Abstract
BackgroundThe development of new therapies for orphan genetic diseases represents an extremely important medical and social challenge. Drug repositioning, i.e. finding new indications for approved drugs, could be one of the most cost- and time-effective strategies to cope with this problem, at least in a subset of cases. Therefore, many computational approaches based on the analysis of high throughput gene expression data have so far been proposed to reposition available drugs. However, most of these methods require gene expression profiles directly relevant to the pathologic conditions under study, such as those obtained from patient cells and/or from suitable experimental models. In this work we have developed a new approach for drug repositioning, based on identifying known drug targets showing conserved anti-correlated expression profiles with human disease genes, which is completely independent from the availability of ‘ad hoc’ gene expression data-sets.ResultsBy analyzing available data, we provide evidence that the genes displaying conserved anti-correlation with drug targets are antagonistically modulated in their expression by treatment with the relevant drugs. We then identified clusters of genes associated to similar phenotypes and showing conserved anticorrelation with drug targets. On this basis, we generated a list of potential candidate drug-disease associations. Importantly, we show that some of the proposed associations are already supported by independent experimental evidence.ConclusionsOur results support the hypothesis that the identification of gene clusters showing conserved anticorrelation with drug targets can be an effective method for drug repositioning and provide a wide list of new potential drug-disease associations for experimental validation.
Highlights
The development of new therapies for orphan genetic diseases represents an extremely important medical and social challenge
When in these cases the centre genes were associated to mendelian disorders, we found that the MimMiner scores calculated between the disease associated to the centre gene and those associated to the other genes of Conserved Anticorrelated Gene Clusters (CAGC) never reached the 0.4 similarity threshold
These results indicate that, the genes that compose a CAGC and the centre of the same clusters frequently work in the same functional modules, the phenotypic consequences of a mutation of the centre gene are different from the consequences of a mutation of the genes composing the corresponding CAGC
Summary
The development of new therapies for orphan genetic diseases represents an extremely important medical and social challenge. Many computational approaches based on the analysis of high throughput gene expression data have so far been proposed to reposition available drugs Most of these methods require gene expression profiles directly relevant to the pathologic conditions under study, such as those obtained from patient cells and/or from suitable experimental models. Our approach is based uniquely on the search for conserved anti-correlation between known drug targets and human disease genes, performed on public microarray databases On this basis, we propose new potential candidate drug targets and drugs for rare human diseases for which no specific gene expression data are available
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