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  • Original Article
  • OPEN ACCESS

Elucidating the Role of a Shared lncRNA-miRNA-mRNA Network in Exacerbating Parkinson’s Disease Symptoms in the Context of COVID-19 Infection

  • Maryam Yousefi1,* ,
  • Motahare-Sadat Hashemi2,
  • Maryam Peymani3,* ,
  • Kamran Ghaedi2,4,
  • Shiva Irani1 and
  • Masoud Etemadifar5
 Author information  Cite
Gene Expression   2024;23(2):83-97

doi: 10.14218/GE.2023.00103

Abstract

Background and objectives

Parkinson’s disease (PD) is a common neurodegenerative disorder with unclear molecular mechanisms. Noncoding RNAs, such as microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), have been identified as critical regulators of gene expression. This study aimed to investigate the triple network of lncRNA-miRNA-mRNA, known as competing endogenous RNAs (ceRNAs), and to identify essential lncRNAs that regulate PD-related gene expression through their target miRNAs. The study also identified a common triple network between COVID-19 and PD that may contribute to exacerbating PD symptoms.

Methods

A bioinformatics approach was employed to construct a PD ceRNA network using common PD genes, miRNAs and lncRNAs with the highest interaction with their targets. Also, a PD-COVID-19 triple network was constructed by integrating PD network nodes into the COVID-19 network.

Results

The PD ceRNA network comprised 34 nodes, including 12 lncRNAs, 16 miRNAs with interconnections and six mRNAs, some of which were related to COVID-19. The network showed parallel expression of the SNCA and PARK7 genes as well as the NEAT1 and MALAT1 lncRNAs in both PD and COVID-19.

Conclusions

This study provide insights into the molecular mechanisms underlying the worsening of symptoms in PD patients with COVID-19. The PD and COVID-19 ceRNA network indicates that coronavirus could worsen PD symptoms by altering the expression of some genes related to PD. Therefore, COVID-19 could dysregulate the common RNAs involved in PD through lncRNAs, miRNAs.

Keywords

Parkinson’s disease, COVID-19, Long noncoding RNA, miRNA, NEAT1, MALAT1

Introduction

Dr. James Parkinson first described Parkinson’s disease (PD) in 1817 as “shaking palsy”.1 PD is a progressive age-dependent and the most prevalent neurodegenerative disease after Alzheimer’s disease.2 It is characterized by both motor and nonmotor features. Motor features are caused by degeneration of dopaminergic neurons within the substantia nigra pars compacta, corpus striatum, brain cortex, and cytoplasmic protein components called Lewy bodies3,4; nonmotor features are neuronal losses in nondopaminergic areas.5 It is suggested that nonmotor features may start before the onset of motor features.6

Many environmental factors, such as nutrient intake,7,8 dietary exposure to noxious compounds,9,10 environmental toxins (like carbon disulfide, cyanide, herbicides, organic solvents and pesticides),11 and methcathinone, contribute to PD incidence.12

Genetic and epigenetic factors can also contribute to PD incidence.13 Previous investigations have indicated that many noncoding RNAs (ncRNAs) play critical roles in PD-specific physiological and pathological processes.14,15 NcRNAs include various classes, such as microRNAs (miRNAs) and long noncoding RNAs (lncRNAs). In other words, miRNAs and lncRNAs are assumed to be associated with PD.16,17

MiRNAs are single-stranded RNA molecules with about 22 nucleotides. They significantly affect biological processes, such as negatively regulating mRNA transcripts, inhibiting the initiation and elongation of target mRNA translation, and degrading or destabilizing mRNAs.18 LncRNAs have more than 200 nucleotides. The transcription of lncRNAs is performed by RNA polymerase II, which is similar to that of coding RNAs. The 5′ end capping and 3′ end processing, splicing, polyadenylation and exporting to the cytoplasm of lncRNAs are similar to those of coding RNAs. Generally, lncRNAs cannot be translated, but a limited number of them can produce small peptides.19 LncRNAs interact with proteins, DNAs and other RNAs, enabling them to regulate diverse cellular processes. They can also control gene expression at both the transcriptional and post-transcriptional levels.20–22

Besides the fact that miRNAs and lncRNAs have a regulatory role in mRNA expression, they also interact with each other. MiRNAs can control the decay of lncRNAs since many lncRNAs are involved in cell functions. Thus, changes in their abundance directly alter cellular responses in physiological and pathological processes. Also, lncRNAs can influence miRNA levels and functions through the sponge and decay of miRNAs. For miRNA sponges, lncRNAs can act as competing endogenous RNAs similar to miRNA target sequences. Moreover, lncRNAs can compete with miRNAs for binding to target mRNAs. Another way by which lncRNAs can affect miRNAs is by producing miRNAs through lncRNAs.19

Recent evidence suggests that viral infections, including COVID-19, may have an impact on the development and progression of neurodegenerative diseases like PD.23 For instance, Fazzini et al. reported that coronavirus antibodies were present in the cerebrospinal fluid of PD patients.24 SARS-CoV-2, the virus that causes COVID-19, can also enter the nerve cells of the brain or spinal cord through angiotensin-converting enzyme 2 (ACE2) receptors.25 ACE2 receptors are highly expressed in dopamine neurons.25 Also, several studies have demonstrated the worsening of motor and nonmotor symptoms and, possibly, higher mortality in PD patients infected with SARS-CoV-2, as compared with those not exposed to SARS-CoV-2.26–29 Cilia et al. also investigated the clinical features of 141 PD patients residing in Lombardy, finding 12 COVID-19 cases (8.5%).30 So, the changes in clinical features from January 2020 to April 2020 were investigated and then compared with those of 36 PD controls matched for sex, age and disease duration. The findings showed that motor and nonmotor symptoms were significantly intensified in the COVID-19 group. Therefore, we investigated the possible associations of coding and ncRNAs with this worsening of symptoms in PD patients with COVID-19.

To enhance the power and generalizability of PD results, meta-analysis can be employed to obtain and summarize favorable information from existing studies. However, a limited number of meta-studies have been performed on the role of miRNAs and lncRNAs in regulating PD-related gene expression. Therefore, the present study aimed to construct a triple miRNA-mRNA-lncRNA competing endogenous RNA (ceRNA) network related to PD. Additionally, we aimed to identify the specific RNA molecules that show altered expression patterns in PD patients with COVID-19 compared to those without COVID-19 and to investigate the reasons behind the worsening symptoms observed in PD patients with COVID-19. We expected that COVID-19 could dysregulate the common RNAs involved in PD through lncRNAs, miRNAs, and finally, the expression of essential mRNAs. On the other hand, COVID-19 could contribute to altered biological processes like neuroinflammation, an impaired immune response, and oxidative stress, and these processes are known to play crucial roles in PD pathogenesis. By investigating the specific dysregulated RNAs and their involvement in altered biological processes, we aim to provide insights into the molecular mechanisms underlying the worsening of symptoms in PD patients with COVID-19. This knowledge could potentially lead to the identification of novel therapeutic targets and strategies for managing PD and its exacerbation in the context of viral infections like SARS-CoV-2.

Methods

Data sources and collection

The method employed in this study is shown in Figure 1. PD-related genes were collected from DisGeNET (v.7.0), which is a platform containing one of the largest publicly available collections of genes and variants associated with human diseases. We employed the following keywords in our study: “Parkinson”, “AD (Autosomal Dominant) juvenile”, “Parkinson disease 2”, AR (Autosomal Recessive) juvenile”, “late onset Parkinson’s disease”, and “young onset Parkinson’s disease”.31 DisGeNET integrates data from diverse sources such as curated repositories, GWAS catalogs, animal models, and scientific literature.

The workflow of the methods and the results and network construction in the study.
Fig. 1  The workflow of the methods and the results and network construction in the study.

PD, Parkinson’s disease; lncRNAs, long noncoding RNAs; miRNAs, microRNAs; CeRNA, competing endogenous RNA.

The common genes of these PD types were identified using a Venn diagram generated by bioinformatics and evolutionary genomics tools, which can show the intersection of the input lists of genes (http://bioinformatics.psb.ugent.be/webtools/Venn/ ).

MiRNAs involved in PD were identified by a literature review and the MiRTarBase database (v.8.0).32–66 Then, miRNAs having strongly validated interactions with PD-related genes were selected, as observed in the literature or reported in the MiRTarBase database, with strong validation (Reporter assay, Western blot, qPCR). MiRTarBase is a curated database that compiles verified miRNA-target interactions validated through biological experiments. These interactions were gathered from a variety of reliable sources, including articles and CLIP-seq data. This database serves as a valuable resource for studying miRNA-related diseases and has potential applications in disease treatment and drug development.

