Next Article in Journal
Friend or Foe: Paradoxical Roles of Autophagy in Gliomagenesis
Next Article in Special Issue
ABHD4-Regulating RNA Panel: Novel Biomarkers in Acute Coronary Syndrome Diagnosis
Previous Article in Journal
Towards Goals to Refine Prophylactic and Therapeutic Strategies Against COVID-19 Linked to Aging and Metabolic Syndrome
Previous Article in Special Issue
Horizontal Transfer of miR-643 from Cisplatin-Resistant Cells Confers Chemoresistance to Recipient Drug-Sensitive Cells by Targeting APOL6
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cells
Volume 10
Issue 6
10.3390/cells10061410
cells-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Regulatory miRNA–mRNA Networks in Parkinson’s Disease

by
Bruno Lopes Santos-Lobato
1,2,*,
Amanda Ferreira Vidal
2,3,4 and
Ândrea Ribeiro-dos-Santos
2,4
1
Laboratório de Neuropatologia Experimental, Universidade Federal do Pará, Belém 66073-000, PA, Brazil
2
Núcleo de Pesquisa em Oncologia, Programa de Pós-Graduação em Oncologia e Ciências Médicas, Universidade Federal do Pará, Belém 66073-000, PA, Brazil
3
Instituto Tecnológico Vale, Belém 66055-090, PA, Brazil
4
Laboratório de Genética Humana e Médica, Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Pará, Belém 66075-110, PA, Brazil
*
Author to whom correspondence should be addressed.
Cells 2021, 10(6), 1410; https://doi.org/10.3390/cells10061410
Submission received: 18 March 2021 / Revised: 4 May 2021 / Accepted: 11 May 2021 / Published: 6 June 2021
(This article belongs to the Collection Regulatory Functions of microRNAs)
Browse Figures
Versions Notes

Abstract

:
Parkinson’s disease (PD) is the second-most common neurodegenerative disease, and its pathophysiology is associated with alpha-synuclein accumulation, oxidative stress, mitochondrial dysfunction, and neuroinflammation. MicroRNAs are small non-coding RNAs that regulate gene expression, and many previous studies have described their dysregulation in plasma, CSF, and in the brain of patients with PD. In this study, we aimed to provide a regulatory network analysis on differentially expressed miRNAs in the brain of patients with PD. Based on our systematic review with a focus on the substantia nigra and the putamen, we found 99 differentially expressed miRNAs in brain samples from patients with PD, which regulate 135 target genes. Five genes associated with neuronal survival (BCL2, CCND1, FOXO3, MYC, and SIRT1) were modulated by dysregulated miRNAs found in the substantia nigra and the putamen of patients with PD. The functional enrichment analysis found FoxO and PI3K-AKT signaling as pathways related to PD. In conclusion, our comprehensive analysis of brain-related miRNA–mRNA regulatory networks in PD showed that mechanisms involving neuronal survival signaling, such as cell cycle control and regulation of autophagy/apoptosis, may be crucial for the neurodegeneration of PD, being a promising way for novel disease-modifying therapies.

Graphical Abstract

1. Introduction

Parkinson’s disease (PD) is the second-most common neurodegenerative disease, affecting approximately 6 million individuals worldwide, with a growing incidence in the last few decades [1]. Furthermore, the disease reduces life expectancy and increases disability-adjusted life years, and apparently, these negative impacts have not been mitigated by the advance of new therapies [2]. Some authors compare the recent expansion of PD to a pandemic and suggest a substantial increase in the funding of new research on its pathophysiology [1].
Multiple mechanisms are associated with the pathophysiology of PD, such as the accumulation of α-synuclein, mitochondrial dysfunction, oxidative stress, calcium homeostasis, and neuroinflammation [3]. These epigenetic mechanisms are influenced by microRNAs (miRNAs), small non-coding RNAs that regulate gene expression at a posttranscriptional level by binding to their target messenger RNAs (mRNAs) [4]. Several studies analyzed the differentially expressed miRNAs in biological samples from patients with PD; however, the low sample size and high methodological heterogeneity compromise the interpretation of these combined results [5]. A recent meta-analysis on miRNAs in PD identified 13 miRNAs that are consistently differentially expressed in the blood and brain of patients with PD, such as hsa-miR-133b, hsa-miR-221-3p, and hsa-miR-214-3p [5].
For instance, it was demonstrated that hsa-miR-34b and hsa-miR-34c are downregulated in PD, reducing the levels of DJ-1 and Parkin in the brain, two proteins involved in the ubiquitin–proteasome system in neurons, causing cell death. Furthermore, hsa-miR-4639-5p is upregulated in PD and inhibits DJ-1, also promoting cell death [6]. The dysregulation of these miRNAs shows how these molecules can modulate the pathophysiology of PD.
For a better understanding of the role of these miRNAs in PD pathophysiology, regulatory miRNA–mRNA networks, followed by their topological analysis and functional enrichment of the hub genes, are important to provide a broad view of the PD-related biological processes and signaling pathways [7,8]. The objective of this study was to explore PD pathophysiology through regulatory network analyses based on differentially expressed miRNAs (DE-miRNAs) in the brain of patients with PD described in previous studies, with a special focus on substantia nigra and putamen. These data can be useful for proposing miRNA-based therapies capable of slowing disease progression [9].

