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Sasikumar DSN, Thiruselvam P, Sundararajan V, Ravindran R, Gunasekaran S, Madathil D, Kaliamurthi S, Peslherbe GH, Selvaraj G, Sudhakaran SL. Insights into dietary phytochemicals targeting Parkinson's disease key genes and pathways: A network pharmacology approach. Comput Biol Med 2024; 172:108195. [PMID: 38460310 DOI: 10.1016/j.compbiomed.2024.108195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 01/26/2024] [Accepted: 02/18/2024] [Indexed: 03/11/2024]
Abstract
Parkinson's disease (PD) is a complex neurological disease associated with the degeneration of dopaminergic neurons. Oxidative stress is a key player in instigating apoptosis in dopaminergic neurons. To improve the survival of neurons many dietary phytochemicals have gathered significant attention recently. Thus, the present study implements a comprehensive network pharmacology approach to unravel the mechanisms of action of dietary phytochemicals that benefit disease management. A literature search was performed to identify ligands (i.e., comprising dietary phytochemicals and Food and Drug Administration pre-approved PD drugs) in the PubMed database. Targets associated with selected ligands were extracted from the search tool for interactions of chemicals (STITCH) database. Then, the construction of a gene-gene interaction (GGI) network, analysis of hub-gene, functional and pathway enrichment, associated transcription factors, miRNAs, ligand-target interaction network, docking were performed using various bioinformatics tools together with molecular dynamics (MD) simulations. The database search resulted in 69 ligands and 144 unique targets. GGI and subsequent topological measures indicate histone acetyltransferase p300 (EP300), mitogen-activated protein kinase 1 (MAPK1) or extracellular signal-regulated kinase (ERK)2, and CREB-binding protein (CREBBP) as hub genes. Neurodegeneration, MAPK signaling, apoptosis, and zinc binding are key pathways and gene ontology terms. hsa-miR-5692a and SCNA gene-associated transcription factors interact with all the 3 hub genes. Ligand-target interaction (LTI) network analysis suggest rasagiline and baicalein as candidate ligands targeting MAPK1. Rasagiline and baicalein form stable complexes with the Y205, K330, and V173 residues of MAPK1. Computational molecular insights suggest that baicalein and rasagiline are promising preclinical candidates for PD management.
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Affiliation(s)
- Devi Soorya Narayana Sasikumar
- Integrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, TN, 632014, India
| | - Premkumar Thiruselvam
- Integrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, TN, 632014, India
| | - Vino Sundararajan
- Integrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, TN, 632014, India
| | - Radhika Ravindran
- Department of Biotechnology, Indian Institute of Technology (Madras), Chennai, TN, 600036, India
| | - Shoba Gunasekaran
- Department of Biotechnology, Dwaraka Doss Goverdhan Doss Vaishnav College, Chennai, TN, 600106, India
| | - Deepa Madathil
- Jindal Institute of Behavioral Sciences, O.P Jindal Global University, Sonipat, Haryana, 131001, India
| | - Satyavani Kaliamurthi
- Centre for Research in Molecular Modeling (CERMM), Department of Chemistry and Biochemistry, Concordia University, Loyola Campus, Montreal, QC, H4B 1R6, Canada
| | - Gilles H Peslherbe
- Centre for Research in Molecular Modeling (CERMM), Department of Chemistry and Biochemistry, Concordia University, Loyola Campus, Montreal, QC, H4B 1R6, Canada
| | - Gurudeeban Selvaraj
- Centre for Research in Molecular Modeling (CERMM), Department of Chemistry and Biochemistry, Concordia University, Loyola Campus, Montreal, QC, H4B 1R6, Canada; Bioinformatics Unit, Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS) University, Chennai, TN, 600077, India.
| | - Sajitha Lulu Sudhakaran
- Integrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, TN, 632014, India.
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Leal DAB, Dias CMV, Ramos RP, Brys I. Prediction of dyskinesia in Parkinson's disease patients using machine learning algorithms. Sci Rep 2023; 13:22426. [PMID: 38104147 PMCID: PMC10725420 DOI: 10.1038/s41598-023-49617-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 12/10/2023] [Indexed: 12/19/2023] Open
Abstract
Dyskinesias are non preventable abnormal involuntary movements that represent the main challenge of the long term treatment of Parkinson's disease (PD) with the gold standard dopamine precursor levodopa. Applying machine learning techniques on the data extracted from the Parkinson's Progression Marker Initiative (PPMI, Michael J. Fox Foundation), this study was aimed to identify PD patients who are at high risk of developing dyskinesias. Data regarding clinical, behavioral and neurological features from 697 PD patients were included in our study. Our results show that the Random Forest was the classifier with the best and most consistent performance, reaching an area under the receiver operating characteristic (ROC) curve of up to 91.8% with only seven features. Information regarding the severity of the symptoms, the semantic verbal fluency, and the levodopa treatment were the most important for the prediction, and were further used to create a Decision Tree, whose rules may guide the pharmacological management of PD symptoms. Our results contribute to the identification of PD patients who are prone to develop dyskinesia, and may be considered in future clinical trials aiming at developing new therapeutic approaches for PD.
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Affiliation(s)
- Denisson Augusto Bastos Leal
- Postgraduate Program in Health and Biological Sciences, Federal University of Vale do São Francisco (UNIVASF), Av José Sá de Maniçoba s/n, Petrolina, 56304-917, Brazil
| | - Carla Michele Vieira Dias
- Postgraduate Program in Psychology, Federal University of Vale do São Francisco (UNIVASF), Petrolina, Brazil
| | - Rodrigo Pereira Ramos
- Postgraduate Program in Health and Biological Sciences, Federal University of Vale do São Francisco (UNIVASF), Av José Sá de Maniçoba s/n, Petrolina, 56304-917, Brazil
| | - Ivani Brys
- Postgraduate Program in Health and Biological Sciences, Federal University of Vale do São Francisco (UNIVASF), Av José Sá de Maniçoba s/n, Petrolina, 56304-917, Brazil.
- Postgraduate Program in Psychology, Federal University of Vale do São Francisco (UNIVASF), Petrolina, Brazil.
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