The verified target lncRNAs of the selected miRNAs were screened using DIANA-LncBase Experimental (v.2).67 LncRNAs in the mentioned database were classified into various biotypes based on their positions in regard to nearby protein-coding loci. In this study, we focused on the lncRNA biotype with a score above 0.6 in the experimental module of DIANA-LncBase Experimental (v.2). The LncBase database utilizes a CLIP-Seq-guided algorithm called the microCLIP framework to analyze AGO-CLIP-Seq libraries. This algorithm enables the identification and cataloging of miRNA binding events in lncRNAs. By leveraging CLIP-Seq data, LncBase provides valuable insights into the interactions between miRNAs and lncRNAs, contributing to our understanding of their functional roles and potential implications in various biological processes. Based on the miRcode database (v.11), the seed position and seed type of the selected miRNAs were predicted in the target lncRNAs.68

Functional enrichment analysis and construction of ceRNA networks

For the common gene list obtained from the Venn diagram, ontology categories of the related disease and GO biological pathway enrichment analysis were performed according to the Metascape database.69 Metascape is a web-based portal for gene list annotation and analysis designed for experimental biologists. It offers a comprehensive resource to interpret and explore gene lists from various experiments. Also, the molecular functions of the candidate genes, as well as the biological processes of the candidate miRNAs, were summarized and visualized using REVIGO (adjusted p < 0.05).70 REVIGO is a web server specifically designed to summarize long lists of Gene Ontology (GO) terms. It utilizes a simple clustering algorithm that relies on semantic similarity measures to identify a representative subset of terms. By doing so, REVIGO helps researchers overcome the challenge of dealing with lengthy and complex lists of GO terms, making the information more concise and intelligible. The clustergram of the perturbations of diseases, including PD and COVID-19, was visualized using the Enrichr database based on the altered expression of the candidate lncRNAs and mRNAs.71 Enrichr provides a diverse collection of gene set libraries for analysis and download. It offers a wide range of gene sets associated with various biological processes, pathways, diseases, and experimental conditions.

Two networks, including mRNA-miRNA and miRNA-lncRNA networks, were used to introduce a PD lncRNA-miRNA-mRNA construct, i.e., an endogenous RNA network. The mRNA-miRNA network was constructed and visualized using Cytoscape software (v3.7.2).72 Cytoscape is an open-source software project that integrates biomolecular interaction networks with high-throughput expression data and other molecular states. It provides a unified conceptual framework for visualizing and analyzing complex biological networks, allowing researchers to gain insights into the relationships and interactions between biomolecules. Then, the network was analyzed, and the miRNAs that interact with two or more mRNAs were selected. Subsequently, a network of the selected miRNAs and lncRNAs described in Section 2.1 was constructed using Cytoscape software. The resulting network was analyzed, and lncRNAs that interact with more than two miRNAs were chosen. Finally, the ceRNA network was constructed and visualized using Cytoscape based on the relationships between the common mRNAs (extracted from the Venn diagram related to PD types), miRNAs and lncRNAs having the most interactions with mRNAs and miRNAs.

The candidate mRNAs (eight genes) and lncRNAs (12 lncRNAs) related to PD were given as input in the Enrichr database. Those related to COVID-19 were specified by the database. So, the ceRNA network nodes simultaneously involved in the PD network and associated with COVID-19 were selected to establish communication between PD and COVID-19. Then, the common genes between PD and COVID-19 were selected. Finally, a ceRNA network was constructed for PD and COVID-19.

Results

Identification of the candidate genes in PD

The DisGeNET database released almost 2079 PD-related genes, of which 360 genes had a score greater than or equal to 0.1 (the DisGeNET score for a gene-disease association reflects its recurrence through all databases). The 360 genes identified via Cytoscape are summarized in Figure 2.31,72 Moreover, genes related to the PD types “Parkinson”, “AD juvenile”, “Parkinson disease 2, AR juvenile”, “late onset Parkinson’s disease”, and “young onset Parkinson’s disease” were collected from the DisGeNET database. Then, eight common genes among them, including PINK1, LRRK2, PARK7, SNCA, PRKN, ATP13A2, GBA and SLC18A2 genes, were reported as a Venn diagram, as shown in Figure 3.

PD-related genes with scores greater than or equal to 0.1.
Fig. 2  PD-related genes with scores greater than or equal to 0.1.

The node fill color and size of the genes are related to their score. The scores of SNCA, PARK7, DDC, DRD2, ATP13A2, MAOB, PRKN, PINK1, SLC18A2, TH, DRD1 and IGF1R mRNAs range from 0.7 to 0.56, and LRRK2, GAK, GBA, MAPT, BST1 and HLA-DRA mRNAs have scores greater than or equal to 0.5. PD, Parkinson’s disease.

Venn diagram of the genes related to PD, AD juveniles, PD2 and AR juveniles, and late-onset and young-onset PD.
Fig. 3  Venn diagram of the genes related to PD, AD juveniles, PD2 and AR juveniles, and late-onset and young-onset PD.

The PINK1, LRRK2, PARK7, SNCA, PRKN, ATP13A2, GBA and SLC18A2 genes are common to all of them. AD, Autosomal Dominant; PD, Parkinson’s disease; AR, Autosomal Recessive.

Interaction between mRNAs and miRNAs

The construction of the mRNA-miRNA network was derived from the correlation among the 2079 mRNAs (obtained from the DisGeNET database) and 125 miRNAs (obtained from the literature and the MiRTarBase database), as validated by strong methods. As shown in Figure 4, 32 mRNAs out of 2079 mRNAs were the targets of the abovementioned 125 miRNAs, described as a network; there was overlap between six candidate genes (described in Section 3.1, the glucocerebrosidase and SLC18A2 genes did not interact with the mentioned miRNAs, and these genes were removed in the next step) and 32 mRNAs. Among the miRNAs listed, we identified 61 miRNAs that interact with two or more mRNAs; among them, 23 miRNAs interacted with the abovementioned six genes using the Cytoscape software (Supplementary Excel 1).

mRNA-miRNA network analysis revealed that 32 mRNAs were targets of 125 miRNAs.
Fig. 4  mRNA-miRNA network analysis revealed that 32 mRNAs were targets of 125 miRNAs.

The 23 miRNAs interacted with six candidate mRNAs, namely, hsa-miR-26b-5p, hsa-miR-7-5p, hsa-miR-335-5p, hsa-miR-148b-3p, hsa-miR-181a-5p, hsa-miR-582-5p, hsa-miR-23a, hsa-miR-144, hsa-miR-221, hsa-miR-488, hsa-miR-133b, hsa-miR-27a-3p, hsa-miR-27b-3p, hsa-miR-4435, hsa-miR-4755-3p, hsa-miR-34c, hsa-miR-92a-3p, hsa-miR-4438, hsa-miR-4478, hsa-miR-4419b, hsa-miR-3929, hsa-miR-30c-5p and hsa-miR-24-3p. mRNA, messenger RNAs; miRNA, microRNAs.

Interaction between miRNAs and lncRNAs

Based on the experimental results of DIANA tool, we obtained 209 lncRNAs interacting with 23 candidate miRNAs. The miRNA-lncRNA network was constructed using Cytoscape software (Fig. 5).72 Among the mentioned lncRNAs, we identified 18 lncRNAs that interact with more than two miRNAs (Supplementary Excel 2). The last step for the construction of a PD ceRNA network was to gather all selected mRNAs, miRNAs and lncRNAs together. We identified six mRNAs (described in Section 3.2). On the other hand, 16 out of the 23 candidate miRNAs that interact with these six mRNAs and target the abovementioned lncRNAs were selected; also, 12 out of the 18 lncRNAs that interact with the final selected miRNAs and mRNAs were selected.

miRNA-lncRNA network analysis revealed that 18 lncRNAs, <italic>NEAT1, XIST, MALAT1, CASC7, RP11-314B1.2, LINC01355, MEG3, RP11-361F15.2, RP11-819C21.1, IPW, LINC00662, LINC00938, LINC01004, LINC01128, LINC01314, MIAT, RP11-215G15.5</italic> and <italic>RP11-54O7.1,</italic> had the most interactions with miRNAs.
Fig. 5  miRNA-lncRNA network analysis revealed that 18 lncRNAs, NEAT1, XIST, MALAT1, CASC7, RP11-314B1.2, LINC01355, MEG3, RP11-361F15.2, RP11-819C21.1, IPW, LINC00662, LINC00938, LINC01004, LINC01128, LINC01314, MIAT, RP11-215G15.5 and RP11-54O7.1, had the most interactions with miRNAs.

lncRNAs, long noncoding RNAs; miRNA, microRNAs.

GO analysis and PD

Gene Ontology enrichment analysis of Metascape demonstrated that 87.50% of the candidate genes were negatively related to neuron death, 75% of them were involved in the regulation of cellular catabolic processes, and 62.50% of them were negatively related to the neuronal apoptotic process (Fig. 6a).73 The DisGeNET graph bar showed the number of selected genes involved in a different type of PD and other diseases (Fig. 6b). Significantly enriched GO terms (adjusted p < 0.05) were summarized and visualized using REVIGO. The “molecular functions of the selected genes” were the binding category and catalytic activity (Fig. 6c).70

Gene ontology and pathway enrichment analysis.
Fig. 6  Gene ontology and pathway enrichment analysis.

Metascape bar graphs showing (a) biological processes and (b) a summary of the enrichment analysis of the candidate mRNAs related to different diseases. (c, d) GO enrichment analysis results were summarized and visualized as a scatter plot using REVIGO by clustering the significant genes (adjusted p < 0.05). The size of each bubble shows the GO terms with more significant p-values, (c) molecular functions of the candidate mRNAs, and (d) the GO biological processes of the candidate miRNAs. (e, f) Clustergrams showing related diseases such as PD and COVID-19 with differential expression of the candidate lncRNAs using the Enrichr database, (e) the relationship between the overexpression of lncRNAs and PD, and (f) the association between COVID-19 and differential expression of lncRNAs. ATP, Adenosine triphosphate; MALAT1, Metastasis associated in lung adenocarcinoma transcript 1; MEG3, maternally expressed 3; MIAT, myocardial infarctionassociated transcript; NEAT1, Nuclear Enriched Abundant Transcript 1; P38MAPK, p38 mitogen-activated protein kinases ; PD, Parkinson’s disease; REVIGO, REduce and Visualize Gene Ontology; XIST, X-inactive specific transcript.

