Dataset Information

In Silico Drug Repurposing in Multiple Sclerosis Using scRNA-Seq Data.

ABSTRACT: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system still lacking a cure. Treatment typically focuses on slowing the progression and managing MS symptoms. Single-cell transcriptomics allows the investigation of the immune system-the key player in MS onset and development-in great detail increasing our understanding of MS mechanisms and stimulating the discovery of the targets for potential therapies. Still, de novo drug development takes decades; however, this can be reduced by drug repositioning. A promising approach is to select potential drugs based on activated or inhibited genes and pathways. In this study, we explored the public single-cell RNA data from an experiment with six patients on single-cell RNA peripheral blood mononuclear cells (PBMC) and cerebrospinal fluid cells (CSF) of patients with MS and idiopathic intracranial hypertension. We demonstrate that AIM2 inflammasome, SMAD2/3 signaling, and complement activation pathways are activated in MS in different CSF and PBMC immune cells. Using genes from top-activated pathways, we detected several promising small molecules to reverse MS immune cells' transcriptomic signatures, including AG14361, FGIN-1-27, CA-074, ARP 101, Flunisolide, and JAK3 Inhibitor VI. Among these molecules, we also detected an FDA-approved MS drug Mitoxantrone, supporting the reliability of our approach.

SUBMITTER: Shevtsov A

PROVIDER: S-EPMC9864606 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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In Silico Drug Repurposing in Multiple Sclerosis Using scRNA-Seq Data.

Shevtsov Andrey A Raevskiy Mikhail M Stupnikov Alexey A Medvedeva Yulia Y

International journal of molecular sciences 20230104 2

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system still lacking a cure. Treatment typically focuses on slowing the progression and managing MS symptoms. Single-cell transcriptomics allows the investigation of the immune system-the key player in MS onset and development-in great detail increasing our understanding of MS mechanisms and stimulating the discovery of the targets for potential therapies. Still, de novo drug development takes decades; however, this can be r ...[more]

PMID: 36674506

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Project description:BackgroundAmyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disorder characterized by the progressive loss of motor neurons. Despite extensive research, the exact etiology of ALS remains elusive. Emerging evidence highlights the critical role of the immune system in ALS pathogenesis and progression. Damage-Associated Molecular Patterns (DAMPs) are endogenous molecules released by stressed or damaged cells, acting as danger signals and activating immune responses. However, their specific involvement in ALS remains unclear.MethodsWe obtained single-cell RNA sequencing (scRNA-seq) data of ALS from the primary motor cortex in the Gene Expression Omnibus (GEO) database. To better understand genes associated with DAMPs, we performed analyses on cell-cell communication and trajectory. The abundance of immune-infiltrating cells was assessed using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. We performed univariate Cox analysis to construct the risk model and utilized the least absolute shrinkage and selection operator (LASSO) analysis. Finally, we identified potential small molecule drugs targeting ALS by screening the Connectivity Map database (CMap) and confirmed their potential through molecular docking analysis.ResultsOur study annotated 10 cell types, with the expression of genes related to DAMPs predominantly observed in microglia. Analysis of intercellular communication revealed 12 ligand-receptor pairs in the pathways associated with DAMPs, where microglial cells acted as ligands. Among these pairs, the SPP1-CD44 pair demonstrated the greatest contribution. Furthermore, trajectory analysis demonstrated distinct differentiation fates of different microglial states. Additionally, we constructed a risk model incorporating four genes (TRPM2, ROCK1, HSP90AA1, and HSPA4). The validity of the risk model was supported by multivariate analysis. Moreover, external validation from dataset GSE112681 confirmed the predictive power of the model, which yielded consistent results with datasets GSE112676 and GSE112680. Lastly, the molecular docking analysis suggested that five compounds, namely mead-acid, nifedipine, nifekalant, androstenol, and hydrastine, hold promise as potential candidates for the treatment of ALS.ConclusionTaken together, our study demonstrated that DAMP entities were predominantly observed in microglial cells within the context of ALS. The utilization of a prognostic risk model can accurately predict ALS patient survival. Additionally, genes related to DAMPs may present viable drug targets for ALS therapy.

Dataset Information

In Silico Drug Repurposing in Multiple Sclerosis Using scRNA-Seq Data.

Publications

In Silico Drug Repurposing in Multiple Sclerosis Using scRNA-Seq Data.

Similar Datasets

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