2. Materials and Methods

2.1. Screening of Candidates Differentially Expressed Brain-Related miRNAs Based on a Systematic Review

To screen differentially expressed miRNAs (DE-miRNAs) in the brain of patients with PD, we conducted a systematic literature search on MEDLINE, EMBASE, and Web of Science (from inception to December 2020) using the following algorithms: MEDLINE—“Parkinson’s disease” AND “microRNA” AND “brain”; EMBASE—(“Parkinson disease”/exp OR “Parkinson disease”) AND (“microrna”/exp OR “microRNA”) AND (“brain”/exp OR “brain”); Web of Science—ALL = (“Parkinson’s disease” AND “microRNA” AND “brain”). Reference lists of the studies included were checked to identify new studies missed in the primary search (cross-reference search).

2.2. Study Selection and Data Extraction

We aimed to select all original research studies describing DE-miRNAs in the brain of patients with PD. Two rounds of selection were performed. In the first round, titles and abstracts were screened and filtered following these exclusion criteria: (1) studies not conducted in patients with PD, (2) studies not conducted in human subjects, and (3) duplicate articles. In the second round, full texts were evaluated and excluded following other exclusion criteria: (1) review studies, (2) studies assessing different conditions from PD (such as atypical parkinsonism and dementia with Lewy bodies), (3) conference abstracts, and (4) full text not found. A single reviewer performed each selection round.
We extracted the following data: (1) the first author’s name, (2) year of publication, (3) brain region, (4) sample size, sex, and age of the study population (patients and controls), (5) dysregulated DE-miRNAs associated with PD, and (6) DE-miRNAs up- or downregulation in PD.

2.3. Prediction of the Target Genes of the Differentially Expressed Brain-Related miRNAs

After that, we predicted the target genes of the DE-miRNAs using miRTargetLink (https://ccb-web.cs.uni-saarland.de/mirtargetlink/ accessed on 7 January 2021), a tool for automating miRNA-targeting gene analysis procedures [10], considering only the strong evidence type of experimental validation. To filter the target genes, we downloaded RNA-Seq data from GTEx (https://gtexportal.org/home/ accessed on 7 January 2021) and selected only those that presented median gene-level TPM > 1 in all brain tissues (amygdala, anterior cingulate cortex, caudate—basal ganglia, cerebellar hemisphere, cerebellum, cortex, frontal cortex, hippocampus, hypothalamus, nucleus accumbens—basal ganglia, putamen, spinal cord, and substantia nigra). The filtered target genes were used in the following analyses.

2.4. Regulatory Networks and Their Topology Analysis

Regulatory networks of miRNA–mRNA interactions were constructed and visualized using Cytoscape software version 3.8.0 (http://www.cytoscape.org/ (accessed on 7 January 2021) [11]. We analyzed the networks’ centrality (degree, betweenness, and closeness) and identified the hub genes using the CytoNCA plugin [12] in Cytoscape [13]. Hub gene expressions in GTEx brain tissues were plotted in heatmaps using the pheatmap package in R (Version 1.2.5033).

2.5. Functional Enrichment Analysis

Functional enrichment analysis of the target genes was performed using clusterProfiler and org.Hs.eg.db packages in R (Version 1.2.5033) [14]. The enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were plotted using the clusterProfiler package in R (Version 1.2.5033).

3. Results

3.1. Differentially Expressed Brain-Related miRNAs Based on the Systematic Review

After pooling the publications from the databases, a total of 880 publications were found. After both rounds of selection, a total of 19 articles were finally included and reviewed (Table 1) [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]. Among these studies, the most collected brain regions were the substantia nigra (n = 9), neocortical areas (n = 9, including prefrontal, frontal, anterior cingulate and temporal cortex), putamen (n = 2), amygdala (n = 2) and cerebellum (n = 2). All samples were extracted from postmortem brains, and the median postmortem interval (PMI) was higher than 12 h (PD: median PMI 18.35 h, interquartile range [IQR] 10–49; controls: median PMI 23.95 h, IQR 15–47). The sample size was less than 10 in most studies (PD: median number 8, IQR 6–15; controls: median number 8, IQR 5.5–11.5), and most participants died over 65 years (PD: median age at death 76 years, IQR 71.5–77; controls: median age at death 69 years, IQR 68.5–74). The median disease duration of patients with PD was 8 years (IQR 5–12).
The number of brain-related DE-miRNAs associated with PD varied from 1 to 29 per study (median 1, IQR 1–8). A total of 99 brain-related DE-miRNAs associated with PD were reported by the selected studies: 60 upregulated miRNAs and 39 downregulated miRNAs—only hsa-miR-144 was reported as upregulated and downregulated in different studies (Table 2).
Particularly, we analyzed samples from substantia nigra and putamen, which comprise the nigrostriatal pathway, a brain circuit with relevant importance to PD. From the substantia nigra samples, 38 DE-miRNAs were reported, while 14 DE-miRNAs were reported related to putamen (Table 2). Two DE-miRNAs, hsa-miR-34b and hsa-miR-95, were dysregulated in both substantia nigra and putamen.

3.2. Analysis of the Differentially Expressed Brain-Related miRNAs’ Target Genes

Target genes prediction was performed using an experimentally validated microRNA–target interactions database. The predicted targets were filtered according to the GTEx data by considering only those that presented median gene-level TPM > 1 in the brain. This approach resulted in 58 target genes for the upregulated brain-related miRNAs and 79 genes for the downregulated miRNAs (Table 3). Especially for the DE-miRNAs found in the substantia nigra and putamen, we found 22 and 18 target genes, respectively (Table 3). When comparing these results, we found some common target genes between the four sets (Figure 1). For instance, we identified that three genes (CCND1, FOXO3, and SIRT1) are in common between all sets, while five genes are shared between substantia nigra and putamen (BCL2, CCND1, FOXO3, MYC, and SIRT1) (Table S1).
After that, we performed a functional enrichment analysis for each gene set (Tables S2–S5) and plotted the 30 most significant KEGG pathways (Figure 2). Among the enriched KEGG pathways, we highlight FoxO and PI3K-AKT signaling pathways, which are processes closely related to PD.

3.3. Regulatory Networks and Their Topology Analysis

We constructed four miRNA–mRNA regulatory networks: (1) upregulated DE-miRNAs and their targets, resulting in 54 nodes and 439 interactions (Figure 3A); (2) downregulated DE-miRNAs and its targets, resulting in 73 nodes and 574 interactions (Figure 3B); (3) substantia nigra DE-miRNAs and their targets (Figure 3C), which involved 21 nodes and 126 interactions; and (4) putamen DE-miRNAs and their targets, represented by 17 nodes and 54 interactions (Figure 3D).
As shown in Figure 3, some genes potentially have a central role in the regulatory networks, such as CCND1 and MYC. To better identify these hub genes, we analyzed the degree, betweenness, and closeness centrality of the nodes (Table 4). After identifying the hub genes of each network, we analyzed their expression across the brain regions using GTEx RNA-Seq data (Figure 4). Overall heatmaps evidence the high expression of PTEN and CCND1 in substantia nigra and putamen, respectively, suggesting the potential role of these genes in the brain.

