Dataset Information

Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data.

ABSTRACT: Gene expression is perturbated by drugs to different extent. Analyzing genes whose expression is frequently regulated by drugs would be useful for the screening of candidate therapeutic targets and genes implicated in side effect. Here, we obtained the differential expression number (DEN) for genes profiled in Affymetrix microarrays from the Connectivity Map project, and conducted systemic comparative computational analysis between high DEN genes and other genes. Results indicated that genes with higher down-/up-regulation number (down_h/up_h) tended to be clustered in genome, and have lower homologous gene number, higher SNP density and more disease-related SNP. Down_h and up_h were significantly enriched in cancer related pathways, while genes with lower down-/up-regulation number (down_l/up_l) were mainly involved in the development of nervous system diseases. Besides, up_h had lower interaction network degree, later developmental stage to express, higher tissue expression specificity than up_l, while down_h showed reversed tendency in comparison with down_l. Together, our analysis suggests that genes frequently regulated by drugs are more likely to be associated with disease-related functions, but the extensive activation of conserved and widely expressed genes by drugs is disfavored.

SUBMITTER: Liu X

PROVIDER: S-EPMC5456389 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data.

Liu Xinhua X Zeng Pan P Cui Qinghua Q Zhou Yuan Y

PloS one 20170602 6

Gene expression is perturbated by drugs to different extent. Analyzing genes whose expression is frequently regulated by drugs would be useful for the screening of candidate therapeutic targets and genes implicated in side effect. Here, we obtained the differential expression number (DEN) for genes profiled in Affymetrix microarrays from the Connectivity Map project, and conducted systemic comparative computational analysis between high DEN genes and other genes. Results indicated that genes wit ...[more]

PMID: 28575118

Similar Datasets

Project description:BackgroundThe coronavirus disease 2019 (COVID-19) has become a worldwide public health crisis. At present, there are no effective antiviral drugs to treat COVID-19. Although some vaccines have been developed, late-stage clinical trials that allow licensure by regulatory agencies are still needed. Previous reports have indicated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and SARS-CoV are highly homologous and both use angiotensin-converting enzyme 2 (ACE2) as the receptor to enter cells, and SARS-CoV infection reduces the ACE2 expression in the lung. Therefore, the analysis of genes co-expressed with ACE2 in the lung may uncover the underlying mechanism of COVID-19. Finally, we used the Connectivity map (Cmap) database to search for candidate drugs using transcriptome profiles of patients with COVID-19.MethodBased on the differentially expressed genes (DEGs), indicated by the expression of RNAs isolated from bronchoalveolar lavage fluid (BALF) cells of patients with COVID-19, we performed functional enrichment analysis and hub gene cluster analysis. Furthermore, we identified genes co-expressed with ACE2 in healthy lung samples and analyzed the significant genes. Additionally, to identify several candidate drugs for the treatment of COVID-19, we queried Cmap using DEGs and genes co-expressed with ACE2.Results and conclusionThe up-regulated genes in the BALF cells of patients with COVID-19 are related to viral mRNA translation. The down-regulated genes are related to immune response functions. Genes positively correlated with ACE2 are related to immune defense and those negatively correlated are enriched in synaptic transmission functions. The results reflected prosperous viral proliferation and immune dysfunction in patients. Furthermore, ACE2 may not only mediate viral entrance, but also play an important role in immune defense. By using Cmap with transcriptome profiles of patients with COVID-19, we identified candidate drugs for the treatment of COVID-19, such as amantadine and acyclovir.

Project description:BackgroundDrug repurposing, the process of identifying additional therapeutic uses for existing drugs, has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials.ResultsIn this study, we propose a novel framework based on clinical connectivity mapping for drug repurposing to analyze therapeutic effects of drugs on diseases. We firstly establish clinical drug effect vectors (i.e., drug-laboratory results associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data, then establish clinical disease sign vectors (i.e., disease-laboratory results associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Eventually, a repurposing possibility score for each drug-disease pair is computed by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. During the experiment, we comprehensively evaluate 392 drugs for 6 important chronic diseases (include asthma, coronary heart disease, congestive heart failure, heart attack, type 2 diabetes, and stroke). The experiment results not only reflect known associations between diseases and drugs, but also include some hidden drug-disease associations. The code for this paper is available at: https://github.com/HoytWen/CCMDR CONCLUSIONS: The proposed clinical connectivity map framework uses laboratory results found from electronic clinical information to bridge drugs and diseases, which make their relations explainable and has better translational power than existing computational methods. Experimental results demonstrate the effectiveness of our proposed framework, further case analysis also proves our method can be used to repurposing existing drugs opportunities.

Dataset Information

Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data.

Publications

Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets