Target Identification Using Homopharma and Network-Based Methods for Predicting Compounds Against Dengue Virus-Infected Cells

Identification of the Hub Genes Involved in Chikungunya Viral Infection

Background Chikungunya virus (CHIKV) infection poses a significant global health threat, necessitating a deeper understanding of its molecular mechanisms for effective management and treatment. This study aimed to understand the molecular and genetic mechanisms of CHIKV infection by analyzing microarray expression data. Methodology National Center for Biotechnology Information (NCBI) GEO2R with an adjusted p-value cut-off of <0.05 and |log2FC ≥ 1.5| was used to identify the differentially expressed genes involved in CHIKV infection using microarray data from the Gene Expression Omnibus (GEO) database, followed by enrichment analysis, protein-protein interaction (PPI) network construction, and, finally, hub gene identification. Results Analysis of the microarray dataset revealed 25 highly significant differentially expressed genes (DEGs), including 21 upregulated and four downregulated genes. PPI network analysis elucidated interactions among these DEGs, with hub genes such as ACTB and CTNNB1 exhibiting central roles. Enrichment analysis identified crucial pathways, including leukocyte transendothelial migration, regulation of actin cytoskeleton, and thyroid hormone signaling, implicating their involvement in CHIKV infection. Furthermore, the study highlights potential therapeutic targets such as ACTB and CTNNB1, which showed significant upregulation in infected cells. Conclusions These findings underscore the complex interplay between viral infection and host cellular processes, shedding light on novel avenues for diagnostic marker discovery and advancing antiviral strategies. In this study, we shed light on the molecular and genetic mechanisms of CHIKV infection and the potential role of ACTB and CTNNB1 genes.

Heterogeneous network propagation with forward similarity integration to enhance drug-target association prediction

Identification of drug-target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug-drug and nine target-target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug-disease associations, and the cosine scores of drug-drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.

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

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.

Alkyne-Tagged Apigenin, a Chemical Tool to Navigate Potential Targets of Flavonoid Anti-Dengue Leads

A flavonoid is a versatile core structure with various cellular, immunological, and pharmacological effects. Recently, flavones have shown anti-dengue activities by interfering with viral translation and replication. However, the molecular target is still elusive. Here we chemically modified apigenin by adding an alkyne moiety into the B-ring hydroxyl group. The alkyne serves as a chemical tag for the alkyne-azide cycloaddition reaction for subcellular visualization. The compound located at the perinuclear region at 1 and 6 h after infection. Interestingly, the compound signal started shifting to vesicle-like structures at 6 h and accumulated at 24 and 48 h after infection. Moreover, the compound treatment in dengue-infected cells showed that the compound restricted the viral protein inside the vesicles, especially at 48 h. As a result, the dengue envelope proteins spread throughout the cells. The alkyne-tagged apigenin showed a more potent efficacy at the EC₅₀ of 2.36 ± 0.22, and 10.55 ± 3.37 µM, respectively, while the cytotoxicities were similar to the original apigenin at the CC₅₀ of 70.34 ± 11.79, and 82.82 ± 11.68 µM, respectively. Molecular docking confirmed the apigenin binding to the previously reported target, ribosomal protein S9, at two binding sites. The network analysis, homopharma, and molecular docking revealed that the estrogen receptor 1 and viral NS1 were potential targets at the late infection stage. The interactions could attenuate dengue productivity by interfering with viral translation and suppressing the viral proteins from trafficking to the cell surface.

Dibromopinocembrin and Dibromopinostrobin Are Potential Anti-Dengue Leads with Mild Animal Toxicity

Dengue infection is one of the most deleterious public health concerns for two-billion world population being at risk. Plasma leakage, hemorrhage, and shock in severe cases were caused by immunological derangement from secondary heterotypic infection. Flavanone, commonly found in medicinal plants, previously showed potential as anti-dengue inhibitors for its direct antiviral effects and suppressing the pro-inflammatory cytokine from dengue immunopathogenesis. Here, we chemically modified flavanones, pinocembrin and pinostrobin, by halogenation and characterized them as potential dengue 2 inhibitors and performed toxicity tests in human-derived cells and in vivo animal model. Dibromopinocembrin and dibromopinostrobin inhibited dengue serotype 2 at the EC₅₀s of 2.0640 ± 0.7537 and 5.8567 ± 0.5074 µM with at the CC₅₀s of 67.2082 ± 0.9731 and >100 µM, respectively. Both of the compounds also showed minimal toxicity against adult C57BL/6 mice assessed by ALT and Cr levels in day one, three, and eight post-intravenous administration. Computational studies suggested the potential target be likely the NS5 methyltransferase at SAM-binding pocket. Taken together, these two brominated flavanones are potential leads for further drug discovery investigation.