4. Discussion

Based on a systematic review, we found a total of 99 DE-miRNAs (including 60 upregulated miRNAs and 39 downregulated miRNAs) in brain samples from patients with PD compared to healthy controls. Among them, hsa-miR-144 is the only one found as both up- and downregulated in PD—there is some evidence showing that this miRNA modifies the expression of three genes associated with monogenic forms of PD (SNCA, PRKN, LRRK2) [27]. Cho et al. showed that an inverse correlation between hsa-miR-205 and LRRK2 in PD was previously described, with high LRRK2 protein expression and low hsa-miR-205 levels in the frontal cortex of patients with PD, probably due to the 3′-UTR region of LRRK2 being an hsa-miR-205 target site [18]. A former study showed that hsa-miR-7, which was downregulated in the substantia nigra according to our review, is a direct regulator of SNCA, reducing its expression in a cell model and in an MPTP PD murine model [34]. Considering the miRNAs associated with both substantia nigra and putamen, we found that hsa-miR-34b and hsa-miR-95-hsa-miR-34b are associated with a reduction in the expression of alpha-synuclein [35], DJ-1, and Parkin [6], while hsa-miR-95 regulates the lysosomal function through the enzyme sulfatase-modifying factor 1 [36], and it was downregulated in pregnant women with multiple sclerosis [37]. To compare with another prevalent neurodegenerative disease, miRNAs such as hsa-miR-132 and hsa-miR-339-5p could be found in the brain of both PD and Alzheimer’s disease patients [38].
Together, the DE-miRNAs regulate 135 genes. From these, five genes are regulated simultaneously by the dysregulated sets of miRNAs found in the substantia nigra and the putamen of patients with PD (BCL2, CCND1, FOXO3, MYC, and SIRT1) (Table 4 and Figure 3). These genes have central roles in the miRNA–mRNA regulatory networks, and some of them have high expression in the brain, particularly in the substantia nigra and the putamen (Figure 3 and Figure 4).
Cyclin D1 (CCND1) is a regulator of the cell cycle progression mediated by extracellular stimulation, and its overexpression results in neoplastic growth [39], or apoptotic-related cell death in postmitotic neurons [40]. The re-expression of cyclins and cyclin-dependent kinases in neurons from patients with Alzheimer’s disease suggests that the failure of cell cycle arrest in adults may be associated with late-onset neurodegenerative diseases [40]. In PD, there is an overexpression of mitotic-associated proteins, such as cyclins and cyclin-dependent kinases, in the substantia nigra of postmortem patients with PD and an MPTP mouse model of PD, resulting in apoptosis of dopaminergic neurons [41,42]. Recently, some cell cycle genes were found enriched in a cell model of PD, and CCND1 was reported as upregulated and involved in alpha-synuclein cell death. It was shown that the knockdown of CCND1 reduces cell death [43], reinforcing our results of upregulation of miRNAs that regulate CCND1 in the brain.
Forkhead box protein O3 (FOXO3), comprising the Forkhead family, is a transcription factor associated with longevity in humans, and it is expressed in dopaminergic neurons of the substantia nigra. In a rat model of PD, FOXO3 was essential in the neuronal survival of the substantia nigra, and it may also reduce alpha-synuclein accumulation and its toxicity [44]. Also extending longevity, the silence information regulator 1 (SIRT1) is a member of the sirtuin family, which regulates DNA stability and controls gene expression and cell cycle progression. Enzymatic activity of SIRT1 is reduced in the temporal and frontal cortex of patients with PD [45], playing a critical role in the pathophysiology of PD through induction of autophagy, regulation of mitochondrial function, inhibition of neuroinflammation, and increasing degradation of alpha-synuclein [46].
MYC (or c-myc) is a transcription factor that regulates cell growth, division, differentiation, and death, and despite having a classic role in brain cancer progression and brain development, MYC expression is increased in neurodegenerative diseases, such as Alzheimer’s disease, and like CCND1, its role is based on cell cycle control [47]. BCL2 is a suppressor of autophagy and apoptotic cell death, and its expression is decreased in cell models of PD [48].
Despite being mostly related to cancer, two of the pathways associated with PD are closely associated with neuronal survival and neurodegenerative diseases: FoxO and PI3K-AKT signaling pathways. The Forkhead box class O (FoxO) family of transcription factors has an essential role in multiple cellular processes in the nervous system, such as neural development and neuronal survival, promoting a proapoptotic effect [49]; otherwise, the PI3K-AKT pathway is associated with neuroprotection and is a major regulator of the FoxO pathway, inhibiting FoxO-induced neuronal death [49].
Previous studies have explored pathways involved in PD pathogenesis. Song et al. [50] found 21 different pathways associated with PD, based on GWAS datasets. In another study, data from Gene Expression Omnibus from patients with PD were used to perform regulatory network and functional and enrichment analysis, showing that distinct pathways, such as amoebiasis and MAPK signaling, might be related to PD [51]. More recently, another study also used a dataset from Gene Expression Omnibus and revealed new 12 pathways associated with PD [52].
These results suggest that, in PD, the expression of genes involved in cell survival is dysregulated by miRNAs. Therefore, besides alpha-synuclein accumulation, oxidative stress, and neuroinflammation, the neurodegeneration of PD may include competing mechanisms over neuronal survival, such as cell cycle control and regulation of autophagy/apoptosis, particularly in the substantia nigra. Neuronal survival signaling may become the target of new disease-modifying treatments for PD, including the use of miRNA-based therapies [9].

5. Conclusions

In conclusion, our analysis of miRNAs associated with PD based on a systematic review showed a multitude of differentially expressed genes in the brain of these patients, especially in the substantia nigra. This expression dysregulation is linked to several pathways, including neuronal survival signaling. The role of these genes and pathways must be explored in further studies and can be used by future studies on miRNA-based therapies.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/cells10061410/s1: Table S1: Target genes in common between the four sets of miRNAs (upregulated miRNAs, downregulated miRNAs, substantia nigra, and putamen); Table S2: KEGG pathways enrichment for the upregulated DE-miRNAs’ target genes; Table S3: KEGG pathways enrichment for the downregulated DE-miRNAs’ target genes; Table S4: KEGG pathways enrichment for the substantia nigra DE-miRNAs’ target genes; Table S5: KEGG pathways enrichment for the putamen DE-miRNAs’ target genes.

Author Contributions

Conceptualization, B.L.S.-L., A.F.V. and Â.R.-d.-S.; methodology, B.L.S.-L. and A.F.V.; software, B.L.S.-L. and A.F.V.; validation, B.L.S.-L., A.F.V. and Â.R.-d.-S.; formal analysis, B.L.S.-L. and A.F.V.; investigation, B.L.S.-L. and A.F.V.; resources, Â.R.-d.-S.; data curation, B.L.S.-L. and A.F.V.; writing—original draft preparation, B.L.S.-L. and A.F.V.; writing—review and editing, B.L.S.-L., A.F.V. and Â.R.-d.-S.; visualization, B.L.S.-L., A.F.V. and Â.R.-d.-S.; supervision, Â.R.-d.-S.; project administration, B.L.S.-L. and Â.R.-d.-S.; funding acquisition, Â.R.-d.-S. All authors have read and agreed to the published version of the manuscript.

