Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

BACKGROUND: Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. RESULTS: S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. CONCLUSIONS: This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.Published versio

The transcriptional response of Caenorhabditis elegans to ivermectin exposure identifies novel genes involved in the response to reduced food intake

We have examined the transcriptional response of Caenorhabditis elegans following exposure to the anthelmintic drug ivermectin (IVM) using whole genome microarrays and real-time QPCR. Our original aim was to identify candidate molecules involved in IVM metabolism and/or excretion. For this reason the IVM tolerant strain, DA1316, was used to minimise transcriptomic changes related to the phenotype of drug exposure. However, unlike equivalent work with benzimidazole drugs, very few of the induced genes were members of xenobiotic metabolising enzyme families. Instead, the transcriptional response was dominated by genes associated with fat mobilization and fatty acid metabolism including catalase, esterase, and fatty acid CoA synthetase genes. This is consistent with the reduction in pharyngeal pumping, and consequential reduction in food intake, upon exposure of DA1316 worms to IVM. Genes with the highest fold change in response to IVM exposure, cyp-37B1, mtl-1 and scl-2, were comparably up-regulated in response to short–term food withdrawal (4 hr) independent of IVM exposure, and GFP reporter constructs confirm their expression in tissues associated with fat storage (intestine and hypodermis). These experiments have serendipitously identified novel genes involved in an early response of C. elegans to reduced food intake and may provide insight into similar processes in higher organisms

Systemic similarity analysis of compatibility drug-induced multiple pathway patterns _in vivo_

A major challenge in post-genomic research is to understand how physiological and pathological phenotypes arise from the networks of expressed genes and to develop powerful tools for translating the information exchanged between gene and the organ system networks. Although different expression modules may contribute independently to different phenotypes, it is difficult to interpret microarray experimental results at the level of single gene associations. The global effects and response pathways of small molecules in cells have been investigated, but the quantitative details of the activation mechanisms of multiple pathways _in vivo_ are not well understood. Similar response networks indicate similar modes of action, and gene networks may appear to be similar despite differences in the behaviour of individual gene groups. Here we establish the method for assessing global effect spectra of the complex signaling forms using Global Similarity Index (GSI) in cosines vector included angle. Our approach provides quantitative multidimensional measures of genes expression profile based on drug-dependent phenotypic alteration _in vivo_. These results make a starting point for identifying relationships between GSI at the molecular level and a step toward phenotypic outcomes at a system level to predict action of unknown compounds and any combination therapy

Differential expression of skeletal muscle genes following administration of clenbuterol to exercised horses.

BackgroundClenbuterol, a beta2-adrenergic receptor agonist, is used therapeutically to treat respiratory conditions in the horse. However, by virtue of its mechanism of action it has been suggested that clenbuterol may also have repartitioning affects in horses and as such the potential to affect performance. Clenbuterol decreases the percent fat and increases fat-free mass following high dose administration in combination with intense exercise in horses. In the current study, microarray analysis and real-time PCR were used to study the temporal effects of low and high dose chronic clenbuterol administration on differential gene expression of several skeletal muscle myosin heavy chains, genes involved in lipid metabolism and the β2-adrenergic receptor. The effect of clenbuterol administration on differential gene expression has not been previously reported in the horse, therefore the primary objective of the current study was to describe clenbuterol-induced temporal changes in gene expression following chronic oral administration of clenbuterol at both high and low doses.ResultsSteady state clenbuterol concentrations were achieved at approximately 50 h post administration of the first dose for the low dose regimen and at approximately 18-19 days (10 days post administration of 3.2 μg/kg) for the escalating dosing regimen. Following chronic administration of the low dose (0.8 μg/kg BID) of clenbuterol, a total of 114 genes were differentially expressed, however, none of these changes were found to be significant following FDR adjustment of the p-values. A total of 7,093 genes were differentially expressed with 3,623 genes up regulated and 3,470 genes down regulated following chronic high dose administration. Of the genes selected for further study by real-time PCR, down-regulation of genes encoding myosin heavy chains 2 and 7, steroyl CoA desaturase and the β2-adrenergic receptor were noted. For most genes, expression levels returned towards baseline levels following cessation of drug administration.ConclusionThis study showed no evidence of modified gene expression following chronic low dose administration of clenbuterol to horses. However, following chronic administration of high doses of clenbuterol alterations were noted in transcripts encoding various myosin heavy chains, lipid metabolizing enzymes and the β2-adrenergic receptor

