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. 2022 Oct 27;72(11):2326–2337. doi: 10.1007/s12031-022-02068-w

Discovering Common Pathogenic Mechanisms of COVID-19 and Parkinson Disease: An Integrated Bioinformatics Analysis

Aria Jahanimoghadam 1,4,#, Hadis Abdolahzadeh 2,#, Niloofar Khoshdel Rad 2, Javad Zahiri 3,✉
PMCID: PMC9607846  PMID: 36301487

Abstract

Coronavirus disease 2019 (COVID-19) has emerged since December 2019 and was later characterized as a pandemic by WHO, imposing a major public health threat globally. Our study aimed to identify common signatures from different biological levels to enlighten the current unclear association between COVID-19 and Parkinson’s disease (PD) as a number of possible links, and hypotheses were reported in the literature. We have analyzed transcriptome data from peripheral blood mononuclear cells (PBMCs) of both COVID-19 and PD patients, resulting in a total of 81 common differentially expressed genes (DEGs). The functional enrichment analysis of common DEGs are mostly involved in the complement system, type II interferon gamma (IFNG) signaling pathway, oxidative damage, microglia pathogen phagocytosis pathway, and GABAergic synapse. The protein–protein interaction network (PPIN) construction was carried out followed by hub detection, revealing 10 hub genes (MX1, IFI27, C1QC, C1QA, IFI6, NFIX, C1S, XAF1, IFI35, and ELANE). Some of the hub genes were associated with molecular mechanisms such as Lewy bodies–induced inflammation, microglia activation, and cytokine storm. We investigated regulatory elements of hub genes at transcription factor and miRNA levels. The major transcription factors regulating hub genes are SOX2, XAF1, RUNX1, MITF, and SPI1. We propose that these events may have important roles in the onset or progression of PD. To sum up, our analysis describes possible mechanisms linking COVID-19 and PD, elucidating some unknown clues in between.

Keywords: COVID-19, Parkinson disease, Transcriptome analysis, Regulatory networks, Signaling pathways, Bioinformatics

Introduction

According to the statistics provided by WHO, there have been approximately 255 million cases diagnosed with coronavirus disease 2019 (COVID-19) with more than 5 million confirmed death cases as of November 2021. The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 and is considered a worldwide pandemic whose impacts are noticeable across the globe (Chams et al. 2020; Khorsand et al. 2020). COVID-19 is initially viewed as a respiratory disease. However, it was demonstrated that SARS-CoV-2 affects multiple organs such as the central nervous system (CNS). Moreover, neurological manifestation associated with SARS-CoV-2 can potentially occur via direct invasion of the virus into the CNS and, thus, introduce the brain as a location containing high replicative values for SARS-CoV-2 (Song et al. 2021; Szcześniak et al. 2021).

Parkinson’s disease (PD) is a neurodegenerative disease characterized for the first time in 1817 by James Parkinson. However, after nearly two centuries of research, the cause of most of the cases is still unclear (Olsen et al. 2018; Hayes 2019). There are multiple reports regarding the occurrence of neurological complications in individuals with COVID-19 as up to 85%. Additionally, hyposmia, one of the symptoms of PD, has been reported to happen in 65% of cases with COVID-19 (Merello et al. 2021). The prevalence of PD among elderly ages higher than 65 is 1–3%, and that for the whole population is approximately 0.3%. Of note, PD is distinguished mainly via degeneration of dopaminergic neurons located in the substantia nigra of the midbrain (Kalia and Lang 2016). Subsequently, a number of theoretical mechanisms such as basal ganglia injury, microglia-induced inflammation, and post-encephalopathy inflammation have been proposed to be the hypothetical link between COVID-19 and PD but there is still a gap of knowledge in the way of understanding the relationship among them. This hypothesis is also supported by some case reports, stating a rapid onset of PD after infection with SARS-CoV-2 (Eichel et al. 2020; Cartella et al. 2021; Merello et al. 2021).

On the other hand, several viruses including Epstein-Barr, hepatitis C, herpes simplex 1, influenza A, and varicella-zoster have previously been shown to be related to increasing the risk of diagnosing with PD in the distant future (Henry et al. 2010; Merello et al. 2021). These viruses can directly induce neuronal injury after the infection. For instance, it has been demonstrated that there is a marked increased risk of developing PD after hepatitis C virus (HCV) infection. This was enabled by the ability of HCV to replicate in the CNS (Tsai et al. 2016). The family of Coronaviridae has been known to cause CNS infection (Bergmann et al. 2006), presumably in the case of SARS-CoV-2 via the blood–brain barrier (BBB) due to the cytokine storm (CS) (Eldeeb et al. 2020; Sulzer et al. 2020). Moreover, SARS-CoV-2 signature was detected in the autoptic brains of 21 out of 40 patients (53%) after dying of COVID-19. Although no relationship between the presence of SARS-CoV-2 and the severity of the disease was found, it was proved that SARS-CoV-2 can reach the CNS (Matschke et al. 2020). Overall, all the aforementioned pieces of evidence create an urgent need that the possible crosstalk between SARS-CoV-2 and neurodegenerative disorders such as PD should be taken into consideration. Other studies have been carried out assessing other potential comorbidities in respect to COVID-19 including chronic kidney disease (Auwul et al. 2021) and diabetes mellitus (Rahman et al. 2021).

In the present study, we adopted an integrated bioinformatics analysis to scrutinize the common molecular mechanisms involved in COVID-19 and PD pathogenesis and how SARS-CoV-2 can possibly contribute to developing PD whether immediately after contracting COVID-19 or years later (an overview of the present study is shown in Fig. 1).

Fig. 1.

Fig. 1

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Flow chart of steps conducted in the study

Materials and Methods

Transcriptomic Data Analysis

We obtained the transcriptome data from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) with accession numbers GSE152418 (16 peripheral blood mononuclear cell (PBMC) samples from COVID-19 subjects and 17 from normal individuals) (Arunachalam et al. 2020) and GSE165082 (12 PBMC samples from PD and 14 from normal individuals) (Henderson et al. 2021). The R package DESeq2 was provided for normalization and differential expression analysis (Love et al. 2014). We used the P value < 0.05 and (log FC >|1|) as thresholds. Common differentially expressed genes (DEGs) between two datasets were obtained using the Venny 2.1.0 tool (https://bioinfogp.cnb.csic.es/tools/venny/index.html).