Funding

We thank Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional do Desenvolvimento Científico e Tecnológico (CNPq), and Pró-Reitoria de Pesquisa (PROPESP) of Universidade Federal do Pará (UFPA) for the received grants. This work is part of Rede de Pesquisa em Genômica Populacional Humana (Biocomputacional—Protocol no. 3381/2013/CAPES). Â.R.-d.-S. is supported by CNPq/Produtividade (CNPq 304413/2015-1). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are included in the paper.

Acknowledgments

We thank Elaine Del Bel (Faculdade de Odontologia de Ribeirão Preto, Brazil) for reviewing the final version of the manuscript.

Conflicts of Interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in the subject matter or materials discussed in the manuscript apart from those disclosed.

References

  1. Dorsey, E.R.; Bloem, B.R. The Parkinson Pandemic-A Call to Action. JAMA Neurol. 2018, 75, 9–10. [Google Scholar] [CrossRef] [PubMed]
  2. GBD 2016 Parkinson’s Disease Collaborators Global, regional, and national burden of Parkinson’s disease, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2018, 17, 939–953.
  3. Poewe, W.; Seppi, K.; Tanner, C.M.; Halliday, G.M.; Brundin, P.; Volkmann, J.; Schrag, A.-E.; Lang, A.E. Parkinson disease. Nat. Rev. Dis. Primers 2017, 3, 17013. [Google Scholar] [CrossRef] [PubMed]
  4. Leggio, L.; Vivarelli, S.; L’Episcopo, F.; Tirolo, C.; Caniglia, S.; Testa, N.; Marchetti, B.; Iraci, N. microRNAs in Parkinson’s Disease: From Pathogenesis to Novel Diagnostic and Therapeutic Approaches. Int. J. Mol. Sci. 2017, 18, 2698. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Schulz, J.; Takousis, P.; Wohlers, I.; Itua, I.O.G.; Dobricic, V.; Rücker, G.; Binder, H.; Middleton, L.; Ioannidis, J.P.A.; Perneczky, R.; et al. Meta-analyses identify differentially expressed micrornas in Parkinson’s disease. Ann. Neurol. 2019, 85, 835–851. [Google Scholar] [CrossRef] [PubMed]
  6. Rezaei, O.; Nateghinia, S.; Estiar, M.A.; Taheri, M.; Ghafouri-Fard, S. Assessment of the role of non-coding RNAs in the pathophysiology of Parkinson’s disease. Eur. J. Pharmacol. 2021, 896, 173914. [Google Scholar] [CrossRef] [PubMed]
  7. Basu, A.; Ash, P.E.; Wolozin, B.; Emili, A. Protein Interaction Network Biology in Neuroscience. Proteomics 2020, 21, e1900311. [Google Scholar] [CrossRef] [PubMed]
  8. Gene Ontology Consortium Gene Ontology Consortium: Going forward. Nucleic Acids Res. 2015, 43, D1049–D1056. [CrossRef]
  9. Titze-de-Almeida, S.S.; Soto-Sánchez, C.; Fernandez, E.; Koprich, J.B.; Brotchie, J.M.; Titze-de-Almeida, R. The Promise and Challenges of Developing miRNA-Based Therapeutics for Parkinson’s Disease. Cells 2020, 9, 841. [Google Scholar] [CrossRef] [Green Version]
  10. Hamberg, M.; Backes, C.; Fehlmann, T.; Hart, M.; Meder, B.; Meese, E.; Keller, A. MiRTargetLink--miRNAs, Genes and Interaction Networks. Int. J. Mol. Sci. 2016, 17, 564. [Google Scholar] [CrossRef]
  11. Kohl, M.; Wiese, S.; Warscheid, B. Cytoscape: Software for Visualization and Analysis of Biological Networks. Methods Mol. Biol. 2011, 696, 291–303. [Google Scholar]
  12. Tang, Y.; Li, M.; Wang, J.; Pan, Y.; Wu, F.-X. CytoNCA: A Cytoscape Plugin for Centrality Analysis and Evaluation of Protein Interaction Networks. Biosystems 2015, 127, 67–72. [Google Scholar] [CrossRef] [PubMed]
  13. He, X.; Zhang, J. Why Do Hubs Tend to Be Essential in Protein Networks? PLoS Genet. 2006, 2, e88. [Google Scholar] [CrossRef] [Green Version]
  14. Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K.P.; et al. STRING v10: Protein-Protein Interaction Networks, Integrated over the Tree of Life. Nucleic Acids Res. 2015, 43, D447–D452. [Google Scholar] [CrossRef]
  15. Kim, J.; Inoue, K.; Ishii, J.; Vanti, W.B.; Voronov, S.V.; Murchison, E.; Hannon, G.; Abeliovich, A. A MicroRNA Feedback Circuit in Midbrain Dopamine Neurons. Science 2007, 317, 1220–1224. [Google Scholar] [CrossRef] [Green Version]
