Abstract
Identification of primary targets associated with phenotypes can facilitate exploration of the underlying molecular mechanisms of compounds and optimization of the structures of promising drugs. However, the literature reports limited effort to identify the target major isoform of a single known target gene. The majority of genes generate multiple transcripts that are translated into proteins that may carry out distinct and even opposing biological functions through alternative splicing. In addition, isoform expression is dynamic and varies depending on the developmental stage and cell type. To identify target major isoforms, we integrated a breast cancer type-specific isoform coexpression network with gene perturbation signatures in the MCF7 cell line in the Connectivity Map database using the ‘shortest path’ drug target prioritization method. We used a leukemia cancer network and differential expression data for drugs in the HL-60 cell line to test the robustness of the detection algorithm for target major isoforms. We further analyzed the properties of target major isoforms for each multi-isoform gene using pharmacogenomic datasets, proteomic data and the principal isoforms defined by the APPRIS and STRING datasets. Then, we tested our predictions for the most promising target major protein isoforms of DNMT1, MGEA5 and P4HB4 based on expression data and topological features in the coexpression network. Interestingly, these isoforms are not annotated as principal isoforms in APPRIS. Lastly, we tested the affinity of the target major isoform of MGEA5 for streptozocin through in silico docking. Our findings will pave the way for more effective and targeted therapies via studies of drug targets at the isoform level.
Highlights
Identifying the primary target associated with a phenotype can assist with exploration of the underlying molecular mechanisms of compounds and optimization of the structures of promising drugs[1]
The IIC networks built from the separate datasets (AUCCCLE = 0.62, AUCgCSI = 0.72) had lower area under the curve (AUC) than the Comb network (AUCComb = 0.78) (Fig. 2D)
These results indicated that the Comb network improved the performance of the shortest path algorithm
Summary
Identifying the primary target associated with a phenotype can assist with exploration of the underlying molecular mechanisms of compounds and optimization of the structures of promising drugs[1]. Major target isoform protein of a drug transcript diversity and the effect of individual protein isoforms on drug treatment results should be considered an integral part of drug design, development and therapy. Applying existing definitions and algorithms to discover the target major isoform is difficult without considering tissue-specific AS, the interactions between the drug and its target protein and drug-induced downstream changes. Isik et al.[17] integrated perturbed genes from drug-treated cell lines with a human protein-protein interaction network to identify drug target genes They considered the perturbed genes to be closer to the target genes than the other proteins in the network. Inspired by this approach, we integrated isoform coexpression networks with perturbed genes to identify target genes at the isoform level. Our results indicate that understanding the major protein isoform targets of a drug is important for elucidating the mechanism of action (MoA) of that drug
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