Gene Ontology and Pathway Enrichment Analysis

For the functional annotation and pathway enrichment analysis of the DEGs, Enrichr web utility tools (Kuleshov et al. 2016) were used. WikiPathways and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for finding pathway enrichment analysis. Gene Ontology (GO) terms were considered in three main categories such as biological process (BP), cellular component (CC), and molecular functions (MF).

Protein–Protein Interaction Network Construction and Analysis

GeneMANIA (Warde-Farley et al. 2010) server was used for protein–protein interaction network (PPIN) construction, and then the obtained PPIN was analyzed and visualized by Cytoscape version 3.8. We adopted a hub detection approach called maximal clique centrality (MCC) via cytoHubba plug-in of Cytoscape to retrieve the top 10 hub nodes. MCC is a local-based algorithm which outperforms other methods in hub identification (Chin et al. 2014).

Identification of Transcription Factors and MicroRNAs Regulating Hub Genes

Transcription factor (TF) and microRNA (miRNA) are considered the major regulatory elements of gene expression at both transcription and post-transcription levels (Qin et al. 2020). We have constructed TF-hub gene and miRNA-hub gene regulatory networks with the use of NetworkAnalyst 3.0 to detect important regulatory elements (Zhou et al. 2019). We used ChEA as a TF database (Lachmann et al. 2010) to create the TF-hub gene interaction network. To construct the miRNA-hub gene interaction network, TarBase (Karagkouni et al. 2018) was selected to retrieve interacting miRNAs with regard to hub genes. Following the network’s construction, network analysis was carried out to identify core TFs and miRNAs based on the degree.

Results

Identification of Common DEGs Between COVID-19 and PD

We examined transcriptional signatures between COVID-19 (n = 16) and healthy controls (n = 17). There were 4795 DEGs in COVID-19 versus healthy controls. Also, we obtained DEGs between PD (n = 14) and normal subjects (n = 12). Our results showed 233 DEGs in PD compared to controls. We detected 81 common DEGs between COVID-19 and PD (Fig. 2). Top ten common DEGs are shown in Table 1.

Fig. 2.

Fig. 2

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Venn diagram showing common DGEs between COVID-19 and PD

Table 1.

Top ten common DEGs between COVID-19 and PD

The common DEGs log FC
GSE152418 (COVID-19 versus healthy control) GSE165082 (Parkinson versus healthy control)
ISG15 1.2 −1.3
GABRD 1.8 −2.1
C1QC 3.5 −1.05
C1QB 2.9 −1.2
IFI6 2.05 −1.2
A3GALT2 2.08 1.04
VCAM1 −1.2 2.9
AQP10 1.9 −1.2
ACTG1P25 1.1 −1.2
C4BPA 1.1 −1.4
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Pathway Enrichment Analysis

Functional annotations of common DEGs indicated involvement in multiple pathways including the complement system, type II interferon gamma (IFNG) signaling pathway, oxidative damage, microglia pathogen phagocytosis pathway, and GABAergic synapse (Table 2). The GO analysis of common DEGs revealed that enriched BPs were mostly involved in the regulation of complement activation, regulation of immune effector process, regulation of humoral immune response, cell junction disassembly, commissural neuron axon guidance, synapse pruning, complement activation, classical pathway, determination of left/right symmetry, negative regulation of humoral immune response mediated by circulating immunoglobulin, and humoral immune response mediated by circulating immunoglobulin. The enriched molecular functions were involved in kinase activator activity, protein kinase activator activity, GABA-A receptor activity, neurotransmitter receptor activity involved in the regulation of postsynaptic membrane potential, GABA receptor activity, transmitter-gated ion channel activity involved in the regulation of postsynaptic membrane potential, protein kinase regulator activity, transmitter-gated ion channel activity, glycerol channel activity, and arylesterase activity. CC enriched in GABA-A receptor complex, azurophilic granule, collagen-containing extracellular matrix, Golgi lumen, secretory granule lumen, caveola, specific granule lumen, vacuolar lumen, primary lysosome, and plasma membrane raft (Table 3).

Table 2.

Top ten molecular pathways enriched by 81 common DEGs in COVID-19 and PD

Source Pathways P value Count Genes
WikiPathways Complement and coagulation cascades (WP558) 0.001682 3 C1QB;SERPING1;C1QC
Complement activation (WP545) 0.003550 2 C1QB;C1QC
Complement system (WP2806) 0.007179 3 SERPING1;C4BPA;ELANE
Type II interferon gamma (IFNG) signaling (WP619) 0.009842 2 IFI6;ISG15
miRNAs’ involvement in the immune response in sepsis (WP4329) 0.009842 2 VCAM1;ELANE
Oxidative damage (WP3941) 0.011437 2 C1QB;C1QC
Microglia pathogen phagocytosis pathway (WP3937) 0.011437 2 C1QB;C1QC
Development of ureteric collection system (WP5053) 0.015565 2 WNT11;SMO
Prader-Willi and Angelman syndrome (WP3998) 0.025409 2 GABRR2;GABRD
Non-genomic actions of 1,25-dihydroxyvitamin D3 (WP4341) 0.033623 2 RSAD2;ISG15
KEGG Pertussis 0.000256 4 C1QB;SERPING1;C4BPA;C1QC
Complement and coagulation cascades 0.000394 4 C1QB;SERPING1;C4BPA;C1QC
Systemic lupus erythematosus 0.002210 4 C1QB;CTSG;ELANE;C1QC
Neuroactive ligand-receptor 0.002609 6 GABRR2;CHRND;GRID1;LPAR1;CTSG;GABRD
Staphylococcus aureus infection 0.006778 3 C1QB;DEFA4;C1QC
Transcriptional misregulation 0.007708 4 ETV7;DEFA4;ERG;ELANE
Nicotine addiction 0.011437 2 GABRR2;GABRD
Basal cell carcinoma 0.026977 2 WNT11;SMO
GABAergic synapse 0.050585 2 GABRR2;GABRD
Morphine addiction 0.052626 2 GABRR2;GABRD
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Table 3.