The GO biological processes of the candidate miRNAs were summarized and visualized using REVIGO. The biological process of the mentioned miRNAs had a regulatory role in gene expression, the cell cycle, the apoptosis signaling pathway, angiogenesis, protein serine/threonine kinase activity, nitric oxide biosynthesis, interleukin-1 alpha, and interferon-beta production (Fig. 6d).70

Enrichr (Crowd) analysis of the candidate lncRNAs showed that the differential expression of these lncRNAs was related to various diseases. The GSE7621 microarray with the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array) was then used to obtain gene expression data from the substantia nigra in the postmortem human brain of 940 samples. This dataset reported an association between high and low expression of the candidate lncRNAs and various diseases. As shown in Fig. 6e, the clustergram indicated the overexpression of MALAT1 and NEAT1 in relation to PD (p = 0.019).71

GO analysis and COVID-19

We analyzed the expression of the six genes in the presence of COVID-19 using the Enrichr database (Diseases/Drugs). This section of the Enrichr database (Diseases/Drugs) uses the Gene Expression Omnibus (GEO) dataset and presents the results of a list of genes entered in the Enrichr database. Therefore, we used the results of several GEO datasets from the Enrichr database to report the expression profiles of our candidate genes and lncRNAs in COVID-19.

The GDS1028 dataset reports the differences in the expression of human peripheral blood mononuclear cell (PBMC) genes in 10 adult patients with severe acute respiratory syndrome (SARS) compared to healthy individuals using microarray data analysis. Expression levels of SNCA and PARK7 were higher in patients with SARS than in healthy individuals (p = 0.004).

Besides, the associations of candidate lncRNAs with COVID-19 were analyzed using the Enrichr database (Legacy) based on GEO datasets. GSE150847 displayed the gene expression profiling of SARS-CoV-2-infected transduced human ACE2 mouse models using high throughput sequencing. Enrichr database based on GSE150847 also revealed that the up-regulation of MALAT1, NEAT1 and XIST was related to SARS-CoV-2 in the lungs of Ad5-ACE2-transduced mice compared to Ad5-Empty-transduced mice (p = 0.004). GSE148729 also provided the gene expression profiling of SARS-CoV-1/2 infected human epithelial cell line Calu-3 at bulk and single-cell levels using high throughput sequencing. Enrichr database based on GSE148729 verified the up-regulation of NEAT1 and MALAT1 in SARS-CoV-1/2-infected human cell lines compared with that in uninfected cell lines ( = 0.046) (Fig. 6f).

CeRNA network of mRNA-miRNA-lncRNA

The ceRNA network for PD was developed by the interaction between mRNA, miRNA and lncRNA. To design a ceRNA network related to PD, three steps were required to select mRNAs, miRNAs and lncRNAs, as detailed above. The final network consisted of 34 nodes, including six mRNAs, 16 miRNAs and 12 lncRNAs that interact with each other (Fig. 7) (Supplementary Excel 3). Moreover, we used the candidate mRNAs (six genes) and lncRNAs (12 lncRNAs) related to PD to construct the PD and COVID-19 ceRNA network. As mentioned, the expression levels of SNCA and PARK7 in the candidate mRNAs were related to COVID-19. Additionally, MALAT1 and NEAT1, among the candidate lncRNAs (12 lncRNAs), were related to COVID-19. The expression levels of the SNCA and PARK7 genes, as well as the expression levels of the NEAT1 and MALAT1 lncRNAs, were upregulated in both PD and COVID-19. This parallel upregulation suggests a positive correlation between the expression of these genes.

The PD ceRNA network showing six mRNAs common to PD types, 16 miRNAs interacting with the mentioned mRNAs, and 12 lncRNAs having the most interactions with the mentioned miRNAs.
Fig. 7  The PD ceRNA network showing six mRNAs common to PD types, 16 miRNAs interacting with the mentioned mRNAs, and 12 lncRNAs having the most interactions with the mentioned miRNAs.

ceRNA, competing endogenous RNA; lncRNAs, long noncoding RNAs; MALAT1, Metastasis associated in lung adenocarcinoma transcript 1; miRNAs, microRNAs; mRNAs, messenger RNAs; NEAT1, Nuclear Enriched Abundant Transcript 1; PD, Parkinson’s disease.

At this point, we focused on lncRNAs because several studies have reported their association with COVID-19.74–76 In this section, XIST was omitted because changes in its expression were reported in infected mouse cells (not human cells). In addition, as shown in Figure 6f, several studies have confirmed some differences in the expression of MALAT1 and NEAT1 in different human cells or human PBMCs infected with SARS-CoV-2.77–79 Thus, a PD and COVID-19 ceRNA network was constructed using SNCA and PARK7 as mRNA nodes, and MALAT1 and NEAT1 as lncRNA nodes, incorporating the candidate miRNAs linked to these genes and lncRNAs (Fig. 8).

The PD and COVID-19 ceRNA network showing two mRNAs, eight miRNAs and two lncRNAs related to PD and COVID-19.
Fig. 8  The PD and COVID-19 ceRNA network showing two mRNAs, eight miRNAs and two lncRNAs related to PD and COVID-19.

lncRNAs, long noncoding RNAs; MALAT1, Metastasis associated in lung adenocarcinoma transcript 1; miRNAs, microRNAs; NEAT1, Nuclear Enriched Abundant Transcript 1; PARK7, parkinsonism associated deglycase; PD, Parkinson’s disease; SNCA, synuclein alpha.

Discussion

PD can be regarded as an age-dependent neurodegenerative disorder affecting movement abilities.80 The regulation of gene expression is one of the major factors in cellular homeostasis that can be impaired in PD.81 NcRNAs are risk factors that play a role in altering PD gene expression and PD.82 There were major differences in the clinical presentation related to age at onset. Juvenile-onset PD is usually defined as the age at disease onset before 20 years. Early-onset PD refers to patients who present motor symptoms before age 40; the most reported age for late-onset PD refers to that after 60 years.83 Patients with early-onset PD had more benign disease progression, delayed onset of falls and longer survival compared to those with late-onset PD.84 Additionally, a comparison between early-onset and late-onset patients showed that early-onset patients were less likely to have gait disturbance as the presenting symptom and had more pronounced rigidity and bradykinesia compared to late-onset patients.84

Some studies have reported that the overexpression of SNCA and PARK7 is related to PD. Chiba-Falek et al. examined the expression levels of SNCA-mRNA in seven sporadic PD brain samples and seven normal controls using real-time PCR.85 They reported that, on average, SNCA mRNA levels in PD brains were almost fourfold greater than those in controls. Also, Miller et al. showed that the amount of SNCA protein in the blood and the level of SNCA in the brain tissue were both doubled compared to the controls.86 However, another study detecting autoantibodies in the serum of patients with PD reported increased serum levels of SNCA-reacting antibodies.87 Yalçınkaya et al. also reported that the PARK7 (DJ-1) expression level was increased in the PBMCs of PD patients.88 Besides, Hu et al. reported the important impact of COVID-19 on neurodegenerative diseases such as PD.89 On the other hand, proinflammatory cytokine levels, as well as the proinflammatory state, were increased in patients with COVID-19. This was closely followed by SNCA dysfunction and accumulation, thus promoting the development and progression of neurodegenerative diseases like PD.90,91 In fact, some viral infections and other possible factors may be linked to increases in SNCA dysfunction or the loss of dopaminergic neurons in PD patients.92,93 In conclusion, there is a possible relationship between SARS-CoV-2 and the pathogenesis of PD. A review also reported, according to some literature studies, that the upregulation of SNCA, which could occur during SARS-CoV-2 infection, would lead to widespread neurodegeneration.93 Another study investigated mRNA expression in the brain tissues, primary microglia and primary astrocytes of M-CoV (murine β-coronavirus)-infected mice; the analyses of qRT-PCR reported that DJ-1 mRNA was up-regulated in the brain tissues, primary microglia and primary astrocytes of M-CoV-infected mice compared to Mock (parallel controls were inoculated with PBS-BSA). They also demonstrated that the alteration of DJ-1, an oxidant-sensing molecule, was the first instance in M-CoV.94 Therefore, Enrichr results of the GDS1028 dataset showed higher expression levels of SNCA and PARK7 in the PBMCs of patients with SARS (COVID-19) compared to healthy individuals. It could be argued that the parallel expression of these genes in PD and COVID-19 would probably lead to an increase in PD symptoms due to infection with COVID-19.

