Identification of drug target candidates of the swine pathogen Actinobacillus pleuropneumoniae by construction of protein-protein interaction network

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

Protein Integrated Network Analysis to Reveal Potential Drug Targets Against Extended Drug-Resistant Mycobacterium tuberculosis XDR1219

The reconstruction and analysis of the protein-protein interaction (PPI) network is a powerful approach to understand the complex biological and molecular functions in normal and disease states of the cell. The interactome of most organisms is largely unidentified except some model organisms. The current study focused on the construction of PPI network for the human pathogen Mycobacterium tuberculosis (MTB)-resistant strain XDR1219 using computational methods. In this work, a bioinformatics approach was employed to reveal potential drug targets. The pipeline adopted the combination of an extensive integrated network analysis that led to identify 22 key proteins involved in drug resistance, resistant metabolic pathways, virulence, pathogenesis and persistency of the infection. The MTB XDR1219 interactome consists of 11,383 non-redundant PPIs among 1499 proteins covering 38% of the entire MTB XDR1219 proteome. The overall quality of the network was assessed and topological parameters of the PPI were calculated. The predicted interactions were functionally annotated and their relevance was assessed with the functional similarity. The study attempts to present the interactome of previously unidentified MTB XDR1219 and revealed potential drug targets that can be further explored by scientific community.

Protein-protein interaction network and potential drug target candidates of Streptococcus suis

AIMS

This study aimed to explore potential drug targets of Streptococcus suis at the system level.

METHODS AND RESULTS

A homologous protein mapping method was used in the construction of a protein-protein interaction (PPI) network of S. suis, which presented 1147 non-redundant interaction pairs among 286 proteins. The parameters of PPI networks were calculated and showed scale-free network properties. In all, 41 possibly essential proteins identified from 47 highly connected proteins were selected as potential drug target candidates. Of these proteins, 30 were already regarded as drug targets in other bacterial species. Six transporters with high connections to other functional proteins were identified as probably not essential but important functional proteins. Afterward, the subnetwork centred with cell division protein FtsZ was used in confirming the PPI network through bacterial two-hybrid analysis.

CONCLUSIONS

The predicted PPI network covers 13·04% of the proteome in S. suis. The selected 41 potential drug target candidates are conserved between S. suis and several model bacteria.

SIGNIFICANCE AND IMPACT OF THE STUDY

The predictions included proteins known to be drug targets, and a verifying experiment confirmed the reliability of predicted interactions. This work is the first to present systematic computational PPI data for S. suis and provides potential drug targets, which are valuable in exploring novel anti-streptococcus drugs.

Common Genes Involved in Autophagy, Cellular Senescence and the Inflammatory Response in AMD and Drug Discovery Identified via Biomedical Databases

Purpose

Retinal pigment epithelial cell autophagy dysfunction, cellular senescence, and the retinal inflammatory response are key pathogenic factors in age-related macular degeneration (AMD), which has been reviewed in our previously work in 2019. This study aims to identify genes collectively involved in these three biological processes and target drugs in AMD.

Methods

The pubmed2ensembl database was used to perform text mining. The GeneCodis database was applied to analyze gene ontology biological process and the KEGG pathway. The STRING database was used to analyze protein–protein interaction analysis and hub genes were identified by the Cytoscape software. The Drug Gene Interaction Database was used to perform drug–gene interactions.

Results

We identified 62 genes collectively involved in AMD, autophagy, cellular senescence, and inflammatory response, 19 biological processes including 42 genes, 11 enriched KEGG pathways including 37 genes, and 12 hub genes step by step via the above biomedical databases. Finally, five hub genes (IL-6, VEGF-A, TP53, IL-1β, and transforming growth factor [TGF]-β1) and their specific interaction modes were identified, corresponding with 24 target drugs with therapeutic potential for AMD.

Conclusions

IL-6, VEGF-A, TP53, IL-1β, and TGF-β1 are pivotal in autophagy, cellular senescence, and the inflammatory response in AMD, corresponding with 24 drugs with therapeutic potential for AMD, providing definite molecular mechanisms for further research and new possibilities for AMD treatment in the future.

Translational Relevance

IL-6, VEGF-A, TP53, IL-1β, and TGF-β1 may be new targets for AMD gene therapy and drug development.

Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine–Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by “druggability” prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types.