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Exploration of the Shared Molecular Mechanisms between COVID-19 and Neurodegenerative Diseases through Bioinformatic Analysis

International Journal of Molecular Sciences · 5 authors, 3 centres

AI SUMMARY

FIDELITY 100%

POPULATIONPatients with COVID-19, Alzheimer's disease, and Parkinson's disease (frontal cortex tissue samples from GEO datasets: GSE188847, GSE150696)

INTERVENTIONBioinformatic analysis of transcriptomic data (differential expression, pathway enrichment, PPI network, hub gene identification, drug target prediction)

COMPARISONAge- and sex-matched healthy controls without neurological disorders or COVID-19

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TL;DR

This bioinformatic study identified 52 common differentially expressed genes (DEGs) shared between COVID-19, Alzheimer's disease (AD), and Parkinson's disease (PD) in frontal cortex tissue, with most genes being down-regulated. The synaptic vesicle cycle pathway emerged as the key shared mechanism, suggesting SARS-CoV-2 infection may cause synaptic dysfunction that could trigger or worsen neurodegenerative processes. Five hub genes (TAGLN3, GAD2, SST, SYP, KCNJ4) and several potential repurposable drugs (including azelnidipine and artenimol) were identified as promising therapeutic targets for COVID-19-related neurological complications.

Full summary

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**Background:** SARS-CoV-2 infection is known to cause neurological symptoms and may trigger or accelerate neurodegenerative disorders such as Alzheimer's disease (AD) and Parkinson's disease (PD). The frontal cortex is critically involved in cognitive function and is affected in both AD and PD, as well as in COVID-19 patients. This study aimed to identify shared molecular mechanisms among COVID-19, AD, and PD using bioinformatic analysis of frontal cortex transcriptomic data.

**Methods:** Three training datasets were obtained from the GEO database: GSE188847 (COVID-19, 12 cases vs. 12 controls), and GSE150696 (AD, 9 cases vs. 9 controls; PD, 12 cases vs. 9 controls). Differential expression analysis was performed using the limma package in R (cutoff: p < 0.05, |logFC| > 1), controlling for age and sex. Common DEGs among the three diseases were identified via Venn diagram analysis. Functional annotation included Gene Ontology (GO), KEGG, Reactome pathway enrichment, and Gene Set Enrichment Analysis (GSEA). A protein-protein interaction (PPI) network was constructed using STRING and visualized in Cytoscape. Hub genes were identified using three algorithms (MCC, Degree, Betweenness Centrality) via CytoHubba. Candidate drugs were predicted using DrugBank, and transcription factors were identified using RcisTarget. Validation was performed using independent datasets: GSE164332 (COVID-19), GSE104704 (AD), and merged GSE20168/GSE8397 (PD).

**Key Results:** A total of 1344 DEGs were identified for COVID-19 (927 up, 417 down), 2655 for AD (651 up, 2004 down), and 2589 for PD (882 up, 1707 down). Cross-comparison yielded 52 common DEGs (9 up-regulated, 43 down-regulated). GO analysis showed enrichment in synaptic signaling terms including neurotransmitter transport (p = 1.64 × 10⁻⁵), modulation of chemical synaptic transmission (p = 0.0001), and regulation of trans-synaptic signaling (p = 0.0001). Cellular component terms were dominated by exocytic vesicle (p = 3.31 × 10⁻¹⁰) and synaptic vesicle membrane (p = 4.42 × 10⁻¹⁰). KEGG analysis identified the synaptic vesicle cycle as the top pathway (p = 2.06 × 10⁻⁶). Reactome analysis highlighted transmission across chemical synapses (p = 8.21 × 10⁻⁷) and neuronal system (p = 1.84 × 10⁻⁶). GSEA confirmed that synapse-related terms were consistently down-regulated across all three diseases, while cytokine-cytokine receptor interaction and humoral immune response were up-regulated. The PPI network contained 52 nodes and 320 edges. MCODE analysis extracted a key module (score 18.222, 19 nodes) also linked to the synaptic vesicle cycle. Five hub genes were identified: TAGLN3, GAD2, SST, SYP, and KCNJ4, all of which were validated in independent datasets. Six drug targets and 18 related drugs were identified from DrugBank. Five drugs were considered potentially therapeutic: Ibutilide (target: CACNB1, p = 0.0058), Azelnidipine (target: CACNB1, p = 0.0058), Dotarizine (target: CACNA1A, p = 0.0068), Copper (target: SFPQ, p = 0.0233), and Artenimol (target: SFPQ, p = 0.0233). Ten transcription factors (SP2, SIN3A, REST, ATF3, MYF6, TBX5, RFX1, RPL6, NR3C1, HDAC2) were predicted to regulate 42 of the common DEGs.