GO enrichment analysis of 81 common DEGs in COVID-19 and PD

Term P value Count Genes
BP Regulation of complement activation (GO:0030449) 0.000049 4 C1QB;SERPING1;C4BPA;C1QC
Regulation of immune effector process (GO:0002697) 0.000062 4 C1QB;SERPING1;C4BPA;C1QC
Regulation of humoral immune response (GO:0002920) 0.000067 4 C1QB;SERPING1;C4BPA;C1QC
Cell junction disassembly (GO:0150146) 0.00024 2 C1QB;C1QC
Commissural neuron axon guidance (GO:0,071679) 0.00044 2 SMO;NFIB
Synapse pruning (GO:0098883) 0.00057 2 C1QB;C1QC
Complement activation, classical pathway (GO:0006958) 0.00057 2 C1QB;C1QC
Determination of left/right symmetry (GO:0007368) 0.00065 3 DNAH11;SMO;FOXJ1
Negative regulation of humoral immune response mediated by circulating immunoglobulin (GO:0002924) 0.00071 2 FOXJ1;C4BPA
Humoral immune response mediated by circulating immunoglobulin (GO:0002455) 0.00087 2 C1QB;C1QC
MF Kinase activator activity (GO:0019209) 0.00026 3 WNT11;SPDYA;GPRC5D
Protein kinase activator activity (GO:0030295) 0.002133 3 WNT11;SPDYA;GPRC5D
GABA-A receptor activity (GO:0004890) 0.002649 2 GABRR2;GABRD
Neurotransmitter receptor activity involved in the regulation of postsynaptic membrane potential (GO:0099529) 0.003236 2 CHRND;GRID1
GABA receptor activity (GO:0016917) 0.00355 2 GABRR2;GABRD
Transmitter-gated ion channel activity involved in the regulation of postsynaptic membrane potential (GO:1904315) 0.006547 2 CHRND;GRID1
Protein kinase regulator activity (GO:0019887) 0.007384 3 WNT11;SPDYA;GPRC5D
Transmitter-gated ion channel activity (GO:0022824) 0.008356 2 CHRND;GRID1
Glycerol channel activity (GO:0015254) 0.020088 1 AQP10
Arylesterase activity (GO:0004064) 0.024058 1 CA1
CC GABA-A receptor complex (GO:1902711) 0.002649 2 GABRR2;GABRD
Azurophilic granule (GO:0042582) 0.003635 4 CEACAM6;DEFA4;CTSG;ELANE
Collagen-containing extracellular matrix (GO:0062023) 0.004433 6 C1QB;SERPING1;CTSG;CSPG4;ELANE;C1QC
Golgi lumen (GO:0005796) 0.007805 3 DEFA4;CSPG4;MUC5B
Secretory granule lumen (GO:0034774) 0.009194 5 DEFA4;SELENOP;SERPING1;CTSG;ELANE
Caveola (GO:0005901) 0.02464 2 SMO;KCNA5
Specific granule lumen (GO:0035580) 0.026188 2 DEFA4;ELANE
Vacuolar lumen (GO:0005775) 0.027664 3 CTSG;CSPG4;ELANE
Primary lysosome (GO:0005766) 0.043669 1 DEFA4
Plasma membrane raft (GO:0044853) 0.043678 2 SMO;KCNA5
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BP biological processes, MF molecular functions, CC cellular components

Protein–Protein Interaction Network Construction and Analysis

We constructed PPIN, containing 71 nodes and 1369 edges as shown in Fig. 3. The PPIN depicts the interaction of common DEGs and was visualized by Cytoscape software. According to Table 4, the ten hub genes based on MCC score are myxovirus resistance genes (MX) dynamin–like GTPase 1 (MX1), interferon alpha–inducible protein 27 (IFI27), C1CQ, C1QA, interferon alpha–inducible protein 6 (IFI6), NFIX, C1S, X-linked inhibitor of apoptosis-associated factor-1 (XAF1), interferon alpha–inducible protein (IFI35), and elastase, neutrophil expressed (ELANE). These hub genes can potentially be used as drug targets and play a crucial role in maintaining the stability of the network. Therefore, further analysis of these genes is of great importance. For instance, scrutinizing the regulatory interaction of hub genes is recommended.

Fig. 3.

Fig. 3

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PPIN of common DEGs. Red nodes indicate top 10 hub genes identified by MCC

Table 4.

Summary of hub nodes

Hubs MCC
MX1 5
IFI27 4
C1QC 3
C1QA 3
IFI6 3
NFIX 2
C1S 2
XAF1 2
IFI35 2
ELANE 2
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MCC maximal clique centrality

Regulatory Networks

In order to gain deeper insights into our hub genes, we sought to construct TF-hub gene and miRNA-hub gene networks. Figures 4 and 5 display the regulators of the hub genes (TFs and miRNAs, respectively). From these regulatory networks, it can be concluded that some regulatory elements are more important and can subsequently interact with more hub genes. In the TF-hub gene network, 16 TFs were identified with 3 or more interactions, whereas in miRNA-hub genes, 29 miRNAs were detected with at least 3 or more interactions. The most connected TFs were SOX2 and XAF1 with the degree of 6, and RUNX1, MITF, SPI1, and MYC with 5 interactions. The most significant miRNA related to hub genes is hsa-mir-129–2-3p with a degree of 8. Other major miRNAs are hsa-mir-124-3p, hsa-mir-34a-5p, hsa-mir-21-3p, and hsa-mir-27a-5p; each has 6 edges with hub genes.

Fig. 4.

Fig. 4

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TF-hub gene regulatory network acquired from Network Analysis web server. Square nodes representing TFs and circle nodes are stand for hub genes

Fig. 5.

Fig. 5

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miRNA-hub gene regulatory network acquired from Network Analysis web server. Square nodes represent miRNAs that regulate circle nodes which denote hub genes

Discussion

The COVID-19 outbreak has undoubtedly become an international concern (WHO 2021). Some case reports hypothesized rapid onset of PD happens after SARS-CoV-2 infection (Cartella et al. 2021; Merello et al. 2021). However, there is no study aimed to investigate common links between COVID-19 and PD yet in an in silico manner.