  16. Sethi, P.; Lukiw, W.J. Micro-RNA Abundance and Stability in Human Brain: Specific Alterations in Alzheimer’s Disease Temporal Lobe Neocortex. Neurosci. Lett. 2009, 459, 100–104. [Google Scholar] [CrossRef]
  17. Miñones-Moyano, E.; Porta, S.; Escaramís, G.; Rabionet, R.; Iraola, S.; Kagerbauer, B.; Espinosa-Parrilla, Y.; Ferrer, I.; Estivill, X.; Martí, E. MicroRNA Profiling of Parkinson’s Disease Brains Identifies Early Downregulation of miR-34b/c Which Modulate Mitochondrial Function. Hum. Mol. Genet. 2011, 20, 3067–3078. [Google Scholar] [CrossRef] [PubMed]
  18. Cho, H.J.; Liu, G.; Jin, S.M.; Parisiadou, L.; Xie, C.; Yu, J.; Sun, L.; Ma, B.; Ding, J.; Vancraenenbroeck, R.; et al. MicroRNA-205 Regulates the Expression of Parkinson’s Disease-Related Leucine-Rich Repeat Kinase 2 Protein. Hum. Mol. Genet. 2013, 22, 608–620. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Alvarez-Erviti, L.; Seow, Y.; Schapira, A.H.V.; Rodriguez-Oroz, M.C.; Obeso, J.A.; Cooper, J.M. Influence of microRNA Deregulation on Chaperone-Mediated Autophagy and α-Synuclein Pathology in Parkinson’s Disease. Cell Death Dis. 2013, 4, e545. [Google Scholar] [CrossRef]
  20. Kim, W.; Lee, Y.; McKenna, N.D.; Yi, M.; Simunovic, F.; Wang, Y.; Kong, B.; Rooney, R.J.; Seo, H.; Stephens, R.M.; et al. miR-126 Contributes to Parkinson’s Disease by Dysregulating the Insulin-like Growth Factor/phosphoinositide 3-Kinase Signaling. Neurobiol. Aging 2014, 35, 1712–1721. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Schlaudraff, F.; Gründemann, J.; Fauler, M.; Dragicevic, E.; Hardy, J.; Liss, B. Orchestrated Increase of Dopamine and PARK mRNAs but Not miR-133b in Dopamine Neurons in Parkinson’s Disease. Neurobiol. Aging 2014, 35, 2302–2315. [Google Scholar] [CrossRef] [Green Version]
  22. Villar-Menéndez, I.; Porta, S.; Buira, S.P.; Pereira-Veiga, T.; Díaz-Sánchez, S.; Albasanz, J.L.; Ferrer, I.; Martín, M.; Barrachina, M. Increased Striatal Adenosine A2A Receptor Levels Is an Early Event in Parkinson’s Disease-Related Pathology and It Is Potentially Regulated by miR-34b. Neurobiol. Dis. 2014, 69, 206–214. [Google Scholar] [CrossRef]
  23. Cardo, L.F.; Coto, E.; Ribacoba, R.; Menéndez, M.; Moris, G.; Suárez, E.; Alvarez, V. MiRNA Profile in the Substantia Nigra of Parkinson’s Disease and Healthy Subjects. J. Mol. Neurosci. 2014, 54, 830–836. [Google Scholar] [CrossRef]
  24. Briggs, C.E.; Wang, Y.; Kong, B.; Woo, T.-U.W.; Iyer, L.K.; Sonntag, K.C. Midbrain Dopamine Neurons in Parkinson’s Disease Exhibit a Dysregulated miRNA and Target-Gene Network. Brain Res. 2015, 1618, 111–121. [Google Scholar] [CrossRef] [Green Version]
  25. Pantano, L.; Friedländer, M.R.; Escaramís, G.; Lizano, E.; Pallarès-Albanell, J.; Ferrer, I.; Estivill, X.; Martí, E. Specific Small-RNA Signatures in the Amygdala at Premotor and Motor Stages of Parkinson’s Disease Revealed by Deep Sequencing Analysis. Bioinformatics 2016, 32, 673–681. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Wake, C.; Labadorf, A.; Dumitriu, A.; Hoss, A.G.; Bregu, J.; Albrecht, K.H.; DeStefano, A.L.; Myers, R.H. Novel microRNA Discovery Using Small RNA Sequencing in Post-Mortem Human Brain. BMC Genomics 2016, 17, 776. [Google Scholar] [CrossRef] [Green Version]
  27. Tatura, R.; Kraus, T.; Giese, A.; Arzberger, T.; Buchholz, M.; Höglinger, G.; Müller, U. Parkinson’s Disease: SNCA-, PARK2-, and LRRK2- Targeting microRNAs Elevated in Cingulate Gyrus. Parkinsonism Relat. Disord. 2016, 33, 115–121. [Google Scholar] [CrossRef]
  28. Nair, V.D.; Ge, Y. Alterations of miRNAs Reveal a Dysregulated Molecular Regulatory Network in Parkinson’s Disease Striatum. Neurosci. Lett. 2016, 629, 99–104. [Google Scholar] [CrossRef]