**Clinical Implications:** This study provides evidence that synaptic vesicle cycle dysfunction is a shared molecular mechanism linking COVID-19 with AD and PD, suggesting that SARS-CoV-2 infection may cause synaptic down-regulation in the frontal cortex that could trigger or accelerate neurodegenerative processes. The five identified hub genes (particularly GAD2, involved in GABA synthesis, and SYP, a key synaptic protein) represent potential therapeutic targets. The candidate drugs identified, especially azelnidipine (a calcium channel blocker with known neuroprotective effects) and artenimol (an artemisinin derivative with anti-SARS-CoV-2 potential), warrant further investigation for treating COVID-19-related neurological complications. However, the authors caution that these findings are based on bioinformatic analyses with limited sample sizes and require validation through cellular and animal experiments.

PICO

PPOPULATION

Patients with COVID-19, Alzheimer's disease, and Parkinson's disease (frontal cortex tissue samples from GEO datasets: GSE188847, GSE150696)

IINTERVENTION

Bioinformatic analysis of transcriptomic data (differential expression, pathway enrichment, PPI network, hub gene identification, drug target prediction)

OOUTCOME

Shared differentially expressed genes, enriched pathways (synaptic vesicle cycle), hub genes, and candidate therapeutic drugs

STUDY TYPE

other

SPECIALTY

neurology

SUMMARISED BY

AI pipeline

FIDELITY CHECK

100% · A

ORIGINAL PAPER

PMC10002862

Exploration of the Shared Molecular Mechanisms between COVID-19 and Neurodegenerative Diseases through Bioinformatic Analysis

International Journal of Molecular Sciences

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ABSTRACT

Abstract The COVID-19 pandemic has caused millions of deaths and remains a major public health burden worldwide. Previous studies found that a large number of COVID-19 patients and survivors developed neurological symptoms and might be at high risk of neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). We aimed to explore the shared pathways between COVID-19, AD, and PD by using bioinformatic analysis to reveal potential mechanisms, which may explain the neurological symptoms and degeneration of brain that occur in COVID-19 patients, and to provide early intervention. In this study, gene expression datasets of the frontal cortex were employed to detect common differentially expressed genes (DEGs) of COVID-19, AD, and PD. A total of 52 common DEGs were then examined using functional annotation, protein–protein interaction (PPI) construction, candidate drug identification, and regulatory network analysis. We found that the involvement of the synaptic vesicle cycle and down-regulation of synapses were shared by these three diseases, suggesting that synaptic dysfunction might contribute to the onset and progress of neurodegenerative diseases caused by COVID-19. Five hub genes and one key module were obtained from the PPI network. Moreover, 5 drugs and 42 transcription factors (TFs) were also identified on the datasets. In conclusion, the results of our study provide new insights and directions for follow-up studies of the relationship between COVID-19 and neurodegenerative diseases. The hub genes and potential drugs we identified may provide promising treatment strategies to prevent COVID-19 patients from developing these disorders. Keywords: COVID-19, Alzheimer’s disease, Parkinson’s disease, bioinformatics, differentially expressed genes, gene ontology, protein–protein interaction, hub genes, drugs

CITATION EXPORT

Yingchao Shi, Wenhao Liu, Yang Yang et al.. Exploration of the Shared Molecular Mechanisms between COVID-19 and Neurodegenerative Diseases through Bioinformatic Analysis. International Journal of Molecular Sciences. (2023). https://doi.org/10.3390/ijms24054839

AUTHORS

5 · 3 CENTRES

Yingchao Shi, Wenhao Liu, Yang Yang, Yali Ci, Lei Shi

1State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing 100005, China
2Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing 100005, China
3Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China