay. The association of serum finasteride concentrations with prostate cancer risk was determined by logistic regression. We also examine whether polymorphisms in the enzyme target and metabolism genes of finasteride are related to drug concentrations using linear regression.

Results and Conclusions

Among men with detectable finasteride concentrations, there was no association between finasteride concentrations and prostate cancer risk, low-grade or high-grade, when finasteride concentration was analyzed as a continuous variable or categorized by cutoff points. Since there was no concentration-dependent effect on prostate cancer, any exposure to finasteride intake may reduce prostate cancer risk. Of the twenty-seven SNPs assessed in the enzyme target and metabolism pathway, five SNPs in two genes, CYP3A4 (rs2242480; rs4646437; rs4986910), and CYP3A5 (rs15524; rs776746) were significantly associated with modifying finasteride concentrations. These results suggest that finasteride exposure may reduce prostate cancer risk and finasteride concentrations are affected by genetic variations in genes responsible for altering its metabolism pathway.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00288106","term_id":"NCT00288106"}}NCT00288106     

Genetic Analysis of Resistance and Sensitivity to 2-Deoxyglucose in Saccharomyces cerevisiae

Aerobic glycolysis is a metabolic pathway utilized by human cancer cells and also by yeast cells when they ferment glucose to ethanol. Both cancer cells and yeast cells are inhibited by the presence of low concentrations of 2-deoxyglucose (2DG). Genetic screens in yeast used resistance to 2-deoxyglucose to identify a small set of genes that function in regulating glucose metabolism. A recent high throughput screen for 2-deoxyglucose resistance identified a much larger set of seemingly unrelated genes. Here, we demonstrate that these newly identified genes do not in fact confer significant resistance to 2-deoxyglucose. Further, we show that the relative toxicity of 2-deoxyglucose is carbon source dependent, as is the resistance conferred by gene deletions. Snf1 kinase, the AMP-activated protein kinase of yeast, is required for 2-deoxyglucose resistance in cells growing on glucose. Mutations in the SNF1 gene that reduce kinase activity render cells hypersensitive to 2-deoxyglucose, while an activating mutation in SNF1 confers 2-deoxyglucose resistance. Snf1 kinase activated by 2-deoxyglucose does not phosphorylate the Mig1 protein, a known Snf1 substrate during glucose limitation. Thus, different stimuli elicit distinct responses from the Snf1 kinase.     

A Comprehensive Molecular Interaction Map for Rheumatoid Arthritis

Background

Computational biology contributes to a variety of areas related to life sciences and, due to the growing impact of translational medicine - the scientific approach to medicine in tight relation with basic science -, it is becoming an important player in clinical-related areas. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Rheumatoid Arthritis (RA).

Methodology

Due to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by RA. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. This global network has then been topologically analysed, as a whole and tissue-specifically, in order to translate the experimental molecular connections into topological motifs meaningful in the identification of tissue-specific markers and targets in the diagnosis, and possibly in the therapy, of RA.

Significance

We find that some nodes in the network that prove to be topologically important, in particular AKT2, IL6, MAPK1 and TP53, are also known to be associated with drugs used for the treatment of RA. Importantly, based on topological consideration, we are also able to suggest CRKL as a novel potentially relevant molecule for the diagnosis or treatment of RA. This type of finding proves the potential of in silico analyses able to produce highly refined hypotheses, based on vast experimental data, to be tested further and more efficiently. As research on RA is ongoing, the present map is in fieri, despite being -at the moment- a reflection of the state of the art. For this reason we make the network freely available in the standardised and easily exportable .xml CellDesigner format at ‘www.picb.ac.cn/ClinicalGenomicNTW/temp.html’ and ‘www.celldesigner.org’.     

Molecular Pathways Involved in Prostate Carcinogenesis: Insights from Public Microarray Datasets

Background

Prostate cancer is currently the most frequently diagnosed malignancy in men and the second leading cause of cancer-related deaths in industrialized countries. Worldwide, an increase in prostate cancer incidence is expected due to an increased life-expectancy, aging of the population and improved diagnosis. Although the specific underlying mechanisms of prostate carcinogenesis remain unknown, prostate cancer is thought to result from a combination of genetic and environmental factors altering key cellular processes. To elucidate these complex interactions and to contribute to the understanding of prostate cancer progression and metastasis, analysis of large scale gene expression studies using bioinformatics approaches is used to decipher regulation of core processes.