Theo et al. also conducted a comprehensive analysis of the expression levels of 90 lncRNAs in the brain samples of 20 PD patients and 10 controls.95 The results demonstrated the significant upregulation of MALAT1 in PD compared to the controls.96 The qPCR results of another study showed higher levels of NEAT1 expression in the substantia nigra of PD brains than in the substantia nigra of control brains (control n = 24; PD n = 29).97 Also, the Enrichr results of the GSE7621 dataset analysis demonstrated that the overexpression of MALAT1 and NEAT1 was related to PD from the substantia nigra of the postmortem human brain compared to the controls. On the other hand, Huang et al. demonstrated that the proinflammatory lncRNA NEAT1 was overexpressed in the epithelial/basal cells of mild patients with severe COVID-19 compared with those of healthy controls98; NEAT1 expression was also significantly higher in severe patients than in mild patients. However, they found that MALAT1 was overexpressed in the bronchoalveolar lavage fluid of severe patients but not in that of mild patients. Furthermore, they indicated that the specific changes in the activity of NEAT1 and MALAT1 lncRNAs were involved in the COVID-19-related hyperinflammatory process. Therefore, the results of the GSE148729 dataset indicated that the expression levels of NEAT1 and MALAT1 were higher in SARS-CoV-1/2 infected human cell lines compared with that in uninfected cells. So, it could be argued that these lncRNAs probably have parallel expression in PD and COVID-19. It is possible that COVID-19 infection can regulate NEAT1 and MALAT1 lncRNAs. Consequently, these lncRNAs may act as sponges for their target miRNAs (hsa-miR-7-5p, hsa-miR-148b-3p, hsa-miR-26b-5p, hsa-miR-34c, hsa-miR-3929, hsa-miR-144, hsa-miR-221, and hsa-miR-488), thereby increasing the expression of the SNCA and PARK7 genes, which worsens PD symptoms in the presence of COVID-19 infection.

As shown in Figure 9, the interactions of NEAT1 and MALAT1 lncRNAs with their target miRNAs were identified. One of the important miRNAs identified in this study is miR-7, which can decrease SNCA stability and regulate target mRNA degradation.99 Shen et al. validated the direct interaction between miR-7 and SNCA with a dual luciferase reporter assay.100 Tatura et al. analyzed miR-7 expression in the frontal cortex by SYBR Green qRT-PCR assays and reported the significant down-regulation of miR-7 in PD compared to the control.41 Using a luciferase activity assay, Zhang et al. reported that NEAT1 can bind with miR-7 competitively, blocking its function through regulating protein tyrosine kinase 2,101 which also leads to altered SNCA expression (Fig. 9a).

Interactions between lncRNAs and target miRNAs and the regulation of miRNAs in PD and COVID-19.
Fig. 9  Interactions between lncRNAs and target miRNAs and the regulation of miRNAs in PD and COVID-19.

PD, Parkinson’s disease; MALAT1, Metastasis associated in lung adenocarcinoma transcript 1; NEAT1, Nuclear Enriched Abundant Transcript 1; PARK7, parkinsonism associated deglycase; SNCA, synuclein alpha.

Tatura et al. also reported an upregulation of miR-26b expression in the cerebellum of PD patients compared to controls. MiR-26b binds to the PARK7 gene and alters its expression.41 As shown in Fig. 9b, MALAT1 binds directly to miR-26b and acts as a sponge for miR-26a/26b, as determined by dual luciferase assays in several studies.102–107 Additionally, some studies have demonstrated the direct interaction between NEAT1 and both miR-26a and miR-26b, using a luciferase assay.108,109 Thus, NEAT1 can act as a sponge for miR-26a and miR-26b.

Hsa-miR-148b-3p can regulate the expression of the SNCA and PARK7 genes in PD.50 The hsa-miR-148b-3p level is lower in PD serum compared to control serum.50,110 Vallelunga et al. reported that miR-148b was downregulated in PD patients compared to healthy controls using TaqMan low-density array technology.45 As shown in Fig. 9c, NEAT1 lncRNA interacts with hsa-miR-148b-3p, regulating its functions. However, a few studies have focused on the interaction between lncRNAs and miR-148b.111

Kabaria et al. reported that miR-34c could repress SNCA expression, as confirmed by Western blot analysis and luciferase assay.112 Additionally, they demonstrated that hsa-miR-34c levels were reduced in the brains of PD patients. The NEAT1 and MALAT1 lncRNAs were identified as sponges for miR-34c (Fig. 9d).113,114 On the other hand, a dual luciferase assay demonstrated the direct interactions between NEAT1 and miR-34c,115 as well as between MALAT1 and miR-34c.116

The MiRTarBase database based on the Next generation sequencing (NGS) method showed that hsa-miR-3929 was involved in PD, with the miR-3929 targets being PARK7 and SNCA. However, very few experiments have focused on this miRNA and its interaction with lncRNAs (Fig. 9e).

Hsa-miR-144, hsa-miR-488 and hsa-miR-221 are associated with PD via altering the SNCA expression.41MALAT1 and NEAT1 lncRNAs bind directly to miR-144-3p and act as sponges for this miRNA as confirmed by the dual luciferase assay. Additionally, qRT-PCR results show that MALAT1 and miR-144-3p suppress each other’s expression (Fig. 9f).117–120 Dual-luciferase reporter assays and RNA immunoprecipitation assays revealed a direct interaction between NEAT1 and miR-221 (Fig. 9g).121 However, some studies have revealed the interaction between NEAT1 and miR-488 using dual luciferase assays (Fig. 9h).122,123

We identified regulatory networks involving 12 lncRNAs, 16 miRNAs, and 6 mRNAs that play critical roles in PD. By elucidating the regulatory interactions within this network, researchers can uncover novel pathways and processes involved in disease development and progression. This knowledge can help identify key molecular targets for therapeutic interventions. Additionally, by identifying specific lncRNAs and miRNAs associated with PD, researchers can potentially develop noninvasive diagnostic tests. These biomarkers could aid in early disease detection, differential diagnosis, and disease monitoring, enabling timely interventions and personalized treatment approaches.

Exploring the regulatory network between PD and COVID-19 can reveal common molecular pathways and the potential interplay between these diseases. This knowledge can provide insights into the possible impact of COVID-19 on PD pathogenesis and the exacerbation of PD symptoms. It may also shed light on the potential neurological complications of COVID-19 and guide the development of strategies for improving patient outcomes.

One of the limitations of our study was the utilization of the MiRTarBase database, which does not provide information about the source of miRNAs. Due to our limited access to studies with the same platforms, we had to use studies with different platforms to select miRNAs. Since MiRTarBase does not involve the source of tissues, we chose these miRNAs without considering specific tissues. Additionally, ethical considerations imposed several limitations on tracking RNA trends in COVID-19 patients with PD.

Conclusions

In this study, two regulatory networks were constructed. The first network consisted of six mRNAs (PINK1, LRRK2, PARK7, SNCA, PRKN and ATP13A2), 16 miRNAs (hsa-miR-26b-5p, hsa-miR-7-5p, hsa-miR-335-5p, hsa-miR-181a-5p, hsa-miR-582-5p, hsa-miR-148b-3p, hsa-miR-144, hsa-miR-221, hsa-miR-488, hsa-miR-27a-3p, miR-27b-3p, hsa-miR-34c, hsa-miR-92a-3p, hsa-miR-3929, hsa-miR-30c-5p and hsa-miR-24-3p) and 12 lncRNAs (MALAT1, NEAT1, RP11-314B1.2, XIST, IPW, LINC00938, MEG3, CASC7, RP11-361F15.2, RP11-391M1.4, LINC01355 and RP11-819C21.1), which may serve as potential biomarkers for PD diagnosis and progression. Therefore, the findings of this network should be explored in future investigations of PD.

The second network was designed using the first network and nodes involved in COVID-19. This network comprised NEAT1 and MALAT1 as important lncRNAs that interacted with eight candidate miRNAs (hsa-miR-148b-3p, hsa-miR-3929, hsa-miR-26b-5p, hsa-miR-144, hsa-miR-34c, hsa-miR-221, hsa-miR-488 and hsa-miR-7-5p) and regulated SNCA and PARK7 genes, thus affecting COVID-19. In this study, based on bioinformatics studies, we found that the expression levels of the SNCA and PARK7 genes in both PD disease and COVID-19 infection were upregulated, as were the expression levels of NEAT1 and MALAT1. Therefore, the parallel expression of these mRNAs and lncRNAs in PD and COVID-19 leads to worsened PD symptoms in the presence of COVID-19. Our study provides insights into specific genes that may serve as biomarkers for PD and highlights the potential interaction between PD and COVID-19. These findings have implications for early diagnosis, disease monitoring, and understanding the impact of COVID-19 on PD symptoms. Further research and validation are needed to confirm these findings and explore their clinical applications.

Supporting information

Supplementary material for this article is available at https://doi.org/10.14218/GE.2023.00103 .

Supplementary Excel 1

Cytoscape analysis of the mRNA-miRNA interactions.

(XLSX)

Click here for additional data file.

Supplementary Excel 2

Cytoscape analysis of the miRNA-lncRNA interactions.

(XLSX)

Click here for additional data file.

Supplementary Excel 3

The names and scores of the PD ceRNA network nodes.

(XLSX)

Click here for additional data file.