In this study, we adopted a network-based approach following transcriptome analysis to detect the common molecular pathways involved in COVID-19 and PD pathogenesis. The analysis demonstrated 81 common DEGs between COVID-19 and PD. We then performed the pathway enrichment analysis of common DEG. Our results showed the complement and coagulation cascades are one of the pathways that are enriched by the common DEGs. The complement system plays a double role in the immune response against SARS-CoV-2 and the pathogenesis of COVID-19 tissue involvement (Gao et al. 2020; Diao et al. 2021). Several studies reported complement components to alter within the blood of PD patients (Goldknopf et al. 2006). The type II IFNG signaling pathway was also identified. The interferon (IFN) responses constitute the main first line of defense against SARS-CoV-2 (Park and Iwasaki 2020). IFN-γ has a role in inflammation and neurodegeneration in PD, as an increase of IFN-γ was detected in the serum of PD patients (Baba et al. 2005). Another common pathway was oxidative damage. Oxidative stress most likely impacts COVID-19 pathogenesis by accompanying cell activation (Chernyak et al. 2020). Oxidative stress is one of the mechanisms mentioned in the etiopathogenesis of PD (Dorszewska et al. 2021). Oxidative stress causes damage to key cellular components in the substantia nigra (SN) of PD patients (Dias et al. 2013). We detected microglia pathogen phagocytosis pathway in which microglia by some pathogenic mechanisms could contribute to the development of post-COVID-19 neurological sequelae and disorders, including PD (Awogbindin et al. 2021). Another enriched pathway was GABAergic synapse. COVID-19-associated inflammation may induce a cortical impairment of GABAergic neurotransmission, possibly representing cognitive fatigue, apathy, and executive deficits (Ortelli et al. 2021). GABA has also been reported to be involved in neurodegenerative disorders such as PD (Muñoz et al. 2020).

The hub genes have been identified from the PPIN to detect major signaling elements that may be used as therapeutic targets for the development of novel drugs to treat COVID-19 patients with PD comorbidity. MX1 is one of the MX which has the antiviral effect against RNA viruses. MX1 expression has been reported to be elevated in COVID-19 patients and conversely declines as age increases. Plus, it can be stimulated in the cytoplasm by IFNs and participates in the cellular antiviral response to SARS-COV-2 (Bizzotto et al. 2020). Furthermore, the accumulation of α-synuclein (α-SYN) in the brain of PD patients induces the expression of MX1. This molecule is involved in PI3K-Akt signaling pathway, cytokine release, and immune response IFN-α, IFN-β, and IFN-γ signaling pathways (Yamada et al. 1994; Qin et al. 2016). It is also a regulator of IFN systems that contributes to CS (Yang et al. 2021). This might facilitate the entry of virus to the CNS via the BBB. It is noteworthy that the BBB was reported to be disrupted in the animal models of PD which can lead to degeneration of neurons in the substantia nigra (Al-Bachari et al. 2020). MX1 localized in self-aggregations and generated Lewy bodies and swelling of neuronal processes in the substantia nigra of brain tissues in Parkinson’s patients (McDonough et al. 2017). Lewy bodies which contain misfolded proteins can then trigger the activation of T cells (Sulzer et al. 2017). IFN-alpha inducible (IRI) family members are closely related to the inflammatory immune response in COVID-19 and PD (Shaath et al. 2020). IFI6 is an immune-associated early predictor for PD (Lei et al. 2020; Yu et al. 2020). IFI35 is involved in type I interferon signaling pathway and have a vital role in inflammation response in SARS-CoV-2-infected cells (Hachim et al. 2020; Ziegler et al. 2020; Ong et al. 2021). On the other hand, IFI35 is upregulated in PD patients in response to INF response (Yu et al. 2020). IFI35 gene is expressed in the stratum and substantia nigra regions of the brain, and its de novo mutation is contributed to early onset of PD pathogenesis (Guo et al. 2018). IFI27 is an early predictor for SARS-COV-2 infection, and high-level expression of IF127 is associated with the presence of a high viral load (Shojaei et al. 2021). One study found elevated expression of IFI27 after microglial activation and neuroinflammation in progressive neurodegenerative disorders such as PD (Zhou et al. 2015). SARS-CoV-2 infection induces a strong activation of major constituents of the human complement subcomponent C1q (C1QA, C1QB, C1QC) (Ramlall et al. 2020; Santiesteban-Lores et al. 2021). These genes are upregulated in the microglial cells in the brain of PD patients. Activation of the complement system improves the removal of pathogens and products of tissue damage from the brain and is related to neuronal cell death in PD (Depboylu et al. 2011; Mariani et al. 2016; Itoh and Voskuhl 2017). ELANE gene codes destructive enzymes named neutrophil elastase that play a key role in host defense mechanism. This enzyme is highly overexpressed in naso-oropharyngeal and blood samples of COVID-19 patients. Neutrophil elastase can activate the spike (S) protein and mediate viral entry and pathogenesis of SARS-COV-2 (Belouzard et al. 2010; Akgun et al. 2020; Guéant et al. 2021). After an inflammatory insult to the CNS structure, the expression of neutrophil elastase increases, then degrades basal lamina and extracellular matrix (ECM) molecules and suppresses neurobehavioral recovery mechanisms (Stowe et al. 2009; Stock et al. 2018). Neutrophil elastase inhibitors could be new treatment options for COVID-19 patients (Mohamed et al. 2020).

Among these transcription factors, sex-determining region Y-box 2 (SOX2) has a critical role in the development and maintenance of neural stem/progenitor cell populations committed to becoming glial cells. SOX2 inhibits myelination in the peripheral nerves and maintains Schwann cells in a proliferative state, which is also associated with the influx of macrophages and increased neuroinflammation (Roberts et al. 2017). Interestingly, the expression level of SOX2 was found to be elevated in the brains of PD patients (Vedam-Mai et al. 2014). Nerve inflammation is one of the important factors in the onset or progression of PD (Pajares et al. 2020). XAF1 is a mitochondrial apoptosis activator that is upregulated in immune cells (T, B, natural killer, and dendritic cells) of COVID-19 patients that may be associated with increased apoptosis of these cells (Zhu et al. 2020; Gao et al. 2021). Furthermore, XAF1 expression is higher in the midbrain of PD patients (Gispert et al. 2015; Santiago and Potashkin 2017). IFN-α and IFN-β induced XAF1 mRNA expression and therefore induced cell apoptosis (Leaman et al. 2002). The expression of Runt-related transcription factor 1 (RUNX1) increases after SARS-CoV-2 infection (O’Hare et al. 2021). Interestingly, its overexpression is related to the progression of PD. RUNX1 increases the expression of leucine-rich repeat kinase 2 (Lrrk2) gene in immune cells and has a critical role in the pathogenesis of familial PD due to developing hyperactive inflammatory phenotype, neuronal toxicity, and cell apoptosis (Cook et al. 2017; Thomsen et al. 2021). Microphthalmia-associated transcription factor (MITF) is one of the key TFs with varying functions in cell homeostasis, cell cycle, and apoptosis. MITF is upregulated in immune cells and worsens severity of infection in an unknown way in COVID-19 patients (Bost et al. 2020; Ding et al. 2021; Jeong et al. 2021). Ubiquitin carboxyl-terminal hydrolase L1 (UCHL1) is expressed in neural cells and inhibits the stability of MITF by binding to the ubiquitinated protein. The ligase activity of UCHL1 is disrupted in PD, resulting in MITF overexpression and cell damage in these patients (Liu et al. 2002; Seo et al. 2017). The E26 transformation–specific (ETS) family transcription factor SPI1 upregulated in PBMCs of COVID-19 patients and is involved in the inflammatory process and modulates host immune systems of these patients (Fagone et al. 2020; Rahman et al. 2021). SPI1 plays a key role in the identity, differentiation, and specialized functions of microglia. Microglia rapidly activate in response to pro-inflammatory response. These activated microglia are accumulated in brain lesions of PD patients. SPI1 has many target functional genes in microglial cells including Spi1, Runx1, Irf8, Il34, Aif1, Csf1r, Csf1, Cx3cr1, Tyrobp, and Trem2 (Satoh et al. 2014). SPI1 induces cytokine release and microglial pro-inflammatory response (Pimenova et al. 2021). Therefore, misregulation of SPI1 target genes might lead to the establishment or development of PD due to the accumulation of activated microglia (Satoh et al. 2014). In addition, one multi-omic study identifies a single nucleotide polymorphism, rs10130373, within a microglia-specific peak; interrupts a SPI1 motif; and interfaces effectively with the promoter of the Rab interactor 3 (RIN3) gene. RIN3 plays an important role in the early endocytic pathway that needs microglial function, thereby playing a particularly critical role in progressive neurodegenerative disease (Kajiho et al. 2003; Corces et al. 2020).

