Abstract
BackgroundGenome-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.ResultsS. 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.ConclusionsThis 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.
Highlights
Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level
This in turn can lead to more effective treatment strategies [53,54]
Wild-type S. cerevisiae cells were treated with FL and harvested at either varying exposure times (ET) or concentrations in aerobic, batch culture conditions (Figure 3A)
Summary
Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. RNA microarrays have had a major impact on both experimental and computational biology They have played a role in predicting molecular targets and bioactive compound modes-of-action [1,2,3], they have helped identify genes responsible for disease- and environmentalinduced phenotypes [4,5,6]. Supervised learning methods like support vector machines have been widely used to develop statistical methods that predict drug-protein interactions [18,19,20,21,22] These methods employ training networks, constructed from protein-ligand binding data, known protein sequences, compound similarity scores, and in the case of Campillos et al, known drug side effects. These patterns are typically taken as known input, whereas in SSEM-Lasso, they are learned from the microarray data
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