  29. Hoss, A.G.; Labadorf, A.; Beach, T.G.; Latourelle, J.C.; Myers, R.H. microRNA Profiles in Parkinson’s Disease Prefrontal Cortex. Front. Aging Neurosci. 2016, 8, 36. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. 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, 557–564. [Google Scholar] [CrossRef] [PubMed]
  31. McMillan, K.J.; Murray, T.K.; Bengoa-Vergniory, N.; Cordero-Llana, O.; Cooper, J.; Buckley, A.; Wade-Martins, R.; Uney, J.B.; O’Neill, M.J.; Wong, L.F.; et al. Loss of MicroRNA-7 Regulation Leads to α-Synuclein Accumulation and Dopaminergic Neuronal Loss In Vivo. Mol. Ther. 2017, 25, 2404–2414. [Google Scholar] [CrossRef] [Green Version]
  32. Xing, R.-X.; Li, L.-G.; Liu, X.-W.; Tian, B.-X.; Cheng, Y. Down Regulation of miR-218, miR-124, and miR-144 Relates to Parkinson’s Disease via Activating NF-κB Signaling. Kaohsiung J. Med. Sci. 2020, 36, 786–792. [Google Scholar] [CrossRef]
  33. Hu, Y.-B.; Zhang, Y.-F.; Wang, H.; Ren, R.-J.; Cui, H.-L.; Huang, W.-Y.; Cheng, Q.; Chen, H.-Z.; Wang, G. miR-425 Deficiency Promotes Necroptosis and Dopaminergic Neurodegeneration in Parkinson’s Disease. Cell Death Dis. 2019, 10, 589. [Google Scholar] [CrossRef] [Green Version]
  34. Junn, E.; Lee, K.-W.; Jeong, B.S.; Chan, T.W.; Im, J.-Y.; Mouradian, M.M. Repression of Alpha-Synuclein Expression and Toxicity by microRNA-7. Proc. Natl. Acad. Sci. USA 2009, 106, 13052–13057. [Google Scholar] [CrossRef] [Green Version]
  35. Kabaria, S.; Choi, D.C.; Chaudhuri, A.D.; Mouradian, M.M.; Junn, E. Inhibition of miR-34b and miR-34c Enhances α-Synuclein Expression in Parkinson’s Disease. FEBS Lett. 2015, 589, 319–325. [Google Scholar] [CrossRef] [Green Version]
  36. Frankel, L.B.; Di Malta, C.; Wen, J.; Eskelinen, E.-L.; Ballabio, A.; Lund, A.H. A Non-Conserved miRNA Regulates Lysosomal Function and Impacts on a Human Lysosomal Storage Disorder. Nat. Commun. 2014, 5, 5840. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Søndergaard, H.B.; Airas, L.; Christensen, J.R.; Nielsen, B.R.; Börnsen, L.; Oturai, A.; Sellebjerg, F. Pregnancy-Induced Changes in microRNA Expression in Multiple Sclerosis. Front. Immunol. 2020, 11, 552101. [Google Scholar] [CrossRef] [PubMed]
  38. Samadian, M.; Gholipour, M.; Hajiesmaeili, M.; Taheri, M.; Ghafouri-Fard, S. The Eminent Role of microRNAs in the Pathogenesis of Alzheimer’s Disease. Front. Aging Neurosci. 2021, 13, 641080. [Google Scholar] [CrossRef]
  39. Qie, S.; Diehl, J.A. Cyclin D1, Cancer Progression, and Opportunities in Cancer Treatment. J. Mol. Med. 2016, 94, 1313–1326. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Herrup, K.; Yang, Y. Cell Cycle Regulation in the Postmitotic Neuron: Oxymoron or New Biology? Nat. Rev. Neurosci. 2007, 8, 368–378. [Google Scholar] [CrossRef] [PubMed]
  41. Höglinger, G.U.; Breunig, J.J.; Depboylu, C.; Rouaux, C.; Michel, P.P.; Alvarez-Fischer, D.; Boutillier, A.-L.; Degregori, J.; Oertel, W.H.; Rakic, P.; et al. The pRb/E2F Cell-Cycle Pathway Mediates Cell Death in Parkinson’s Disease. Proc. Natl. Acad. Sci. USA 2007, 104, 3585–3590. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Smith, P.D.; Crocker, S.J.; Jackson-Lewis, V.; Jordan-Sciutto, K.L.; Hayley, S.; Mount, M.P.; O’Hare, M.J.; Callaghan, S.; Slack, R.S.; Przedborski, S.; et al. Cyclin-Dependent Kinase 5 Is a Mediator of Dopaminergic Neuron Loss in a Mouse Model of Parkinson’s Disease. Proc. Natl. Acad. Sci. USA 2003, 100, 13650–13655. [Google Scholar] [CrossRef] [Green Version]
  43. Findeiss, E.; Schwarz, S.C.; Evsyukov, V.; Rösler, T.W.; Höllerhage, M.; Chakroun, T.; Nykänen, N.-P.; Shen, Y.; Wurst, W.; Kohl, M.; et al. Comprehensive miRNome-Wide Profiling in a Neuronal Cell Model of Synucleinopathy Implies Involvement of Cell Cycle Genes. Front. Cell Dev. Biol. 2021, 9, 561086. [Google Scholar] [CrossRef]
