Screening novel drug candidates for Alzheimer’s disease by an integrated network and transcriptome analysis

Abstract

Motivation Alzheimer’s disease (AD) is a serious degenerative brain disease and the most common cause of dementia. The current available drugs for AD provide symptomatic benefit, but there is no effective drug to cure the disease. The emergence of large-scale genomic, pharmacological data provides new opportunities for drug discovery and drug repositioning as a promising strategy in searching novel drug for AD. Results In this study, we took advantage of our increasing understanding based on systems biology approaches on the pathway and network levels and perturbation data sets from the Library of Integrated Network-Based Cellular Signatures (LINCS) to introduce a systematic computational process to discover new drugs implicated in AD. Firstly, we collected 561 genes that have reported to be risk genes of AD, and applied functional enrichment analysis on these genes. Then, by quantifying proximity between 5595 molecule drugs and AD based on human interactome, we filtered out 1092 drugs that were proximal to the disease. We further performed an Inverted Gene Set Enrichment analysis on these drug candidates, which allowed us to estimate effect of perturbations on gene expression and identify 24 potential drug candidates for AD treatment. Results from this study also provided insights for understanding the molecular mechanisms underlying AD. As a useful systematic method, our approach can also be used to identify efficacious therapies for other complex diseases. Supplementary information Supplementary data are available at Bioinformatics online.

Comprehensive Network Analysis Identified SIRT7, NTRK2, and CHI3L1 as New Potential Markers for Intervertebral Disc Degeneration

“…To further determine the potential drug targets of key IDD genes, following Wang et al [ 18 ], we determined the network distance between these 3 key genes and 5490 drugs on DrugBank ( Figure 6(a) ), and found that the distance between the three key genes and the drug was shorter than that of the random background. A total of two drugs were determined according to a global FDR < 0.05 ( Table 2 ).…”

“…These drug-target genes and IDDG genes were mapped to the STRING V11.0 [ 16 ] database to obtain gene interaction information, and a drug-gene-IDDG network was constructed. As previously described by Wang et al [ 18 ], the shortest path of drugs to IDDG was calculated for identifying potentially related drugs to IDDG.…”

Drug Repurposing for COVID-19 Treatment by Integrating Network Pharmacology and Transcriptomics

“…Genes (nodes) with interaction (links) constructed a network graph of PPI, while the interaction between two nodes was undirected and unweighted. Here, a proximity score was defined by the average shortest path length between the drug target genes and their nearest disease proteins in the context of PPI to quantify the therapeutic effect of drugs [28,29]. Given the set of COVID-19 related genes sourced from SARS-CoV-2 proteins (S), the group of drug target genes (T), the shortest distance between two genes in the PPI network d(s, t) where s∈S and t∈T (Equation (1)),…”

Diagnostic biomarkers and potential drug targets for coronary artery disease as revealed by systematic analysis of lncRNA characteristics

“…To observe potential drugs targeting LRM-CAD, 5,490 druggene interaction data were obtained from Drugbank (31) using the method previously described by Peng et al (32). These proteins and genes in LRM-CAD were mapped to the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database ( 33) to construct drug-protein and protein-protein interaction network (DPPI) and used to define the degree of node of LRM-CAD-related gene set in PPI, T, and drug target gene set.…”