Genetics-directed drug discovery for combating Mycobacterium tuberculosis infection

Drug Repurposing for Japanese Encephalitis Virus Infection by Systems Biology Methods

Japanese encephalitis is a zoonotic disease caused by the Japanese encephalitis virus (JEV). It is mainly epidemic in Asia with an estimated 69,000 cases occurring per year. However, no approved agents are available for the treatment of JEV infection, and existing vaccines cannot control various types of JEV strains. Drug repurposing is a new concept for finding new indication of existing drugs, and, recently, the concept has been used to discover new antiviral agents. Identifying host proteins involved in the progress of JEV infection and using these proteins as targets are the center of drug repurposing for JEV infection. In this study, based on the gene expression data of JEV infection and the phenome-wide association study (PheWAS) data, we identified 286 genes that participate in the progress of JEV infection using systems biology methods. The enrichment analysis of these genes suggested that the genes identified by our methods were predominantly related to viral infection pathways and immune response-related pathways. We found that bortezomib, which can target these genes, may have an effect on the treatment of JEV infection. Subsequently, we evaluated the antiviral activity of bortezomib using a JEV-infected mouse model. The results showed that bortezomib can lower JEV-induced lethality in mice, alleviate suffering in JEV-infected mice and reduce the damage in brains caused by JEV infection. This work provides an agent with new indication to treat JEV infection.

Molecular dynamics simulation and binding free energy studies of novel leads belonging to the benzofuran class inhibitors of Mycobacterium tuberculosis Polyketide Synthase 13

In this work, the binding mechanism of new Polyketide Synthase 13 (Pks13) inhibitors has been studied through molecular dynamics simulation and free energy calculations. The drug Tam1 and its analogs, belonging to the benzofuran class, were submitted to 100 ns simulations, and according to the results obtained for root mean square deviation, all the simulations converged from approximately 30 ns. For the analysis of backbone flotation, the root mean square fluctuations were plotted for the Cα atoms; analysis revealed that the greatest fluctuation occurred in the residues that are part of the protein lid domain. The binding free energy value (ΔG_bind) obtained for the Tam16 lead molecule was of -51.43 kcal/mol. When comparing this result with the ΔG_bind values for the remaining analogs, the drug Tam16 was found to be the highest ranked: this result is in agreement with the experimental results obtained by Aggarwal and collaborators, where it was verified that the IC₅₀ for Tam16 is the smallest necessary to inhibit the Pks13 (IC₅₀ = 0.19 μM). The energy decomposition analysis suggested that the residues which most interact with inhibitors are: Ser1636, Tyr1637, Asn1640, Ala1667, Phe1670, and Tyr1674, from which the greatest energy contribution to Phe1670 was particularly notable. For the lead molecule Tam16, a hydrogen bond with the hydroxyl of the phenol not observed in the other analogs induced a more stable molecular structure. Aggarwal and colleagues reported this hydrogen bonding as being responsible for the stability of the molecule, optimizing its physic-chemical, toxicological, and pharmacokinetic properties.

Facilitating Anti-Cancer Combinatorial Drug Discovery by Targeting Epistatic Disease Genes

Due to synergistic effects, combinatorial drugs are widely used for treating complex diseases. However, combining drugs and making them synergetic remains a challenge. Genetic disease genes are considered a promising source of drug targets with important implications for navigating the drug space. Most diseases are not caused by a single pathogenic factor, but by multiple disease genes, in particular, interacting disease genes. Thus, it is reasonable to consider that targeting epistatic disease genes may enhance the therapeutic effects of combinatorial drugs. In this study, synthetic lethality gene pairs of tumors, similar to epistatic disease genes, were first targeted by combinatorial drugs, resulting in the enrichment of the combinatorial drugs with cancer treatment, which verified our hypothesis. Then, conventional epistasis detection software was used to identify epistatic disease genes from the genome wide association studies (GWAS) dataset. Furthermore, combinatorial drugs were predicted by targeting these epistatic disease genes, and five combinations were proven to have synergistic anti-cancer effects on MCF-7 cells through cell cytotoxicity assay. Combined with the three-dimensional (3D) genome-based method, the epistatic disease genes were filtered and were more closely related to disease. By targeting the filtered gene pairs, the efficiency of combinatorial drug discovery has been further improved.