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Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine.

ABSTRACT: The coronavirus disease 2019 (COVID-19) pandemic causes many morbidity and mortality cases. Despite several developed vaccines and antiviral therapies, some patients experience severe conditions that need intensive care units (ICU); therefore, precision medicine is necessary to predict and treat these patients using novel biomarkers and targeted drugs. In this study, we proposed a multi-level biological network analysis framework to identify key genes via protein-protein interaction (PPI) network analysis as well as survival analysis based on differentially expressed genes (DEGs) in leukocyte transcriptomic profiles, discover novel biomarkers using microRNAs (miRNA) from regulatory network analysis, and provide candidate drugs targeting the key genes using drug-gene interaction network and structural analysis. The results show that upregulated DEGs were mainly enriched in cell division, cell cycle, and innate immune signaling pathways. Downregulated DEGs were primarily concentrated in the cellular response to stress, lysosome, glycosaminoglycan catabolic process, and mature B cell differentiation. Regulatory network analysis revealed that hsa-miR-6792-5p, hsa-let-7b-5p, hsa-miR-34a-5p, hsa-miR-92a-3p, and hsa-miR-146a-5p were predicted biomarkers. CDC25A, GUSB, MYBL2, and SDAD1 were identified as key genes in severe COVID-19. In addition, drug repurposing from drug-gene and drug-protein database searching and molecular docking showed that camptothecin and doxorubicin were candidate drugs interacting with the key genes. In conclusion, multi-level systems biology analysis plays an important role in precision medicine by finding novel biomarkers and targeted drugs based on key gene identification.

SUBMITTER: Sagulkoo P

PROVIDER: S-EPMC9319133 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In tropical and subtropical areas, malaria stands as a profound public health challenge, causing an estimated 247 million cases worldwide annually. Given the absence of a viable vaccine, the timely and effective treatment of malaria remains a critical priority. However, the growing resistance of parasites to currently utilized drugs underscores the critical need for the identification of new antimalarial therapies. Here, we aimed to identify potential new drug candidates against Plasmodium falciparum, the main causative agent of malaria, by analyzing the transcriptomes of different life stages of the parasite and identifying highly expressed genes. We searched for genes that were expressed in all stages of the parasite's life cycle, including the asexual blood stage, gametocyte stage, liver stage, and sexual stages in the insect vector, using transcriptomics data from publicly available databases. From this analysis, we found 674 overlapping genes, including 409 essential ones. By searching through drug target databases, we discovered 70 potential drug targets and 75 associated bioactive compounds. We sought to expand this analysis to similar compounds to known drugs. So, we found a list of 1557 similar compounds, which we predicted as actives and inactives using previously developed machine learning models against five life stages of Plasmodium spp. From this analysis, two compounds were selected, and the reactions were experimentally evaluated. The compounds HSP-990 and silvestrol aglycone showed potent inhibitory activity at nanomolar concentrations against the P. falciparum 3D7 strain asexual blood stage. Moreover, silvestrol aglycone exhibited low cytotoxicity in mammalian cells, transmission-blocking potential, and inhibitory activity comparable to those of established antimalarials. These findings warrant further investigation of silvestrol aglycone as a potential dual-acting antimalarial and transmission-blocking candidate for malaria control.

Dataset Information

Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine.

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