Predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profiling
Sepsis-induced acute respiratory distress syndrome (ARDS) remains a major threat to human health without effective therapeutic drugs. Previous studies demonstrated the power of gene expression profiling to reveal pathological changes associated with sepsis-induced ARDS. However, there is still a lac...
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doaj-1cddca03a06d40748ff5674319d973fb2021-08-09T15:50:08ZengTaylor & Francis GroupBioengineered2165-59792165-59872021-01-011211369138010.1080/21655979.2021.19179811917981Predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profilingJiawei Ma0Qianqian Li1Dandan Ji2Liang Luo3Lei Hong4The Affiliated Wuxi No.2 People’s Hospital of Nanjing Medical UniversityThe Affiliated Wuxi No.2 People’s Hospital of Nanjing Medical UniversityThe Affiliated Wuxi No.2 People’s Hospital of Nanjing Medical UniversityThe Affiliated Wuxi No.2 People’s Hospital of Nanjing Medical UniversityThe Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical UniversitySepsis-induced acute respiratory distress syndrome (ARDS) remains a major threat to human health without effective therapeutic drugs. Previous studies demonstrated the power of gene expression profiling to reveal pathological changes associated with sepsis-induced ARDS. However, there is still a lack of systematic data mining framework for identifying potential targets for treatment. In this study, we demonstrated the feasibility of druggable targets prediction based on gene expression data. Through the functional enrichment analysis of microarray-based expression profiles between sepsis-induced ARDS and non-sepsis ARDS samples, we revealed genes involved in anti-microbial infection immunity were significantly altered in sepsis-induced ARDS. Protein–protein interaction (PPI) network analysis highlighted TOP2A gene as the key regulator in the dysregulated gene network of sepsis-induced ARDS. We were also able to predict several therapeutic drug candidates for sepsis-induced ARDS using Connectivity Map (Cmap) database, among which doxorubicin was identified to interact with TOP2A with a high affinity similar to its endogenous ligand. Overall, our findings suggest that doxorubicin could be a potential therapeutic for sepsis-induced ARDS by targeting TOP2A, which requires further investigation and validation. The whole study relies on publicly available dataset and publicly accessible database or bioinformatic tools for data mining. Therefore, our study benchmarks a workflow for druggable target prediction which can be widely applicable in the search of targets in other pathological conditions.http://dx.doi.org/10.1080/21655979.2021.1917981sepsis-induced ardsfunctional enrichment analysisprotein-protein interaction (ppi)connectivity mapmolecular docking |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiawei Ma Qianqian Li Dandan Ji Liang Luo Lei Hong |
spellingShingle |
Jiawei Ma Qianqian Li Dandan Ji Liang Luo Lei Hong Predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profiling Bioengineered sepsis-induced ards functional enrichment analysis protein-protein interaction (ppi) connectivity map molecular docking |
author_facet |
Jiawei Ma Qianqian Li Dandan Ji Liang Luo Lei Hong |
author_sort |
Jiawei Ma |
title |
Predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profiling |
title_short |
Predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profiling |
title_full |
Predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profiling |
title_fullStr |
Predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profiling |
title_full_unstemmed |
Predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profiling |
title_sort |
predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profiling |
publisher |
Taylor & Francis Group |
series |
Bioengineered |
issn |
2165-5979 2165-5987 |
publishDate |
2021-01-01 |
description |
Sepsis-induced acute respiratory distress syndrome (ARDS) remains a major threat to human health without effective therapeutic drugs. Previous studies demonstrated the power of gene expression profiling to reveal pathological changes associated with sepsis-induced ARDS. However, there is still a lack of systematic data mining framework for identifying potential targets for treatment. In this study, we demonstrated the feasibility of druggable targets prediction based on gene expression data. Through the functional enrichment analysis of microarray-based expression profiles between sepsis-induced ARDS and non-sepsis ARDS samples, we revealed genes involved in anti-microbial infection immunity were significantly altered in sepsis-induced ARDS. Protein–protein interaction (PPI) network analysis highlighted TOP2A gene as the key regulator in the dysregulated gene network of sepsis-induced ARDS. We were also able to predict several therapeutic drug candidates for sepsis-induced ARDS using Connectivity Map (Cmap) database, among which doxorubicin was identified to interact with TOP2A with a high affinity similar to its endogenous ligand. Overall, our findings suggest that doxorubicin could be a potential therapeutic for sepsis-induced ARDS by targeting TOP2A, which requires further investigation and validation. The whole study relies on publicly available dataset and publicly accessible database or bioinformatic tools for data mining. Therefore, our study benchmarks a workflow for druggable target prediction which can be widely applicable in the search of targets in other pathological conditions. |
topic |
sepsis-induced ards functional enrichment analysis protein-protein interaction (ppi) connectivity map molecular docking |
url |
http://dx.doi.org/10.1080/21655979.2021.1917981 |
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