hsa-mir-129–2-3p is the most significant miRNA in miRNA-hub gene regulatory networks. miR-129 is a brain-enriched miRNA, and its level increases in the peripheral blood lymphocytes of PD patients (Qin et al. 2016).

In the present study, an integrated bioinformatics approach was adopted to explore the possible risk of PD development after COVID-19 infection by investigating the common molecular mechanisms. By taking advantage of the holistic viewpoint of systems biology, we were able to consider every aspect of both diseases and infer novel hypotheses. Further supplementary studies need to be conducted to clarify the association between COVID-19 and PD, as, at the moment, there is little known regarding both of these disease entities. It is worth mentioning that contracting PD is a complex and age-dependent neurodegenerative disorder. Thus, it is encouraged to investigate infected COVID-19 patients’ years after their infection to estimate the probability of getting PD.

Conclusion

The current study aimed to investigate common regulators between COVID-19 and PD. Overall, our analysis highlights multiple mechanisms such as complement system, oxidative stress, activation microglia, cytokine storm, and activation of T cells by misfolded proteins which might be the potential links between both comorbidities. Nonetheless, as this is a thorough in silico analysis, the results of this work should be taken into account carefully. Further case reports and follow-up experiments of COVID-19 patients can corroborate these links.

Abbreviations

BP

Biological process

CC

Cellular component

CNS

Central nervous system

COVID-19

Coronavirus disease 2019

CS

Cytokine storm

ETS

E26 transformation–specific

GABA

Gamma-aminobutyric acid

GEO

Gene Expression Omnibus

GO

Gene Ontology

HCV

Hepatitis C virus

IFI6

Interferon alpha–inducible protein 6

IFI27

Interferon alpha–inducible protein 27

IFI35

Interferon alpha–inducible protein 35

IFN

Interferon

KEGG

Kyoto Encyclopedia of Genes and Genomes

Lrrk2

Leucine-rich repeat kinase 2

MF

Molecular functions

miRNA

microRNAs

MITF

Microphthalmia-associated transcription factor

MX

Myxovirus resistance genes

MX1

MX dynamin–like GTPase 1

PBMCs

Peripheral blood mononuclear cells

PD

Parkinson’s disease

PPIN

Protein-protein interaction network

RIN3

Rab interactor 3

RUNX1

Runt-related transcription factor 1

SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2

SOX2

Sex-determining region Y-box 2

TFs

Transcription factors

UCHL1

Ubiquitin carboxyl-terminal hydrolase L1

XAF1

X-linked inhibitor of apoptosis-associated factor 1

Author Contribution

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Aria Jahanimoghadam and Hadis Abdolahzade. The first draft of the manuscript was written by Aria Jahanimoghadam, Hadis Abdolahzadeh, and Niloofar Khoshdel rad, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Availability of Data and Materials

The data used in this study were downloaded from the GEO database.

Code Availability

The code that supports the findings of this study is available on request from the corresponding author.

Declarations

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Aria Jahanimoghadam and Hadis Abdolahzadeh equally contributed to this work.