Abbreviations

AD: 

Autosomal Dominant

AR: 

Autosomal Recessive

ATP13A2: 

ATPase Cation Transporting 13A2

BST1: 

Bone Marrow Stromal Cell Antigen 1

CeRNA: 

competing endogenous RNA

CASC7: 

cancer susceptibility candidate 7

DDC: 

DOPA decarboxylase

DRD1: 

dopamine receptor D1

DRD2: 

dopamine receptor D2

GAK: 

cyclin G-associated kinase

GBA: 

glucocerebrosidase

HLA-DR: 

Human Leukocyte Antigen-DR isotype

IGF1R: 

insulin-like growth factor 1 receptor

LINC00938: 

long intergenic nonprotein coding RNA 938

lncRNAs: 

long noncoding RNAs

LRRK2: 

leucine-rich repeat kinase 2

MALAT1: 

Metastasis associated in lung adenocarcinoma transcript 1

MAOB: 

monoamine oxidase B

MEG3: 

maternally expressed 3

MAPT: 

microtubule-associated protein tau

MIAT: 

myocardial infarction-associated transcript

mRNA: 

messenger RNAs

miRNAs: 

microRNAs

NEAT1: 

Nuclear Enriched Abundant Transcript 1

PD: 

Parkinson’s disease

PRKN: 

Parkin RBR E3 ubiquitin protein ligase

PINK1: 

PTEN-induced kinase 1

PARK7: 

parkinsonism associated deglycase

RP11-314B1.2: 

clone-based (vega) gene

SNCA: 

synuclein alpha

SLC18A2: 

solute carrier family 18 member 2

TH: 

Tyrosine Hydroxylase

XIST: 

X-inactive specific transcript

Declarations

Acknowledgement

We gratefully thank the Science and Research Branch, Islamic Azad University, Tehran, for supporting this study.

Data sharing statement

The information of the genes associated to “Parkinson” is available in the DisGeNET database (https://www.disgenet.org/browser/0/1/0/C0030567/); The information of the genes associated to “AD juvenile” is available in the DisGeNET database (https://www.disgenet.org/browser/0/1/0/C0752097/); The information of the genes associated to “Parkinson disease 2, AR juvenile” is available in the DisGeNET database (https://www.disgenet.org/browser/0/1/0/C1868675/); The information of the genes associated to “young onset Parkinson’s disease” is available in the DisGeNET database (https://www.disgenet.org/browser/0/1/0/C4275179/); The information of the genes associated to “late onset Parkinson’s disease” is available in the DisGeNET database (https://www.disgenet.org/browser/0/1/0/C3160718/); The information of the miRNAs associated to Parkinson is available in the MiRTarBase database (v.8.0) (https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2019/php/search.php?opt=path&path=hsa05012); The verified target lncRNAs of the selected miRNAs were screened using DIANA-LncBase Experimental (v.2) (http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=lncbasev2%2Findex-experimental); The miRNAs’ seed position and seed type in the target lncRNAs were predicted using miRcode database (v.11) (http://www.mircode.org/); The biological processes and pathways of the genes were obtained from the Metascape database (https://metascape.org/gp/index.html#/reportfinal/t0py580jd); The “molecular functions of the selected genes” were obtained from REVIGO (http://revigo.irb.hr/Results.aspx?jobid=150251458); The GO biological process of the candidate miRNAs was visualized using REVIGO (http://revigo.irb.hr/Results.aspx?jobid=151945504); COVID-19-related gene sets are available in the Enrichr database (Diseases/Drugs). (https://maayanlab.cloud/Enrichr/enrich?dataset=e363e682964ffe4c9133013ff4ad24bf); COVID-19-related lncRNA sets are available in the Enrichr database (Legacy). (https://maayanlab.cloud/Enrichr/enrich?dataset=0d5816d0306ddaa38e8d459facb7856b); The gene expression data from the GSE7621 dataset are available in the Enrichr and NCBI databases (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7621 and https://maayanlab.cloud/Enrichr/enrich#; the gene expression data from the GDS1028 dataset are available in the Enrichr and NCBI databases (https://maayanlab.cloud/Enrichr/enrich# and https://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS1028); the gene expression data from the GSE17400 dataset are available in the Enrichr and NCBI databases (https://maayanlab.cloud/Enrichr/enrich# and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi); the gene expression data from GSE150847 are available in the Enrichr and NCBI databases (https://maayanlab.cloud/Enrichr/enrich# and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi); and the gene expression data from GSE148729 are available in the Enrichr and NCBI databases (https://maayanlab.cloud/Enrichr/enrich# and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148729).

Funding

This research did not receive any specific grant from any funding agency in the public, commercial or not-for-profit sector.

Conflict of interest

The authors declare that there are no conflict of interests that could be perceived as prejudicing the impartiality of the research reported.

Authors’ contributions

The design and conceptualization of the study were done by MY, MSH and MP. Data mining, formal analysis and investigation were performed by MY Supervision, validation and visualization were done by MP, KG, and ME. The manuscript was written by MY. Reviewing, editing and proofing were done by MP, KG, and SI. All the authors have read and approved the final manuscript.