Methodology/Principal Findings

In this study, a standardized quality control procedure and statistical analysis (http://www.arrayanalysis.org/) were applied to multiple prostate cancer datasets retrieved from the ArrayExpress data repository and pathway analysis using PathVisio (http://www.pathvisio.org/) was performed. The results led to the identification of three core biological processes that are strongly affected during prostate carcinogenesis: cholesterol biosynthesis, the process of epithelial-to-mesenchymal transition and an increased metabolic activity.

Conclusions

This study illustrates how a standardized bioinformatics evaluation of existing microarray data and subsequent pathway analysis can quickly and cost-effectively provide essential information about important molecular pathways and cellular processes involved in prostate cancer development and disease progression. The presented results may assist in biomarker profiling and the development of novel treatment approaches.     

GraphProt: modeling binding preferences of RNA-binding proteins

We present GraphProt, a computational framework for learning sequence- and structure-binding preferences of RNA-binding proteins (RBPs) from high-throughput experimental data. We benchmark GraphProt, demonstrating that the modeled binding preferences conform to the literature, and showcase the biological relevance and two applications of GraphProt models. First, estimated binding affinities correlate with experimental measurements. Second, predicted Ago2 targets display higher levels of expression upon Ago2 knockdown, whereas control targets do not. Computational binding models, such as those provided by GraphProt, are essential for predicting RBP binding sites and affinities in all tissues. GraphProt is freely available at http://www.bioinf.uni-freiburg.de/Software/GraphProt.     

Design,Synthesis and Pharmacological Evaluation of Novel Vanadium-Containing Complexes as Antidiabetic Agents

Based on the data about structure and antidiabetic activity of twenty seven vanadium and zinc coordination complexes collected from literature we developed QSAR models using the GUSAR program. These QSAR models were applied to 10 novel vanadium coordination complexes designed in silico in order to predict their hypoglycemic action. The five most promising substances with predicted potent hypoglycemic action were selected for chemical synthesis and pharmacological evaluation. The selected coordination vanadium complexes were synthesized and tested in vitro and in vivo for their hypoglycemic activities and acute rat toxicity. Estimation of acute rat toxicity of these five vanadium complexes was performed using a freely available web-resource (http://way2drug.com/GUSAR/acutoxpredict.html). It has shown that the selected compounds belong to the class of moderate toxic pharmaceutical agents, according to the scale of Hodge and Sterner. Comparison with the predicted data has demonstrated a reasonable correspondence between the experimental and predicted values of hypoglycemic activity and toxicity. Bis{tert-butyl[amino(imino)methyl]carbamato}oxovanadium (IV) and sodium(2,2′-Bipyridyl)oxo-diperoxovanadate(V) octahydrate were identified as the most potent hypoglycemic agents among the synthesized compounds.     

Synthetic Lethal Targeting of Superoxide Dismutase 1 Selectively Kills RAD54B-Deficient Colorectal Cancer Cells

Synthetic lethality is a rational approach to identify candidate drug targets for selective killing of cancer cells harboring somatic mutations that cause chromosome instability (CIN). To identify a set of the most highly connected synthetic lethal partner genes in yeast for subsequent testing in mammalian cells, we used the entire set of 692 yeast CIN genes to query the genome-wide synthetic lethal datasets. Hierarchical clustering revealed a highly connected set of synthetic lethal partners of yeast genes whose human orthologs are somatically mutated in colorectal cancer. Testing of a small matrix of synthetic lethal gene pairs in mammalian cells suggested that members of a pathway that remove reactive oxygen species that cause DNA damage would be excellent candidates for further testing. We show that the synthetic lethal interaction between budding yeast rad54 and sod1 is conserved within a human colorectal cancer context. Specifically, we demonstrate RAD54B-deficient cells are selectively killed relative to controls via siRNA-based silencing and chemical inhibition and further demonstrate that this interaction is conserved in an unrelated cell type. We further show that the DNA double strand breaks, resulting from increased reactive oxygen species following SOD1 inhibition, persist within the RAD54B-deficient cells and result in apoptosis. Collectively, these data identify SOD1 as a novel candidate cancer drug target and suggest that SOD1 inhibition may have broad-spectrum applicability in a variety of tumor types exhibiting RAD54B deficiencies.     