References

  1. Akgun E, Tuzuner MB, Sahin B, et al. Proteins associated with neutrophil degranulation are upregulated in nasopharyngeal swabs from SARS-CoV-2 patients. PLoS ONE. 2020;15:1–10. doi: 10.1371/journal.pone.0240012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Al-Bachari S, Naish JH, Parker GJM, et al. Blood–brain barrier leakage is increased in Parkinson’s disease. Front Physiol. 2020;11:1–12. doi: 10.3389/fphys.2020.593026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Arunachalam PS, Wimmers F, Mok CKP, et al. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. Science. 2020;369:1210–1220. doi: 10.1126/SCIENCE.ABC6261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Auwul MR, Zhang C, Rahman MR, et al. Network-based transcriptomic analysis identifies the genetic effect of COVID-19 to chronic kidney disease patients: a bioinformatics approach. Saudi J Biol Sci. 2021;28:5647–5656. doi: 10.1016/j.sjbs.2021.06.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Awogbindin IO, Ben-Azu B, Olusola BA, et al. Microglial implications in SARS-CoV-2 infection and COVID-19: lessons from viral RNA neurotropism and possible relevance to Parkinson’s disease. Front Cell Neurosci. 2021 doi: 10.3389/fncel.2021.670298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Baba Y, Kuroiwa A, Uitti RJ, et al. Alterations of T-lymphocyte populations in Parkinson disease. Park Relat Disord. 2005;11:493–498. doi: 10.1016/j.parkreldis.2005.07.005. [DOI] [PubMed] [Google Scholar]
  7. Belouzard S, Madu I, Whittaker GR. Elastase-mediated activation of the severe acute respiratory syndrome coronavirus spike protein at discrete sites within the S2 domain. J Biol Chem. 2010;285:22758–22763. doi: 10.1074/jbc.M110.103275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bergmann CC, Lane TE, Stohlman SA. Coronavirus infection of the central nervous system: host-virus stand-off. Nat Rev Microbiol. 2006;4:121–132. doi: 10.1038/nrmicro1343. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bizzotto J, Sanchis P, Abbate M, et al. SARS-CoV-2 infection boosts MX1 antiviral effector in COVID-19 patients. iScience. 2020 doi: 10.1016/j.isci.2020.101585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bost P, Giladi A, Liu Y, et al. Host-viral infection maps reveal signatures of severe COVID-19 patients. Cell. 2020;181:1475–1488.e12. doi: 10.1016/j.cell.2020.05.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cartella SM, Terranova C, Rizzo V, et al. Covid-19 and Parkinson’s disease: an overview. J Neurol. 2021;268:4415–4421. doi: 10.1007/s00415-021-10721-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chams N, Chams S, Badran R, et al. COVID-19: a multidisciplinary review. Front Public Heal. 2020;8:1–20. doi: 10.3389/fpubh.2020.00383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Chernyak BV, Popova EN, Prikhodko AS, et al. COVID-19 and oxidative stress. Biochem. 2020;85:1543–1553. doi: 10.1134/S0006297920120068. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Chin CH, Chen SH, Wu HH, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8:1–7. doi: 10.1186/1752-0509-8-S4-S11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cook DA, Kannarkat GT, Cintron AF, et al. LRRK2 levels in immune cells are increased in Parkinson’s disease. Npj Park Dis. 2017;3:1–11. doi: 10.1038/s41531-017-0010-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Corces MR, Shcherbina A, Kundu S, et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat Genet. 2020;52:1158–1168. doi: 10.1038/s41588-020-00721-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Depboylu C, Schäfer MKH, Arias-Carrión O, et al. Possible involvement of complement factor C1q in the clearance of extracellular neuromelanin from the substantia nigra in Parkinson disease. J Neuropathol Exp Neurol. 2011;70:125–132. doi: 10.1097/NEN.0b013e31820805b9. [DOI] [PubMed] [Google Scholar]
  18. Diao B, Wang C, Wang R, et al. Human kidney is a target for novel severe acute respiratory syndrome coronavirus 2 infection. Nat Commun. 2021 doi: 10.1038/s41467-021-22781-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Dias V, Junn E, Mouradian MM. The role of oxidative stress in Parkinson’s disease. J Park Dis. 2013;3:461–491. doi: 10.3233/JPD-130230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Ding J, Hostallero DE, El Khili MR, et al. A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes’ interactions points to Neutrophil extracellular traps as mediators of thrombosis in COVID-19. PLoS Comput Biol. 2021;17:1–23. doi: 10.1371/journal.pcbi.1008810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Dorszewska J, Kowalska M, Prendecki M, et al. Oxidative stress factors in Parkinson’s disease. Neural Regen Res. 2021;16:1383–1391. doi: 10.4103/1673-5374.300980. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Eichel MEC, Steiner-Birmans B, Janah A, et al. A case of Parkinson disease after SARS-CoV-2 infection. Lancet Neurol. 2020;19:804–805. doi: 10.1016/S1474-4422(20)30305-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Eldeeb MA, Hussain FS, Siddiqi ZA. COVID-19 infection may increase the risk of parkinsonism – remember the Spanish flu. Cytokine Growth Factor Rev. 2020;54:6–7. doi: 10.1016/j.cytogfr.2020.06.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Fagone P, Ciurleo R, Lombardo SD, et al. Transcriptional landscape of SARS-CoV-2 infection dismantles pathogenic pathways activated by the virus, proposes unique sex-specific differences and predicts tailored therapeutic strategies. Autoimmun Rev. 2020 doi: 10.1016/j.autrev.2020.102571. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Gao T, Hu M, Zhang X, et al. Highly pathogenic coronavirus N protein aggravates lung injury by MASP-2-mediated complement over-activation. medRxiv. 2020 doi: 10.1101/2020.03.29.20041962. [DOI] [Google Scholar]
  26. Gao X, Liu Y, Zou S, et al. Genome-wide screening of SARS-CoV-2 infection-related genes based on the blood leukocytes sequencing data set of of patients with COVID-19. J Med Virol. 2021 doi: 10.1002/jmv.27093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gispert S, Brehm N, Weil J, et al. Potentiation of neurotoxicity in double-mutant mice with Pink1 ablation and A53T-SNCA overexpression. Hum Mol Genet. 2015;24:1061–1076. doi: 10.1093/hmg/ddu520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Goldknopf IL, Sheta EA, Bryson J, et al. Complement C3c and related protein biomarkers in amyotrophic lateral sclerosis and Parkinson’s disease. Biochem Biophys Res Commun. 2006;342:1034–1039. doi: 10.1016/j.bbrc.2006.02.051. [DOI] [PubMed] [Google Scholar]
  29. Guéant JL, Guéant-Rodriguez RM, Fromonot J, et al. Elastase and exacerbation of neutrophil innate immunity are involved in multi-visceral manifestations of COVID-19. Allergy Eur J Allergy Clin Immunol. 2021;76:1846–1858. doi: 10.1111/all.14746. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Guo JF, Zhang L, Li K, et al. Coding mutations in NUS1 contribute to Parkinson’s disease. Proc Natl Acad Sci U S A. 2018;115:11567–11572. doi: 10.1073/pnas.1809969115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Hachim MY, Al Heialy S, Hachim IY, et al. Interferon-induced transmembrane protein (IFITM3) is upregulated explicitly in SARS-CoV-2 infected lung epithelial cells. Front Immunol. 2020;11:1–9. doi: 10.3389/fimmu.2020.01372. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hayes MT. Parkinson’s disease and parkinsonism. Am J Med. 2019;132:802–807. doi: 10.1016/j.amjmed.2019.03.001. [DOI] [PubMed] [Google Scholar]
