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MicroRNA-gene regulatory network of TLR signaling in neuroinflammation-induced Parkinson’s disease: a bioinformatics approach

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Abstract

Parkinson’s disease (PD) is the second-most common neurodegenerative disease, affecting 10 million people worldwide. Neuroinflammation is one of the major pathologic processes in the development of PD. Neuroinflammation is promoted via the activation of TLRs present on immune cells in the brain. In addition, miRNA regulates TLR expression in neurodegenerative diseases. However, there is limited information on the miRNA that regulates TLR signaling genes in PD. In this study, we used GO, a bioinformatics tool that uses the representations for genes in an organism; PPI, which shows the physical interaction between proteins in an organism; and miRNet, a tool to navigate the complex relationships between miRNAs and their targets for deeper biologic understanding. To find out the potential TLR genes and regulatory miRNAs that play a role in neuroinflammation-induced PD. We acquired the gene expression profile, GSE26927, from the GEO Omnibus. DAVID bioinformatics and SHINY GO software were employed for GO analysis of DEGs, and the fold enrichment score for each pathway was verified. The TLR signaling pathways most deregulated genes (upregulated: log FC ≥ 2.0, downregulated: log FC ≤ – 2.0) were chosen for network analysis to identify crucial or hub genes. Subsequently, a miRNA-gene network was constructed using the miRNet tool. The foremost TLR signaling gene, distinguishing between PD and control samples, has been discerned. In the Protein–Protein Interaction (PPI) network, we identified genes with heightened connectivity, notably TLR4, exhibiting the highest degree of betweenness (degree = 22) in the TLR signaling pathway. Furthermore, in the miRNA-gene network, we unveiled the preeminent five miRNAs: hsa-miR-21-5p, hsa-miR-17-5p, hsa-miR-93-5p, hsa-miR-7-5p, and hsa-mir-92b-3p that interacted with the TLR signaling gene. The top ten TLR genes could be potential targets for new therapeutics. In addition, the identified potential miRNAs can strongly regulate the expression of TLR genes in PD and serve as therapeutic target.

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Data availability

The GEO database from NCBI (Gene Expression Omnibus database, https://www.ncbi.nlm.nih.gov/geo/) was used to access the GSE26927 dataset.

Abbreviations

PD:

Parkinson’s disease

TLRs :

Toll-like Receptors

VD:

Vascular dementia

DEGs:

Differentially Expressed Genes

GEO:

Gene Expression Omnibus

GO:

Gene ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

DAVID:

Database for Annotation, Visualization, and Integrated Discovery

STRING:

Search Tool for Recurring Instances of Neighbouring Genes

PPI:

Protein–Protein interaction

PRRs:

Pattern recognition receptor

TLR4 :

Toll-like receptor 4

MyD88 :

Myeloid differentiation primary response 88

IRAK1 :

Interleukin-1 receptor-associated kinase 1

TRAF6 :

Tumor necrosis factor receptor-associated factor 6

IRF7 :

Interleukin regulatory factor 7

FE:

Fold enrichment

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Acknowledgements

The authors acknowledge National Institute of Pharmaceutical Education and Research (NIPER)-Hyderabad for providing the necessary facilities and resources to prepare this manuscript.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Molecular and Cellular Neuroscience Lab, Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER)- Hyderabad, Hyderabad, Telangana, 500037, India

    Gurpreet Singh & Dharmendra Kumar Khatri

  2. Department of Pharmacology, Shobhaben Pratapbai Patel School of Pharmacy & Technology Management, SVKMs Narsee Monjee Institute of Management Studies (NMIMS) Deemed-to-University, Mumbai, 400056, India

    Dharmendra Kumar Khatri

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Singh, G., Khatri, D.K. MicroRNA-gene regulatory network of TLR signaling in neuroinflammation-induced Parkinson’s disease: a bioinformatics approach. Netw Model Anal Health Inform Bioinforma 13, 7 (2024). https://doi.org/10.1007/s13721-024-00445-6

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