In-silico discovery of type-2 diabetes-causing host key genes that are associated with the complexity of monkeypox and repurposing common drugs

Abstract

Monkeypox (Mpox) is a major global human health threat after COVID-19. Its treatment becomes complicated with type-2 diabetes (T2D). It may happen due to the influence of both disease-causing common host key genes (cHKGs). Therefore, it is necessary to explore both disease-causing cHKGs to reveal their shared pathogenetic mechanisms and candidate drugs as their common treatments without adverse side effect. This study aimed to address these issues. At first, 3 transcriptomics datasets for each of Mpox and 6 T2D datasets were analyzed and found 52 common host differentially expressed genes (cHDEGs) that can separate both T2D and Mpox patients from the control samples. Then top-ranked six cHDEGs (HSP90AA1, B2M, IGF1R, ALD1HA1, ASS1, and HADHA) were detected as the T2D-causing cHKGs that are associated with the complexity of Mpox through the protein-protein interaction network analysis. Then common pathogenetic processes between T2D and Mpox were disclosed by cHKG-set enrichment analysis with biological processes, molecular functions, cellular components and Kyoto Encyclopedia of Genes and Genomes pathways, and regulatory network analysis with transcription factors and microRNAs. Finally, cHKG-guided top-ranked three drug molecules (tecovirimat, vindoline, and brincidofovir) were recommended as the repurposable common therapeutic agents for both Mpox and T2D by molecular docking. The absorption, distribution, metabolism, excretion, and toxicity and drug-likeness analysis of these drug molecules indicated their good pharmacokinetics properties. The 100-ns molecular dynamics simulation results (root mean square deviation, root mean square fluctuation, and molecular mechanics generalized born surface area) with the top-ranked three complexes ASS1-tecovirimat, ALDH1A1-vindoline, and B2M-brincidofovir exhibited good pharmacodynamics properties. Therefore, the results provided in this article might be important resources for diagnosis and therapies of Mpox patients who are also suffering from T2D.

Keywords: common drugs and toxicity; monkeypox; statistics and bioinformatics analysis; transcriptomics profiles and common host key genes; type-2 diabetes.

Figure 1

A schematic diagram about the link between T2D and Mpox infection.

Figure 2

A graphical representation of the entire workflow.

Figure 3

PPI networks based on (A) STRING and (B) IMEx databases. In both A and B, larger nodes indicate the top-ranked cHDEGs, where brown nodes in both A and B represent the T2D- and Mpox-causing cHKGs in both databases.

Figure 4

The interaction network visualized the relationships between (A) miRNAs and cHKGs and (B) TFs and cHKGs, where larger octagonal nodes indicate cHKGs in both A and B. Two larger circular nodes in A and two larger oval nodes in B indicated by the top-ranked miRNAs and TFs, respectively, represent the key regulators of cHKGs.

Figure 5

Image of drug-target binding affinity matrices. (A) X-axis indicates top-ordered 40 drug agents (out of 583) and Y-axis indicates ordered proposed receptor proteins. (B) X-axis shows top-ranked five Mpox-causing proteins and top-ranked five T2D-causing proteins, and Y-axis indicates top 4 proposed drug agents as the common treatment for both T2D and Mpox.

Figure 6

(A) The RMSD analysis results for a duration of 100-ns simulation with each of the top-ranked three drug-target complexes, (B) the RMSF analysis results for a duration of 100-ns simulation with each of the top-ranked three drug-target complexes, and (C) binding free energy (MM-GBSA) calculations of the top-ranked three drug-target complexes.