  33. Henderson AR, Wang Q, Meechoovet B, et al. DNA methylation and expression profiles of whole blood in Parkinson’s disease. Front Genet. 2021;12:1–17. doi: 10.3389/fgene.2021.640266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Henry J, Smeyne RJ, Jang H, et al. Parkinsonism and neurological manifestations of influenza throughout the 20th and 21st centuries. Park Relat Disord. 2010;16:566–571. doi: 10.1016/j.parkreldis.2010.06.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Itoh Y, Voskuhl RR. Cell specificity dictates similarities in gene expression in multiple sclerosis, Parkinson’s disease, and Alzheimer’s disease. PLoS ONE. 2017;12:1–11. doi: 10.1371/journal.pone.0181349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Jeong HH, Jia J, Dai Y, et al. Investigating cellular trajectories in the severity of COVID-19 and their transcriptional programs using machine learning approaches. Genes (basel) 2021 doi: 10.3390/genes12050635. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kajiho H, Saito K, Tsujita K, et al. RIN3: a novel Rab5 GEF interacting with amphiphysin II involved in the early endocytic pathway. J Cell Sci. 2003;116:4159–4168. doi: 10.1242/jcs.00718. [DOI] [PubMed] [Google Scholar]
  38. Kalia LV, Lang AE. Parkinson disease in 2015: evolving basic, pathological and clinical concepts in PD. Nat Rev Neurol. 2016;12:2–3. doi: 10.1038/nrneurol.2015.249. [DOI] [PubMed] [Google Scholar]
  39. Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, et al. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res. 2018;46:D239–D245. doi: 10.1093/nar/gkx1141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Khorsand B, Savadi A, Naghibzadeh M. SARS-CoV-2-human protein-protein interaction network. Informatics Med Unlocked. 2020;20:100413. doi: 10.1016/j.imu.2020.100413. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kuleshov MV, Jones MR, Rouillard AD, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44:W90–W97. doi: 10.1093/nar/gkw377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lachmann A, Xu H, Krishnan J, et al. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics. 2010;26:2438–2444. doi: 10.1093/bioinformatics/btq466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Leaman DW, Chawla-Sarkar M, Vyas K, et al. Identification of X-linked inhibitor of apoptosis-associated factor-1 as an interferon-stimulated gene that augments trail Apo2L-induced apoptosis. J Biol Chem. 2002;277:28504–28511. doi: 10.1074/jbc.M204851200. [DOI] [PubMed] [Google Scholar]
  44. Lei K, Zhang L, He Y, et al. Immune-associated biomarkers for early diagnosis of Parkinson’s disease based on hematological IncRNA–mRNA co-expression. Biosci Rep. 2020;40:1–13. doi: 10.1042/BSR20202921. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Liu Y, Fallon L, Lashuel HA, et al. The UCH-L1 gene encodes two opposing enzymatic activities that affect α-synuclein degradation and Parkinson’s disease susceptibility. Cell. 2002;111:209–218. doi: 10.1016/S0092-8674(02)01012-7. [DOI] [PubMed] [Google Scholar]
  46. Love M, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2 [DOI] [PMC free article] [PubMed]
  47. Mariani E, Frabetti F, Tarozzi A, et al. Meta-analysis of Parkinson’s disease transcriptome data using TRAM software: whole substantia nigra tissue and single dopamine neuron differential gene expression. PLoS ONE. 2016;11:1–21. doi: 10.1371/journal.pone.0161567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Matschke J, Lütgehetmann M, Hagel C, et al. Neuropathology of patients with COVID-19 in Germany: a post-mortem case series. Lancet Neurol. 2020;19:919–929. doi: 10.1016/S1474-4422(20)30308-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. McDonough A, Lee RV, Weinstein JR. Microglial interferon signaling and white matter. Neurochem Res. 2017;42:2625–2638. doi: 10.1007/s11064-017-2307-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Merello M, Bhatia KP, Obeso JA. SARS-CoV-2 and the risk of Parkinson’s disease: facts and fantasy. Lancet Neurol. 2021;20:94–95. doi: 10.1016/S1474-4422(20)30442-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Mohamed MMA, El-Shimy IA, El-Shimy IA, Hadi MA. Neutrophil elastase inhibitors: a potential prophylactic treatment option for SARS-CoV-2-induced respiratory complications? Crit Care. 2020;24:9–11. doi: 10.1186/s13054-020-03023-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Muñoz MD, de la Fuente N, Sánchez-capelo A. TGF-β/Smad3 signalling modulates GABA neurotransmission: implications in Parkinson’s disease. Int J Mol Sci. 2020 doi: 10.3390/ijms21020590. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. O’Hare M, Amarnani D, Whitmore HAB, et al. Targeting Runt-related transcription gactor 1 prevents pulmonary fibrosis and reduces expression of severe acute respiratory syndrome coronavirus 2 host mediators. Am J Pathol. 2021;191:1193–1208. doi: 10.1016/j.ajpath.2021.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Olsen LK, Dowd E, McKernan DP. A role for viral infections in Parkinson’s etiology? Neuronal Signal. 2018;2:1–14. doi: 10.1042/ns20170166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Ong EZ, Kalimuddin S, Chia WC, et al. Temporal dynamics of the host molecular responses underlying severe COVID-19 progression and disease resolution. EBioMedicine. 2021;65:103262. doi: 10.1016/j.ebiom.2021.103262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Ortelli P, Ferrazzoli D, Sebastianelli L, et al. Neuropsychological and neurophysiological correlates of fatigue in post-acute patients with neurological manifestations of COVID-19: insights into a challenging symptom. J Neurol Sci. 2021;420:117271. doi: 10.1016/j.jns.2020.117271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Pajares M, Rojo AI, Manda G, et al. Inflammation in Parkinson’s disease: mechanisms and therapeutic implications. Cells. 2020;9:1–32. doi: 10.3390/cells9071687. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Park A, Iwasaki A. Type I and type III interferons – induction, signaling, evasion, and application to combat COVID-19. Cell Host Microbe. 2020;27:870–878. doi: 10.1016/j.chom.2020.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Pimenova AA, Herbinet M, Gupta I, et al. Alzheimer’s-associated PU.1 expression levels regulate microglial inflammatory response. Neurobiol Dis. 2021;148:105217. doi: 10.1016/j.nbd.2020.105217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Qin G, Mallik S, Mitra R, et al. MicroRNA and transcription factor co-regulatory networks and subtype classification of seminoma and non-seminoma in testicular germ cell tumors. Sci Rep. 2020;10:1–14. doi: 10.1038/s41598-020-57834-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Qin H, Buckley JA, Li X, et al. Inhibition of the JAK/STAT pathway protects against α-synuclein-induced neuroinflammation and dopaminergic neurodegeneration. J Neurosci. 