Dataset Information

L1000 connectivity map interrogation identifies candidate drugs for repurposing as SARS-CoV-2 antiviral therapies.

ABSTRACT: Adaptive clinical trials are underway to determine the efficacy of potential therapies for COVID-19, with flexibility to include emerging therapies if there is sufficient preclinical evidence for their potential utility. In silico screening of connectivity maps, which link gene expression profiles to libraries of perturbagens, may facilitate the identification of such emerging therapies. The L1000 Connectivity Map is built from samples of transcripts taken from gene expression profiles of cells in various experimental conditions followed by computational inferences of the remainder of the transcriptome. Searching the L1000 Connectivity Map for modulators of a protease that facilitates coronavirus infection identifies plausible candidate drugs for repurposing as antiviral agents against SARS-CoV-2 following further investigation.

SUBMITTER: Sendama W

PROVIDER: S-EPMC7719280 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

L1000 connectivity map interrogation identifies candidate drugs for repurposing as SARS-CoV-2 antiviral therapies.

Sendama Wezi W

Computational and structural biotechnology journal 20201206

Adaptive clinical trials are underway to determine the efficacy of potential therapies for COVID-19, with flexibility to include emerging therapies if there is sufficient preclinical evidence for their potential utility. <i>In silico</i> screening of connectivity maps, which link gene expression profiles to libraries of perturbagens, may facilitate the identification of such emerging therapies. The L1000 Connectivity Map is built from samples of transcripts taken from gene expression profiles of ...[more]

PMID: 33312454

Similar Datasets

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.

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.

Dataset Information

L1000 connectivity map interrogation identifies candidate drugs for repurposing as SARS-CoV-2 antiviral therapies.

Publications

L1000 connectivity map interrogation identifies candidate drugs for repurposing as SARS-CoV-2 antiviral therapies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets