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Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease.

ABSTRACT: This study aimed to identify potential novel drug candidates and targets for Parkinson's disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

SUBMITTER: Quan P

PROVIDER: S-EPMC8222400 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease.

Quan Pusheng P Wang Kai K Yan Shi S Wen Shirong S Wei Chengqun C Zhang Xinyu X Cao Jingwei J Yao Lifen L

Scientific reports 20210623 1

This study aimed to identify potential novel drug candidates and targets for Parkinson's disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates ...[more]

PMID: 34162989

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Project description:Parkinson's disease (PD) is the second commonest progressive neurodegenerative disease worldwide. Increasing evidence reveals that non-coding RNAs play roles in the pathophysiological process of PD. The notion called competing endogenous RNAs (ceRNAs) network is used to describe the roles of non-coding RNAs. According to this theory, long non-coding RNAs (lncRNAs) act as microRNAs (miRNAs) sponges by miRNA response elements or miRNA binding sites to control the availability of endogenous miRNA for binding to their target mRNAs. This study aimed to construct a ceRNA network in PD, which might have the potential to clarify the pathogenesis of PD. We investigated differential expression (DE) lncRNAs and mRNAs in substantia nigra array data GSE7621 between PD patients and healthy controls from the Gene Expression Omnibus database. And we used starBase 2.0 and miRWalk 2.0 databases to predict miRNAs that have interactions with DElncRNAs and DEmRNAs. Based on DElncRNAs, DEmRNAs and predicted miRNAs, two ceRNA networks were constructed. The first one was based on lncRNA-miRNA interactions and miRNA-mRNA interactions that shared the same miRNAs that we predicted, on which function annotation and PPI analysis were performed to identify hub genes. Hereby the second ceRNA network was generated to explore the core section in the first ceRNA network and was validated in external datasets. As a result, we identified 31 DE lncRNAs and 1,828 DEmRNAs, and finally constructed the first ceRNA network associated with PD, including 9 lncRNAs, 18 miRNAs, and 185 mRNAs. mRNAs in the first ceRNA network focused on autophagy, DNA repair and vesicle transport, which were critical pathological processes in PD. Nineteen hub genes in the first ceRNA network identified through PPI analysis, the second ceRNA network was constructed to annotate the core part of the first one. Moreover, the core subnetwork was validated in external datasets, of which several nodes including FBXL7, PTBP2, and lncRNA NEAT1 were verified. In conclusion, a ceRNA network was constructed based on the differential expression profiles of whole substantia nigra tissues of normal and PD patients, and the network was subsequently identified which revealed its association with autophagy, DNA repair and vesicle transport. The core subnetwork of the ceRNA network was identified and validated in external data. Our findings offered novel insights into the roles of ceRNAs in the pathogenesis of PD and provided promising diagnostic biomarkers.

Project description:BACKGROUND:Alzheimer's disease (AD) is a multifactorial and complex neuropathology that involves impairment of many intricate molecular mechanisms. Despite recent advances, AD pathophysiological characterization remains incomplete, which hampers the development of effective treatments. In fact, currently, there are no effective pharmacological treatments for AD. Integrative strategies such as transcription regulatory network and master regulator analyses exemplify promising new approaches to study complex diseases and may help in the identification of potential pharmacological targets. METHODS:In this study, we used transcription regulatory network and master regulator analyses on transcriptomic data of human hippocampus to identify transcription factors (TFs) that can potentially act as master regulators in AD. All expression profiles were obtained from the Gene Expression Omnibus database using the GEOquery package. A normal hippocampus transcription factor-centered regulatory network was reconstructed using the ARACNe algorithm. Master regulator analysis and two-tail gene set enrichment analysis were employed to evaluate the inferred regulatory units in AD case-control studies. Finally, we used a connectivity map adaptation to prospect new potential therapeutic interventions by drug repurposing. RESULTS:We identified TFs with already reported involvement in AD, such as ATF2 and PARK2, as well as possible new targets for future investigations, such as CNOT7, CSRNP2, SLC30A9, and TSC22D1. Furthermore, Connectivity Map Analysis adaptation suggested the repositioning of six FDA-approved drugs that can potentially modulate master regulator candidate regulatory units (Cefuroxime, Cyproterone, Dydrogesterone, Metrizamide, Trimethadione, and Vorinostat). CONCLUSIONS:Using a transcription factor-centered regulatory network reconstruction we were able to identify several potential molecular targets and six drug candidates for repositioning in AD. Our study provides further support for the use of bioinformatics tools as exploratory strategies in neurodegenerative diseases research, and also provides new perspectives on molecular targets and drug therapies for future investigation and validation in AD.

Project description:Human monocytotropic ehrlichiosis is an emerging tick-borne infection caused by the obligate intracellular pathogen, Ehrlichia chaffeensis. The non-specific symptoms can range from a self-limiting fever to a fatal septic-like syndrome and may be misdiagnosed. The limited treatment choices including doxycycline are effective only in the initiation phase of the infection. It seems that novel therapeutic targets and new vaccine strategies could be effective to control this pathogen. This study is comprised of two major phases. First, the common proteins retrieved through subtractive analysis and potential drug targets were evaluated by subcellular localization, homology prediction, metabolic pathways, druggability, essentiality, protein-protein interaction networks, and protein data bank availability. In the second phase, surface-exposed proteins were assessed based on antigenicity, allergenicity, physiochemical properties, B cell and T cell epitopes, conserved domains, and protein-protein interaction networks. A multi-epitope vaccine was designed and characterized using molecular dockings and immune simulation analysis. Six proteins including WP_011452818.1, WP_011452723.1, WP_006010413.1, WP_006010278.1, WP_011452938.1, and WP_006010644.1 were detected. They belong to unique metabolic pathways of E. chaffeensis that are considered as new essential drug targets. Based on the reverse vaccinology, WP_011452702.1, WP_044193405.1, WP_044170604.1, and WP_006010191.1 proteins were potential vaccine candidates. Finally, four B cell epitopes, including SINNQDRNC, FESVSSYNI, SGKKEISVQSN, and QSSAKRKST, were used to generate the multi-epitope vaccine based on LCL platform. The vaccine showed strong interactions with toll-like receptors and acceptable immune-reactivity by immune simulation analysis. The findings of this study may represent a turning point in developing an effective drug and vaccine against E. chaffeensis. However, further experimental analyses have remained.

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Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease.

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Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease.

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