References

  1. Ma L, Wei L, Wu F, Hu Z, Liu Z, Yuan W. Advances with microRNAs in Parkinson’s disease research. Drug Des Devel Ther 2013;7:1103-1113 View Article PubMed/NCBI
  2. Poewe W, Seppi K, Tanner CM, et al. Parkinson disease. Nat Rev Dis Primers 2017;3(1):17013 View Article PubMed/NCBI
  3. Feng Y, Jankovic J, Wu YC. Epigenetic mechanisms in Parkinson’s disease. J Neurol Sci 2015;349(1-2):3-9 View Article PubMed/NCBI
  4. Harraz MM, Dawson TM, Dawson VL. MicroRNAs in Parkinson’s disease. J Chem Neuroanat 2011;42(2):127-130 View Article PubMed/NCBI
  5. Kalinderi K, Bostantjopoulou S, Fidani L. The genetic background of Parkinson’s disease: current progress and future prospects. Acta Neurol Scand 2016;134(5):314-326 View Article PubMed/NCBI
  6. DeMaagd G, Philip A. Parkinson’s disease and its management: part 1: disease entity, risk factors, pathophysiology, clinical presentation, and diagnosis. Pharmal Ther 2015;40(8):504
  7. Johnson CC, Gorell JM, Rybicki BA, Sanders K, Peterson EL. Adult nutrient intake as a risk factor for Parkinson’s disease. Int J Epidemiol 1999;28(6):1102-1109 View Article PubMed/NCBI
  8. Etminan M, Gill SS, Samii A. Intake of vitamin E, vitamin C, and carotenoids and the risk of Parkinson’s disease: a meta-analysis. Lancet Neurol 2005;4(6):362-365 View Article PubMed/NCBI
  9. Grandinetti A, Morens DM, Reed D, MacEachern D. Prospective study of cigarette smoking and the risk of developing idiopathic Parkinson’s disease. Am J Epidemiol 1994;139(12):1129-1138 View Article PubMed/NCBI
  10. Benedetti MD, Bower JH, Maraganore DM, McDonnell SK, Peterson BJ, Ahlskog JE, et al. Smoking, alcohol, and coffee consumption preceding Parkinson’s disease: a case-control study. Neurology 2000;55(9):1350-1358 View Article PubMed/NCBI
  11. Liou HH, Tsai MC, Chen CJ, et al. Environmental risk factors and Parkinson’s disease. A case-control study in Taiwan. Neurology 1997;48(6):1583-1588 View Article PubMed/NCBI
  12. Stepens A, Logina I, Liguts V, Aldins P, Eksteina I, Platkājis A, et al. A Parkinsonian syndrome in methcathinone users and the role of manganese. N Engl J Med 2008;358(10):1009-1017 View Article PubMed/NCBI
  13. Coppedè F. Genetics and epigenetics of Parkinson’s disease. Scientific World Journal 2012;2012:489830 View Article PubMed/NCBI
  14. Su L, Wang C, Zheng C, Wei H, Song X. A meta-analysis of public microarray data identifies biological regulatory networks in Parkinson’s disease. BMC Med Genomics 2018;11(1):40 View Article PubMed/NCBI
  15. Chi LM, Wang LP, Jiao D. Identification of Differentially Expressed Genes and Long Noncoding RNAs Associated with Parkinson’s Disease. Parkinsons Dis 2019;2019:6078251 View Article PubMed/NCBI
  16. Yousefi M, Peymani M, Ghaedi K, Irani S, Etemadifar M. Significant modulations of linc001128 and linc0938 with miR-24-3p and miR-30c-5p in Parkinson disease. Sci Rep 2022;12(1):2569 View Article PubMed/NCBI
  17. Zhang H, Yao L, Zheng Z, Koc S, Lu G. The Role of Non-Coding RNAs in the Pathogenesis of Parkinson’s Disease: Recent Advancement. Pharmaceuticals (Basel) 2022;15(7):811 View Article PubMed/NCBI
  18. Adams BD, Kasinski AL, Slack FJ. Aberrant regulation and function of microRNAs in cancer. Curr Biol 2014;24(16):R762-R776 View Article PubMed/NCBI
  19. Yoon JH, Abdelmohsen K, Gorospe M. Functional interactions among microRNAs and long noncoding RNAs. Semin Cell Dev Biol 2014;34:9-14 View Article PubMed/NCBI
  20. Guttman M, Rinn JL. Modular regulatory principles of large non-coding RNAs. Nature 2012;482(7385):339-346 View Article PubMed/NCBI
  21. Fang Y, Fullwood MJ. Roles, Functions, and Mechanisms of Long Non-coding RNAs in Cancer. Genomics Proteomics Bioinformatics 2016;14(1):42-54 View Article PubMed/NCBI
  22. Ponting CP, Oliver PL, Reik W. Evolution and functions of long noncoding RNAs. Cell 2009;136(4):629-641 View Article PubMed/NCBI
  23. Limongi D, Baldelli S. Redox Imbalance and Viral Infections in Neurodegenerative Diseases. Oxid Med Cell Longev 2016;2016:6547248 View Article PubMed/NCBI
  24. Fazzini E, Fleming J, Fahn S. Cerebrospinal fluid antibodies to coronavirus in patients with Parkinson’s disease. Mov Disord 1992;7(2):153-158 View Article PubMed/NCBI
  25. Del Prete E, Francesconi A, Palermo G, Mazzucchi S, Frosini D, Morganti R, et al. Prevalence and impact of COVID-19 in Parkinson’s disease: evidence from a multi-center survey in Tuscany region. J Neurol 2021;268(4):1179-1187 View Article PubMed/NCBI
  26. Antonini A, Leta V, Teo J, Chaudhuri KR. Outcome of Parkinson’s Disease Patients Affected by COVID-19. Mov Disord 2020;35(6):905-908 View Article PubMed/NCBI
  27. Zhang Q, Schultz JL, Aldridge GM, Simmering JE, Kim Y, Ogilvie AC, et al. COVID-19 Case Fatality and Alzheimer’s Disease. J Alzheimers Dis 2021;84(4):1447-1452 View Article PubMed/NCBI
  28. Sulzer D, Antonini A, Leta V, Nordvig A, Smeyne RJ, Goldman JE, et al. COVID-19 and possible links with Parkinson’s disease and parkinsonism: from bench to bedside. NPJ Parkinsons Dis 2020;6:18 View Article PubMed/NCBI
  29. Artusi CA, Romagnolo A, Imbalzano G, Marchet A, Zibetti M, Rizzone MG, et al. COVID-19 in Parkinson’s disease: Report on prevalence and outcome. Parkinsonism Relat Disord 2020;80:7-9 View Article PubMed/NCBI
  30. Cilia R, Bonvegna S, Straccia G, Andreasi NG, Elia AE, Romito LM, et al. Effects of COVID-19 on Parkinson’s Disease Clinical Features: A Community-Based Case-Control Study. Mov Disord 2020;35(8):1287-1292 View Article PubMed/NCBI
  31. Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res 2020;48(D1):D845-D855 View Article PubMed/NCBI
  32. Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 2018;46(D1):D296-D302 View Article PubMed/NCBI
  33. Doxakis E. Post-transcriptional regulation of alpha-synuclein expression by mir-7 and mir-153. J Biol Chem 2010;285(17):12726-12734 View Article PubMed/NCBI
  34. Zhang X, Yang R, Hu BL, Lu P, Zhou LL, He ZY, et al. Reduced Circulating Levels of miR-433 and miR-133b Are Potential Biomarkers for Parkinson’s Disease. Front Cell Neurosci 2017;11:170 View Article PubMed/NCBI
  35. Alieva AKh, Filatova EV, Karabanov AV, Illarioshkin SN, Limborska SA, Shadrina MI, Slominsky PA. miRNA expression is highly sensitive to a drug therapy in Parkinson’s disease. Parkinsonism Relat Disord 2015;21(1):72-74 View Article PubMed/NCBI
  36. Cao XY, Lu JM, Zhao ZQ, Li MC, Lu T, An XS, et al. MicroRNA biomarkers of Parkinson’s disease in serum exosome-like microvesicles. Neurosci Lett 2017;644:94-99 View Article PubMed/NCBI
  37. Gui Y, Liu H, Zhang L, Lv W, Hu X. Altered microRNA profiles in cerebrospinal fluid exosome in Parkinson disease and Alzheimer disease. Oncotarget 2015;6(35):37043-37053 View Article PubMed/NCBI
  38. Yang CP, Zhang ZH, Zhang LH, Rui HC. Neuroprotective Role of MicroRNA-22 in a 6-Hydroxydopamine-Induced Cell Model of Parkinson’s Disease via Regulation of Its Target Gene TRPM7. J Mol Neurosci 2016;60(4):445-452 View Article PubMed/NCBI
  39. Kim J, Inoue K, Ishii J, Vanti WB, Voronov SV, Murchison E, et al. A MicroRNA feedback circuit in midbrain dopamine neurons. Science 2007;317(5842):1220-1224 View Article PubMed/NCBI
  40. Botta-Orfila T, Morató X, Compta Y, Lozano JJ, Falgàs N, Valldeoriola F, et al. Identification of blood serum micro-RNAs associated with idiopathic and LRRK2 Parkinson’s disease. J Neurosci Res 2014;92(8):1071-1077 View Article PubMed/NCBI
  41. Tatura R, Kraus T, Giese A, Arzberger T, Buchholz M, Höglinger G, et al. Parkinson’s disease: SNCA-, PARK2-, and LRRK2- targeting microRNAs elevated in cingulate gyrus. Parkinsonism Relat Disord 2016;33:115-121 View Article PubMed/NCBI
  42. Margis R, Margis R, Rieder CR. Identification of blood microRNAs associated to Parkinsońs disease. J Biotechnol 2011;152(3):96-101 View Article PubMed/NCBI
  43. Serafin A, Foco L, Zanigni S, Blankenburg H, Picard A, Zanon A, et al. Overexpression of blood microRNAs 103a, 30b, and 29a in L-dopa-treated patients with PD. Neurology 2015;84(7):645-653 View Article PubMed/NCBI
  44. Soreq L, Salomonis N, Bronstein M, Greenberg DS, Israel Z, Bergman H, et al. Small RNA sequencing-microarray analyses in Parkinson leukocytes reveal deep brain stimulation-induced splicing changes that classify brain region transcriptomes. Front Mol Neurosci 2013;6:10 View Article PubMed/NCBI
  45. Vallelunga A, Ragusa M, Di Mauro S, Iannitti T, Pilleri M, Biundo R, et al. Identification of circulating microRNAs for the differential diagnosis of Parkinson’s disease and Multiple System Atrophy. Front Cell Neurosci 2014;8:156 View Article PubMed/NCBI