Increased expression of a microRNA correlates with anthelmintic resistance in parasitic nematodes

Resistance to anthelmintic drugs is a major problem in the global fight against parasitic nematodes infecting humans and animals. While previous studies have identified mutations in drug target genes in resistant parasites, changes in the expression levels of both targets and transporters have also been reported. The mechanisms underlying these changes in gene expression are unresolved. Here, we take a novel approach to this problem by investigating the role of small regulatory RNAs in drug resistant strains of the important parasite Haemonchus contortus. microRNAs (miRNAs) are small (22 nt) non-coding RNAs that regulate gene expression by binding predominantly to the 3′ UTR of mRNAs. Changes in miRNA expression have been implicated in drug resistance in a variety of tumor cells. In this study, we focused on two geographically distinct ivermectin resistant strains of H. contortus and two lines generated by multiple rounds of backcrossing between susceptible and resistant parents, with ivermectin selection. All four resistant strains showed significantly increased expression of a single miRNA, hco-miR-9551, compared to the susceptible strain. This same miRNA is also upregulated in a multi-drug-resistant strain of the related nematode Teladorsagia circumcincta. hco-miR-9551 is enriched in female worms, is likely to be located on the X chromosome and is restricted to clade V parasitic nematodes. Genes containing predicted binding sites for hco-miR-9551 were identified computationally and refined based on differential expression in a transcriptomic dataset prepared from the same drug resistant and susceptible strains. This analysis identified three putative target mRNAs, one of which, a CHAC domain containing protein, is located in a region of the H. contortus genome introgressed from the resistant parent. hco-miR-9551 was shown to interact with the 3′ UTR of this gene by dual luciferase assay. This study is the first to suggest a role for miRNAs and the genes they regulate in drug resistant parasitic nematodes. miR-9551 also has potential as a biomarker of resistance in different nematode species

Transcriptomic effects of the non-steroidal anti-inflammatory drug Ibuprofen in the marine bivalve Mytilus galloprovincialis Lam

The transcriptomic effects of Ibuprofen (IBU) in the digestive gland tissue of Mytilus galloprovincialis Lam. specimens exposed at low environmental concentrations (250 ng L-1) are presented. Using a 1.7 K feature cDNA microarray along with linear models and empirical Bayes statistical methods 225 differentially expressed genes were identified in mussels treated with IBU across a 15-day period. Transcriptional dynamics were typical of an adaptive response with a peak of gene expression change at day 7 (177 features, representing about 11% of sequences available for analysis) and an almost full recovery at the end of the exposure period. Functional genomics by means of Gene Ontology term analysis unraveled typical mussel stress responses i.e. aminoglycan (chitin) metabolic processes but also more specific effects such as the regulation of NF-kappa B transcription factor activity. (C) 2016 Elsevier Ltd. All rights reserved

Enhanced anticancer activity of a combination of docetaxel and Aneustat (OMN54) in a patient-derived, advanced prostate cancer tissue xenograft model.

The current first-line treatment for advanced metastatic prostate cancer, i.e. docetaxel-based therapy, is only marginally effective. The aim of the present study was to determine whether such therapy can be improved by combining docetaxel with Aneustat (OMN54), a multivalent botanical drug candidate shown to have anti-prostate cancer activity in preliminary in vitro experiments, which is currently undergoing a Phase-I Clinical Trial. Human metastatic, androgen-independent C4-2 prostate cancer cells and NOD-SCID mice bearing PTEN-deficient, metastatic and PSA-secreting, patient-derived subrenal capsule LTL-313H prostate cancer tissue xenografts were treated with docetaxel and Aneustat, alone and in combination. In vitro, Aneustat markedly inhibited C4-2 cell replication in a dose-dependent manner. When Aneustat was combined with docetaxel, the growth inhibitions of the drugs were essentially additive. In vivo, however, the combination of docetaxel and Aneustat enhanced anti-tumor activity synergistically and very markedly, without inducing major host toxicity. Complete growth inhibition and shrinkage of the xenografts could be obtained with the combined drugs as distinct from the drugs on their own. Analysis of the gene expression of the xenografts using microarray indicated that docetaxel + Aneustat led to expanded anticancer activity, in particular to targeting of cancer hallmarks that were not affected by the single drugs. Our findings, obtained with a highly clinically relevant prostate cancer model, suggest, for the first time, that docetaxel-based therapy of advanced human prostate cancer may be improved by combining docetaxel with Aneustat