DSIR: Assessing the Design of Highly Potent siRNA by Testing a Set of Cancer-Relevant Target Genes

Chemically synthesized small interfering RNA (siRNA) is a widespread molecular tool used to knock down genes in mammalian cells. However, designing potent siRNA remains challenging. Among tools predicting siRNA efficacy, very few have been validated on endogenous targets in realistic experimental conditions. We previously described a tool to assist efficient siRNA design (DSIR, Designer of siRNA), which focuses on intrinsic features of the siRNA sequence. Here, we evaluated DSIR’s performance by systematically investigating the potency of the siRNA it designs to target ten cancer-related genes. mRNA knockdown was measured by quantitative RT-PCR in cell-based assays, revealing that over 60% of siRNA sequences designed by DSIR silenced their target genes by at least 70%. Silencing efficacy was sustained even when low siRNA concentrations were used. This systematic analysis revealed in particular that, for a subset of genes, the efficiency of siRNA constructs significantly increases when the sequence is located closer to the 5′-end of the target gene coding sequence, suggesting the distance to the 5′-end as a new feature for siRNA potency prediction. A new version of DSIR incorporating these new findings, as well as the list of validated siRNA against the tested cancer genes, has been made available on the web (http://biodev.extra.cea.fr/DSIR).     

Ensemble-Based Network Aggregation Improves the Accuracy of Gene Network Reconstruction

Reverse engineering approaches to constructing gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings, such as the discovery of novel drug targets. However, the reliability of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improving the accuracy of network topologies constructed from mRNA expression data. To evaluate the performances of different approaches, we created dozens of simulated networks from combinations of gene-set sizes and sample sizes and also tested our methods on three Escherichia coli datasets. We demonstrate that the ensemble-based network aggregation approach can be used to effectively integrate GRNs constructed from different studies – producing more accurate networks. We also apply this approach to building a network from epithelial mesenchymal transition (EMT) signature microarray data and identify hub genes that might be potential drug targets. The R code used to perform all of the analyses is available in an R package entitled “ENA”, accessible on CRAN (http://cran.r-project.org/web/packages/ENA/).     

The Principal Role of Ku in Telomere Length Maintenance Is Promotion of Est1 Association with Telomeres

Telomere length is tightly regulated in cells that express telomerase. The Saccharomyces cerevisiae Ku heterodimer, a DNA end-binding complex, positively regulates telomere length in a telomerase-dependent manner. Ku associates with the telomerase RNA subunit TLC1, and this association is required for TLC1 nuclear retention. Ku–TLC1 interaction also impacts the cell-cycle-regulated association of the telomerase catalytic subunit Est2 to telomeres. The promotion of TLC1 nuclear localization and Est2 recruitment have been proposed to be the principal role of Ku in telomere length maintenance, but neither model has been directly tested. Here we study the impact of forced recruitment of Est2 to telomeres on telomere length in the absence of Ku’s ability to bind TLC1 or DNA ends. We show that tethering Est2 to telomeres does not promote efficient telomere elongation in the absence of Ku–TLC1 interaction or DNA end binding. Moreover, restoration of TLC1 nuclear localization, even when combined with Est2 recruitment, does not bypass the role of Ku. In contrast, forced recruitment of Est1, which has roles in telomerase recruitment and activation, to telomeres promotes efficient and progressive telomere elongation in the absence of Ku–TLC1 interaction, Ku DNA end binding, or Ku altogether. Ku associates with Est1 and Est2 in a TLC1-dependent manner and enhances Est1 recruitment to telomeres independently of Est2. Together, our results unexpectedly demonstrate that the principal role of Ku in telomere length maintenance is to promote the association of Est1 with telomeres, which may in turn allow for efficient recruitment and activation of the telomerase holoenzyme.     

Genomic Target Database (GTD): A database of potential targets in human pathogenic bacteria

A Genomic Target Database (GTD) has been developed having putative genomic drug targets for human bacterial pathogens. The selected pathogens are either drug resistant or vaccines are yet to be developed against them. The drug targets have been identified using subtractive genomics approaches and these are subsequently classified into

1. Drug targets in pathogen specific unique metabolic pathways,
2. Drug targets in host-pathogen common metabolic pathways, and
3. Membrane localized drug targets.

HTML code is used to link each target to its various properties and other available public resources. Essential resources and tools for subtractive genomic analysis, sub-cellular localization, vaccine and drug designing are also mentioned. To the best of authors knowledge, no such database (DB) is presently available that has listed metabolic pathways and membrane specific genomic drug targets based on subtractive genomics. Listed targets in GTD are readily available resource in developing drug and vaccine against the respective pathogen, its subtypes, and other family members. Currently GTD contains 58 drug targets for four pathogens. Shortly, drug targets for six more pathogens will be listed.

Availability

GTD is available at IIOAB website http://www.iioab.webs.com/GTD.htm. It can also be accessed at http://www.iioabdgd.webs.com.GTD is free for academic research and non-commercial use only. Commercial use is strictly prohibited without prior permission from IIOAB.