  46. Cardo LF, Coto E, de Mena L, Ribacoba R, Moris G, Menéndez M, et al. Profile of microRNAs in the plasma of Parkinson’s disease patients and healthy controls. J Neurol 2013;260(5):1420-1422 View Article PubMed/NCBI
  47. Khoo SK, Petillo D, Kang UJ, Resau JH, Berryhill B, Linder J, et al. Plasma-based circulating MicroRNA biomarkers for Parkinson’s disease. J Parkinsons Dis 2012;2(4):321-331 View Article PubMed/NCBI
  48. Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, et al. Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 2022;605(7909):E3 View Article PubMed/NCBI
  49. Gehrke S, Imai Y, Sokol N, Lu B. Pathogenic LRRK2 negatively regulates microRNA-mediated translational repression. Nature 2010;466(7306):637-641 View Article PubMed/NCBI
  50. Marques TM, Kuiperij HB, Bruinsma IB, van Rumund A, Aerts MB, Esselink RAJ, et al. MicroRNAs in Cerebrospinal Fluid as Potential Biomarkers for Parkinson’s Disease and Multiple System Atrophy. Mol Neurobiol 2017;54(10):7736-7745 View Article PubMed/NCBI
  51. Shen YE, Cui X, Hu Y, Zhang Z, Zhang Z. LncRNA-MIAT regulates the growth of SHSY5Y cells by regulating the miR-34-5p-SYT1 axis and exerts a neuroprotective effect in a mouse model of Parkinson’s disease. Am J Trans Research 2021;13(9):9993 PubMed/NCBI
  52. Li L, Chen HZ, Chen FF, Li F, Wang M, Wang L. Global microRNA expression profiling reveals differential expression of target genes in 6-hydroxydopamine-injured MN9D cells. Neuromolecular Med 2013;15(3):593-604 View Article PubMed/NCBI
  53. Chen Y, Lian YJ, Ma YQ, Wu CJ, Zheng YK, Xie NC. LncRNA SNHG1 promotes α-synuclein aggregation and toxicity by targeting miR-15b-5p to activate SIAH1 in human neuroblastoma SH-SY5Y cells. Neurotoxicology 2018;68:212-221 View Article PubMed/NCBI
  54. Kim W, Lee Y, McKenna ND, Yi M, Simunovic F, Wang Y, et al. miR-126 contributes to Parkinson’s disease by dysregulating the insulin-like growth factor/phosphoinositide 3-kinase signaling. Neurobiol Aging 2014;35(7):1712-1721 View Article PubMed/NCBI
  55. Yılmaz ŞG, Geyik S, Neyal AM, Soko ND, Bozkurt H, Dandara C. Hypothesis: Do miRNAs Targeting the Leucine-Rich Repeat Kinase 2 Gene (LRRK2) Influence Parkinson’s Disease Susceptibility?. OMICS 2016;20(4):224-228 View Article PubMed/NCBI
  56. Yang D, Li T, Wang Y, Tang Y, Cui H, Tang Y, et al. miR-132 regulates the differentiation of dopamine neurons by directly targeting Nurr1 expression. J Cell Sci 2012;125(Pt 7):1673-1682 View Article PubMed/NCBI
  57. Lungu G, Stoica G, Ambrus A. MicroRNA profiling and the role of microRNA-132 in neurodegeneration using a rat model. Neurosci Lett 2013;553:153-158 View Article PubMed/NCBI
  58. Zhang Z, Cheng Y. miR-16-1 promotes the aberrant α-synuclein accumulation in parkinson disease via targeting heat shock protein 70. Sci World J 2014;2014:938348 View Article PubMed/NCBI
  59. Wang H, Ye Y, Zhu Z, Mo L, Lin C, Wang Q, et al. MiR-124 Regulates Apoptosis and Autophagy Process in MPTP Model of Parkinson’s Disease by Targeting to Bim. Brain Pathol 2016;26(2):167-176 View Article PubMed/NCBI
  60. Xiong R, Wang Z, Zhao Z, Li H, Chen W, Zhang B, et al. MicroRNA-494 reduces DJ-1 expression and exacerbates neurodegeneration. Neurobiol Aging 2014;35(3):705-714 View Article PubMed/NCBI
  61. Miñones-Moyano E, Porta S, Escaramís G, Rabionet R, Iraola S, Kagerbauer B, et al. MicroRNA profiling of Parkinson’s disease brains identifies early downregulation of miR-34b/c which modulate mitochondrial function. Hum Mol Genet 2011;20(15):3067-3078 View Article PubMed/NCBI
  62. Prajapati P, Sripada L, Singh K, Bhatelia K, Singh R, Singh R. TNF-α regulates miRNA targeting mitochondrial complex-I and induces cell death in dopaminergic cells. Biochim Biophys Acta 2015;1852(3):451-461 View Article PubMed/NCBI
  63. Chaudhuri AD, Choi DC, Kabaria S, Tran A, Junn E. MicroRNA-7 Regulates the Function of Mitochondrial Permeability Transition Pore by Targeting VDAC1 Expression. J Biol Chem 2016;291(12):6483-6493 View Article PubMed/NCBI
  64. Chen L, Yang J, Lü J, Cao S, Zhao Q, Yu Z. Identification of aberrant circulating miRNAs in Parkinson’s disease plasma samples. Brain Behav 2018;8(4):e00941 View Article PubMed/NCBI
  65. Sheinerman KS, Tsivinsky VG, Crawford F, Mullan MJ, Abdullah L, Umansky SR. Plasma microRNA biomarkers for detection of mild cognitive impairment. Aging (Albany NY) 2012;4(9):590-605 View Article PubMed/NCBI
  66. Huang HY, Lin YC, Li J, Huang KY, Shrestha S, Hong HC, et al. miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res 2020;48(D1):D148-D154 View Article PubMed/NCBI
  67. Paraskevopoulou MD, Georgakilas G, Kostoulas N, Reczko M, Maragkakis M, Dalamagas TM, et al. DIANA-LncBase: experimentally verified and computationally predicted microRNA targets on long non-coding RNAs. Nucleic Acids Res 2013;41:D239-245 View Article PubMed/NCBI
  68. Jeggari A, Marks DS, Larsson E. miRcode: a map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics 2012;28(15):2062-2063 View Article PubMed/NCBI
  69. Ma JX, Wang B, Li HS, Yu J, Hu HM, Ding CF, et al. Uncovering the mechanisms of leech and centipede granules in the treatment of diabetes mellitus-induced erectile dysfunction utilising network pharmacology. J Ethnopharmacol 2021;265:113358 View Article PubMed/NCBI
  70. Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 2011;6(7):e21800 View Article PubMed/NCBI
  71. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 2016;44(W1):W90-W97 View Article PubMed/NCBI
  72. Kohl M, Wiese S, Warscheid B. Cytoscape: Software for visualization and analysis of biological networks. Methods Mol Biol 2011;696:291-303 View Article PubMed/NCBI
  73. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523 View Article PubMed/NCBI
  74. Sabetian S, Castiglioni I, Jahromi BN, Mousavi P, Cava C. In Silico Identification of miRNA-lncRNA Interactions in Male Reproductive Disorder Associated with COVID-19 Infection. Cells 2021;10(6):1480 View Article PubMed/NCBI
  75. Liu L, Zhang Y, Chen Y, Zhao Y, Shen J, Wu X, et al. Therapeutic prospects of ceRNAs in COVID-19. Front Cell Infect Microbiol 2022;12:998748 View Article PubMed/NCBI
  76. Taheri M, Rad LM, Hussen BM, Nicknafs F, Sayad A, Ghafouri-Fard S. Evaluation of expression of VDR-associated lncRNAs in COVID-19 patients. BMC Infect Dis 2021;21(1):588 View Article PubMed/NCBI
  77. Huang K, Wang C, Vagts C, Raguveer V, Finn PW, Perkins DL. Long non-coding RNAs (lncRNAs) NEAT1 and MALAT1 are differentially expressed in severe COVID-19 patients: An integrated single-cell analysis. PLoS One 2022;17(1):e0261242 View Article PubMed/NCBI
  78. Rahni Z, Hosseini SM, Shahrokh S, Niasar MS, Shoraka S, Mirjalali H, et al. Long non-coding RNAs ANRIL, THRIL, and NEAT1 as potential circulating biomarkers of SARS-CoV-2 infection and disease severity. Virus Research 2023;336:199214 View Article
  79. Rodrigues AC, Adamoski D, Genelhould G, Zhen F, Yamaguto GE, Araujo-Souza PS, et al. NEAT1 and MALAT1 are highly expressed in saliva and nasopharyngeal swab samples of COVID-19 patients. Mol Oral Microbiol 2021;36(6):291-294 View Article PubMed/NCBI
  80. Eriksen JL, Wszolek Z, Petrucelli L. Molecular pathogenesis of Parkinson disease. Arch Neurol 2005;62(3):353-357 View Article PubMed/NCBI
  81. Soukup SF, Vanhauwaert R, Verstreken P. Parkinson’s disease: convergence on synaptic homeostasis. EMBO J 2018;37(18):e98960 View Article PubMed/NCBI
  82. Manna I, Quattrone A, De Benedittis S, Iaccino E, Quattrone A. Roles of Non-Coding RNAs as Novel Diagnostic Biomarkers in Parkinson’s Disease. J Parkinsons Dis 2021;11(4):1475-1489 View Article PubMed/NCBI
  83. Mehanna R, Jankovic J. Young-onset Parkinson’s disease: Its unique features and their impact on quality of life. Parkinsonism Relat Disord 2019;65:39-48 View Article PubMed/NCBI
  84. Ferguson LW, Rajput AH, Rajput A. Early-onset vs. Late-onset Parkinson’s disease: A Clinical-pathological Study. Can J Neurol Sci 2016;43(1):113-119 View Article PubMed/NCBI
  85. Chiba-Falek O, Lopez GJ, Nussbaum RL. Levels of alpha-synuclein mRNA in sporadic Parkinson disease patients. Mov Disord 2006;21(10):1703-1708 View Article PubMed/NCBI
  86. Miller DW, Hague SM, Clarimon J, Baptista M, Gwinn-Hardy K, Cookson MR, et al. Alpha-synuclein in blood and brain from familial Parkinson disease with SNCA locus triplication. Neurology 2004;62(10):1835-1838 View Article PubMed/NCBI
  87. Bonam SR, Muller S. Parkinson’s disease is an autoimmune disease: A reappraisal. Autoimmun Rev 2020;19(12):102684 View Article PubMed/NCBI
