Knowledge of psychiatric disease genetics has advanced rapidly during the past decade with the advent of genome-wide association studies (GWAS). However, less progress has been made in harnessing these data to reveal new therapies. Here we propose a framework for drug repositioning by comparing transcriptomes imputed from GWAS data with drug-induced gene expression profiles from the Connectivity Map database and apply this approach to seven psychiatric disorders. We found a number of repositioning candidates, many supported by preclinical or clinical evidence. Repositioning candidates for a number of disorders were also significantly enriched for known psychiatric medications or therapies considered in clinical trials. For example, candidates for schizophrenia were enriched for antipsychotics, while those for bipolar disorder were enriched for both antipsychotics and antidepressants. These findings provide support for the usefulness of GWAS data in guiding drug discovery.
Our knowledge of disease genetics has advanced rapidly during the past decade, with the advent of high-throughput genotyping technologies such as genome-wide association studies (GWAS). However, few methodologies were developed and systemic studies performed to identify novel drug candidates utilizing GWAS data. In this study we focus on drug repositioning, which is a cost-effective approach to shorten the developmental process of new therapies. We proposed a novel framework of drug repositioning by comparing GWAS-imputed transcriptome with drug expression profiles from the Connectivity Map. The approach was applied to 7 psychiatric disorders. We discovered a number of novel repositioning candidates, many of which are supported by preclinical or clinical evidence. We found that the predicted drugs are significantly enriched for known psychiatric medications, or therapies considered in clinical trials. For example, drugs repurposed for schizophrenia are strongly enriched for antipsychotics (p = 4.69E-06), while those repurposed for bipolar disorder are enriched for antipsychotics (p = 2.26E-07) and antidepressants (p = 1.17E-05).These findings provide support to the usefulness of GWAS signals in guiding drug discoveries and the validity of our approach in drug repositioning. We also present manually curated lists of top repositioning candidates for each disorder, which we believe will serve as a useful resource for researchers.
Elementary form of nitrogen (protein), phosphorus, potassium and magnesium contained in 180 different kinds of Chinese medicines are analyzed by means of 14-MeV neutron activation technique. The percent contents of these elements in the Chinese medicines range from 0 to 34.41% (average: 11.73%) for protein, from 0.03 to 37.42% (average: 1.72%) for phosphorus, from 0.12 to 33.22% (average: 2.94%) for potassium and 0.03 to 5.62% (average: 0.43%) for magnesium. Comparison of the present results with the previous measurements for another 66 kinds of Chinese medicines is made.
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