2016;36:5144–5159. doi: 10.1523/JNEUROSCI.4658-15.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Rahman MR, Islam T, Shahjaman M, et al. Discovering common pathogenetic processes between COVID-19 and diabetes mellitus by differential gene expression pattern analysis. Brief Bioinform. 2021;22:1–12. doi: 10.1093/bib/bbab262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Ramlall V, Thangaraj PM, Meydan C, et al. Immune complement and coagulation dysfunction in adverse outcomes of SARS-CoV-2 infection. Nat Med. 2020;26:1609–1615. doi: 10.1038/s41591-020-1021-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Roberts SL, Dun XP, Doddrell RDS, et al. Sox2 expression in schwann cells inhibits myelination in vivo and induces influx of macrophages to the nerve. Dev. 2017;144:3114–3125. doi: 10.1242/dev.150656. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Santiago JA, Potashkin JA. Blood transcriptomic meta-analysis identifies dysregulation of hemoglobin and iron metabolism in Parkinson’ disease. Front Aging Neurosci. 2017;9:1–8. doi: 10.3389/fnagi.2017.00073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Santiesteban-Lores LE, Amamura TA, da Silva TF, et al. A double edged-sword - the complement system during SARS-CoV-2 infection. Life Sci. 2021;272:1–9. doi: 10.1016/j.lfs.2021.119245. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Satoh JI, Asahina N, Kitano S, Kino Y. A comprehensive profile of ChIp-Seq-based PU.1/Spi1 target genes in microglia. Gene Regul Syst Bio. 2014;8:127–139. doi: 10.4137/GRSB.S19711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Seo EY, Jin SP, Sohn KC, et al. UCHL1 regulates melanogenesis through controlling MITF stability in human melanocytes. J Invest Dermatol. 2017;137:1757–1765. doi: 10.1016/j.jid.2017.03.024. [DOI] [PubMed] [Google Scholar]
  69. Shaath H, Vishnubalaji R, Elkord E, Alajez NM. Single-cell transcriptome analysis highlights a role for neutrophils and inflammatory macrophages in the pathogenesis of severe COVID-19. Cells. 2020;9:1–19. doi: 10.3390/cells9112374. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Shojaei M, Shamshirian A, Monkman J, et al. IFI27 transcription is an early predictor for COVID-19 outcomes; a multi-cohort observational study. medRxiv. 2021 doi: 10.1101/2021.10.29.21265555. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Song E, Zhang C, Israelow B, et al. Neuroinvasion of SARS-CoV-2 in human and mouse brain. J Exp Med. 2021 doi: 10.1084/JEM.20202135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Stock AJ, Kasus-Jacobi A, Pereira HA. The role of neutrophil granule proteins in neuroinflammation and Alzheimer’s disease. J Neuroinflammation. 2018;15:1–15. doi: 10.1186/s12974-018-1284-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Stowe AM, Adair-Kirk TL, Gonzales ER, et al. Neutrophil elastase and neurovascular injury following focal stroke and reperfusion. Neurobiol Disord. 2009;35:82–90. doi: 10.1016/j.nbd.2009.04.006.Neutrophil. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Sulzer D, Alcalay RN, Garretti F, et al. T cells from patients with Parkinson’s disease recognize α-synuclein peptides. Nature. 2017;546:656–661. doi: 10.1038/nature22815. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Sulzer D, Antonini A, Leta V, et al. COVID-19 and possible links with Parkinson’s disease and parkinsonism: from bench to bedside. Npj Park Dis. 2020 doi: 10.1038/s41531-020-00123-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Szcześniak D, Gładka A, Misiak B, et al. The SARS-CoV-2 and mental health: from biological mechanisms to social consequences. Prog Neuro-Psychopharmacology Biol Psychiatry. 2021 doi: 10.1016/j.pnpbp.2020.110046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Thomsen I, Kunowska N, de Souza R et al (2021) RUNX1 controls the dynamics of cell cycle entry of naïve resting B cells by regulating expression of cell cycle and immunomodulatory genes in response to BCR stimulation
  78. Tsai HH, Liou HH, Muo CH, et al. Hepatitis C virus infection as a risk factor for Parkinson disease: a nationwide cohort study. Neurology. 2016;86:840–846. doi: 10.1212/WNL.0000000000002307. [DOI] [PubMed] [Google Scholar]
  79. Vedam-Mai V, Gardner B, Okun MS, et al. Increased precursor cell proliferation after deep brain stimulation for Parkinson’s disease: a human study. PLoS ONE. 2014;9:1–8. doi: 10.1371/journal.pone.0088770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Warde-Farley D, Donaldson SL, Comes O, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38:214–220. doi: 10.1093/nar/gkq537. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. WHO (2021) General’s opening remarks at the media briefing on COVID‐19 ‐ 11 March 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
  82. Yamada T, Horisberger M, Kawaguchi N, et al. Immunohistochemistry using antibodies to α-interferon and its induced protein, MxA, in Alzheimer’s and Parkinson’s disease brain tissues. Neurosci Lett. 1994;181:61–64. doi: 10.1016/0304-3940(94)90560-6. [DOI] [PubMed] [Google Scholar]
  83. Yang L, Xie X, Tu Z, et al. The signal pathways and treatment of cytokine storm in COVID-19. Signal Transduct Target Ther. 2021;6:1–20. doi: 10.1038/s41392-021-00679-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Yu F, Sen LZ, Chen LH, et al. Identification of biomolecular information in rotenone-induced cellular model of Parkinson’s disease by public microarray data analysis. J Comput Biol. 2020;27:888–903. doi: 10.1089/cmb.2019.0151. [DOI] [PubMed] [Google Scholar]
  85. Zhou G, Soufan O, Ewald J, et al. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019;47:W234–W241. doi: 10.1093/nar/gkz240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Zhou X, Zöller T, Krieglstein K, Spittau B. TGFβ1 inhibits IFNγ-mediated microglia activation and protects mDA neurons from IFNγ-driven neurotoxicity. J Neurochem. 2015;134:125–134. doi: 10.1111/jnc.13111. [DOI] [PubMed] [Google Scholar]
  87. Zhu L, Yang P, Zhao Y, et al. Single-cell sequencing of peripheral mononuclear cells reveals distinct immune response landscapes of COVID-19 and influenza patients. Immunity. 2020;53:685–696.e3. doi: 10.1016/j.immuni.2020.07.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Ziegler CGK, Allon SJ, Nyquist SK, et al. SARS-CoV-2 receptor ACE2 is an interferon-stimulated gene in human airway epithelial cells and is detected in specific cell subsets across tissues. Cell. 2020;181:1016–1035.e19. doi: 10.1016/j.cell.2020.04.035. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data used in this study were downloaded from the GEO database.

The code that supports the findings of this study is available on request from the corresponding author.

Articles from Journal of Molecular Neuroscience are provided here courtesy of Nature Publishing Group

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