  88. Yalçınkaya N, Haytural H, Bilgiç B, Özdemir Ö, Hanağası H, Küçükali Cİ, et al. Expression changes of genes associated with apoptosis and survival processes in Parkinson’s disease. Neurosci Lett 2016;615:72-77 View Article PubMed/NCBI
  89. Hu C, Chen C, Dong XP. Impact of COVID-19 pandemic on patients with neurodegenerative diseases. Front Aging Neurosci 2021;13:664965 View Article
  90. Al-Kuraishy HM, Al-Gareeb AI, Kaushik A, Kujawska M, Ahmed EA, Batiha GE. SARS-COV-2 infection and Parkinson’s disease: Possible links and perspectives. J Neurosci Res 2023;101(6):952-975 View Article PubMed/NCBI
  91. Fu YW, Xu HS, Liu SJ. COVID-19 and neurodegenerative diseases. Eur Rev Med Pharmacol Sci 2022;26(12):4535-4544 View Article PubMed/NCBI
  92. Follmer C. Viral Infection-Induced Gut Dysbiosis, Neuroinflammation, and α-Synuclein Aggregation: Updates and Perspectives on COVID-19 and Neurodegenerative Disorders. ACS Chem Neurosci 2020;11(24):4012-4016 View Article PubMed/NCBI
  93. Sinha S, Mittal S, Roy R. Parkinson’s Disease and the COVID-19 Pandemic: A Review Article on the Association between SARS-CoV-2 and α-Synucleinopathy. J Mov Disord 2021;14(3):184-192 View Article PubMed/NCBI
  94. Kundu S, Saadi F, Sengupta S, Antony GR, Raveendran VA, Kumar R, et al. DJ-1-Nrf2 axis is activated upon murine β-coronavirus infection in the CNS. Brain Disord 2021;4:100021 View Article PubMed/NCBI
  95. Theo F, Kraus J, Haider M. Altered Long Noncoding RNA Expression Precedes the Course of Parkinson's Disease–a Preliminary Report. Molecular neurobiology 2017;54(4):2869-2877 View Article PubMed/NCBI
  96. Kraus TFJ, Haider M, Spanner J, Steinmaurer M, Dietinger V, Kretzschmar HA. Altered Long Noncoding RNA Expression Precedes the Course of Parkinson’s Disease-a Preliminary Report. Mol Neurobiol 2017;54(4):2869-2877 View Article PubMed/NCBI
  97. Simchovitz A, Hanan M, Niederhoffer N, Madrer N, Yayon N, Bennett ER, et al. NEAT1 is overexpressed in Parkinson’s disease substantia nigra and confers drug-inducible neuroprotection from oxidative stress. FASEB J 2019;33(10):11223-11234 View Article PubMed/NCBI
  98. Huang K, Wang C, Vagts C, Raguveer V, Finn PW, Perkins DL. Long non-coding RNAs (lncRNAs) NEAT1 and MALAT1 are differentially expressed in severe COVID-19 patients: An integrated single-cell analysis. PLoS One 2022;17(1):e0261242
  99. Junn E, Lee KW, Jeong BS, Chan TW, Im JY, Mouradian MM. Repression of alpha-synuclein expression and toxicity by microRNA-7. Proc Natl Acad Sci USA 2009;106(31):13052-13057 View Article PubMed/NCBI
  100. Shen DF, Qi HP, Ma C, Chang MX, Zhang WN, Song RR. Astaxanthin suppresses endoplasmic reticulum stress and protects against neuron damage in Parkinson’s disease by regulating miR-7/SNCA axis. Neurosci Res 2021;165:51-60 View Article PubMed/NCBI
  101. Zhang Y, Chen XF, Li J, He F, Li X, Guo Y. lncRNA Neat1 Stimulates Osteoclastogenesis Via Sponging miR-7. J Bone Miner Res 2020;35(9):1772-1781 View Article PubMed/NCBI
  102. Li Q, Liu X, Liu W, Zhang Y, Wu M, Chen Z, et al. MALAT1 sponges miR-26a and miR-26b to regulate endothelial cell angiogenesis via PFKFB3-driven glycolysis in early-onset preeclampsia. Mol Ther Nucleic Acids 2021;23:897-907 View Article PubMed/NCBI
  103. Xu J, Xiao Y, Liu B, Pan S, Liu Q, Shan Y, et al. Exosomal MALAT1 sponges miR-26a/26b to promote the invasion and metastasis of colorectal cancer via FUT4 enhanced fucosylation and PI3K/Akt pathway. J Exp Clin Cancer Res 2020;39(1):54 View Article PubMed/NCBI
  104. Li Z, Li J, Tang N. Long noncoding RNA Malat1 is a potent autophagy inducer protecting brain microvascular endothelial cells against oxygen-glucose deprivation/reoxygenation-induced injury by sponging miR-26b and upregulating ULK2 expression. Neuroscience 2017;354:1-10 View Article PubMed/NCBI
  105. Xu J, Xiao Y, Liu B, Pan S, Liu Q, Shan Y, et al. Exosomal MALAT1 sponges miR-26a/26b to promote the invasion and metastasis of colorectal cancer via FUT4 enhanced fucosylation and PI3K/Akt pathway. J Exp Clin Cancer Res 2020;39(1):54 View Article PubMed/NCBI
  106. Zhou J, Wang M, Mao A, Zhao Y, Wang L, Xu Y, et al. Long noncoding RNA MALAT1 sponging miR-26a-5p to modulate Smad1 contributes to colorectal cancer progression by regulating autophagy. Carcinogenesis 2021;42(11):1370-1379 View Article PubMed/NCBI
  107. Wang N, Cao S, Wang X, Zhang L, Yuan H, Ma X. lncRNA MALAT1/miR-26a/26b/ST8SIA4 axis mediates cell invasion and migration in breast cancer cell lines. Oncol Rep 2021;46(2):181 View Article PubMed/NCBI
  108. Fan JT, Zhou ZY, Luo YL, Luo Q, Chen SB, Zhao JC, et al. Exosomal lncRNA NEAT1 from cancer-associated fibroblasts facilitates endometrial cancer progression via miR-26a/b-5p-mediated STAT3/YKL-40 signaling pathway. Neoplasia 2021;23(7):692-703 View Article PubMed/NCBI
  109. Yu XH, Liu SY, Li CF. TGF-β2-induced NEAT1 regulates lens epithelial cell proliferation, migration and EMT by the miR-26a-5p/FANCE axis. Int J Ophthalmol 2021;14(11):1674-1682 View Article PubMed/NCBI
  110. Chatterjee P, Roy D. Comparative analysis of RNA-Seq data from brain and blood samples of Parkinson’s disease. Biochem Biophys Res Commun 2017;484(3):557-564 View Article PubMed/NCBI
  111. Wu C, Liu J, Chen Z, Wu Y, Gao F. Comprehensive analysis of ferroptosis-related hub gene signatures as a potential pathogenesis and therapeutic target for systemic sclerosis: A bioinformatics analysis. Int J Immunopathol Pharmacol 2023;37:3946320231187783 View Article PubMed/NCBI
  112. Kabaria S, Choi DC, Chaudhuri AD, Mouradian MM, Junn E. Inhibition of miR-34b and miR-34c enhances α-synuclein expression in Parkinson’s disease. FEBS Lett 2015;589(3):319-325 View Article PubMed/NCBI
  113. Hu Y, Yang Q, Wang L, Wang S, Sun F, Xu D, et al. Knockdown of the oncogene lncRNA NEAT1 restores the availability of miR-34c and improves the sensitivity to cisplatin in osteosarcoma. Biosci Rep 2018;38(3):BSR20180375 View Article PubMed/NCBI
  114. Sun Z, Zhang T, Chen B. Long Non-Coding RNA Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1) Promotes Proliferation and Metastasis of Osteosarcoma Cells by Targeting c-Met and SOX4 via miR-34a/c-5p and miR-449a/b. Med Sci Monit 2019;25:1410-1422 View Article PubMed/NCBI
  115. Zhan JF, Huang HW, Huang C, Hu LL, Xu WW. Long Non-Coding RNA NEAT1 Regulates Pyroptosis in Diabetic Nephropathy via Mediating the miR-34c/NLRP3 Axis. Kidney Blood Press Res 2020;45(4):589-602 View Article PubMed/NCBI
  116. Yang X, Yang J, Lei P, Wen T. LncRNA MALAT1 shuttled by bone marrow-derived mesenchymal stem cells-secreted exosomes alleviates osteoporosis through mediating microRNA-34c/SATB2 axis. Aging (Albany NY) 2019;11(20):8777-8791 View Article PubMed/NCBI
  117. Liu R, Jiang C, Li J, Li X, Zhao L, Yun H, et al. Serum-derived exosomes containing NEAT1 promote the occurrence of rheumatoid arthritis through regulation of miR-144-3p/ROCK2 axis. Ther Adv Chronic Dis 2021;12:2040622321991705 View Article PubMed/NCBI
  118. Wei JL, Wu CJ, Chen JJ, Shang FT, Guo SG, Zhang XC, et al. LncRNA NEAT1 promotes the progression of sepsis-induced myocardial cell injury by sponging miR-144-3p. Eur Rev Med Pharmacol Sci 2020;24(2):851-861 View Article PubMed/NCBI
  119. Wang Y, Zhang Y, Yang T, Zhao W, Wang N, Li P, et al. Long non-coding RNA MALAT1 for promoting metastasis and proliferation by acting as a ceRNA of miR-144-3p in osteosarcoma cells. Oncotarget 2017;8(35):59417-59434 View Article PubMed/NCBI
  120. Gong X, Zhu Y, Chang H, Li Y, Ma F. Long noncoding RNA MALAT1 promotes cardiomyocyte apoptosis after myocardial infarction via targeting miR-144-3p. Biosci Rep 2019;39(8):BSR20191103 View Article PubMed/NCBI
  121. Geng L, Zhao J, Liu W, Chen Y. Retracted Article: Knockdown of NEAT1 ameliorated MPP+-induced neuronal damage by sponging miR-221 in SH-SY5Y cells. RSC Adv 2019;9(43):25257-25265 View Article PubMed/NCBI
  122. Zheng H, Zhang G, Liu G, Wang L. Up-regulation of lncRNA NEAT1 in cerebral ischemic stroke promotes activation of astrocytes by modulation of miR-488-3p/RAC1. Exp Brain Res 2023;241(2):395-406 View Article PubMed/NCBI
  123. Xu H, Guo X, Tian Y, Wang J. Knockdown of lncRNA-NEAT1 expression inhibits hypoxia-induced scar fibroblast proliferation through regulation of the miR-488-3p/COL3A1 axis. Exp Ther Med 2022;24(1):442 View Article PubMed/NCBI
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Elucidating the Role of a Shared lncRNA-miRNA-mRNA Network in Exacerbating Parkinson’s Disease Symptoms in the Context of COVID-19 Infection

Maryam Yousefi, Motahare-Sadat Hashemi, Maryam Peymani, Kamran Ghaedi, Shiva Irani, Masoud Etemadifar
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