  44. Pino, E.; Amamoto, R.; Zheng, L.; Cacquevel, M.; Sarria, J.-C.; Knott, G.W.; Schneider, B.L. FOXO3 Determines the Accumulation of α-Synuclein and Controls the Fate of Dopaminergic Neurons in the Substantia Nigra. Hum. Mol. Genet. 2014, 23, 1435–1452. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Singh, P.; Hanson, P.S.; Morris, C.M. SIRT1 Ameliorates Oxidative Stress Induced Neural Cell Death and Is down-Regulated in Parkinson’s Disease. BMC Neurosci. 2017, 18, 46. [Google Scholar] [CrossRef]
  46. Li, X.; Feng, Y.; Wang, X.-X.; Truong, D.; Wu, Y.-C. The Critical Role of SIRT1 in Parkinson’s Disease: Mechanism and Therapeutic Considerations. Aging Dis. 2020, 11, 1608–1622. [Google Scholar] [CrossRef]
  47. Lee, H.-G.; Casadesus, G.; Nunomura, A.; Zhu, X.; Castellani, R.J.; Richardson, S.L.; Perry, G.; Felsher, D.W.; Petersen, R.B.; Smith, M.A. The Neuronal Expression of MYC Causes a Neurodegenerative Phenotype in a Novel Transgenic Mouse. Am. J. Pathol. 2009, 174, 891–897. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Bekker, M.; Abrahams, S.; Loos, B.; Bardien, S. Can the Interplay between Autophagy and Apoptosis Be Targeted as a Novel Therapy for Parkinson’s Disease? Neurobiol. Aging 2021, 100, 91–105. [Google Scholar] [CrossRef]
  49. Santo, E.E.; Paik, J. FOXO in Neural Cells and Diseases of the Nervous System. Curr. Top. Dev. Biol. 2018, 127, 105–118. [Google Scholar] [PubMed] [Green Version]
  50. Song, G.G.; Lee, Y.H. Pathway Analysis of Genome-Wide Association Studies for Parkinson’s Disease. Mol. Biol. Rep. 2013, 40, 2599–2607. [Google Scholar] [CrossRef]
  51. Wang, J.; Liu, Y.; Chen, T. Identification of Key Genes and Pathways in Parkinson’s Disease through Integrated Analysis. Mol. Med. Rep. 2017, 16, 3769–3776. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Shen, J.; Chen, X.-C.; Li, W.-J.; Han, Q.; Chen, C.; Lu, J.-M.; Zheng, J.-Y.; Xue, S.-R. Identification of Parkinson’s Disease-Related Pathways and Potential Risk Factors. J. Int. Med. Res. 2020, 48, 300060520957197. [Google Scholar] [CrossRef] [PubMed]
Cells 10 01410 g001 550
Figure 1. Number of shared target genes between the four sets of differentially expressed miRNAs (vertical bars). The lower part of the figure shows the intersection of sets associated with the vertical bars (dots connected by black lines).
Figure 1. Number of shared target genes between the four sets of differentially expressed miRNAs (vertical bars). The lower part of the figure shows the intersection of sets associated with the vertical bars (dots connected by black lines).
Cells 10 01410 g001
Cells 10 01410 g002 550
Figure 2. Enriched KEGG pathways for the target genes regulated by the four sets of differentially expressed miRNAs. (A) Upregulated DE-miRNAs’ target gene enrichment. (B) Downregulated DE-miRNAs’ target gene enrichment. (C) Substantia nigra DE-miRNAs’ target gene enrichment. (D) Putamen DE-miRNAs’ target genes enrichment. The color of the circles indicates the significance of the pathway, and the size of the circles indicates the number of target genes involved in each pathway.
Figure 2. Enriched KEGG pathways for the target genes regulated by the four sets of differentially expressed miRNAs. (A) Upregulated DE-miRNAs’ target gene enrichment. (B) Downregulated DE-miRNAs’ target gene enrichment. (C) Substantia nigra DE-miRNAs’ target gene enrichment. (D) Putamen DE-miRNAs’ target genes enrichment. The color of the circles indicates the significance of the pathway, and the size of the circles indicates the number of target genes involved in each pathway.
Cells 10 01410 g002
Cells 10 01410 g003 550
Figure 3. Regulatory networks for the four sets of differentially expressed miRNAs including the top 20 hub nodes. (A) Upregulated DE-miRNAs–mRNA network. (B) Downregulated DE-miRNAs–mRNA network. (C) Substantia nigra DE-miRNAs–mRNA network. (D) Putamen DE-miRNAs–mRNA network. The node size indicates degree centrality, and the scale of the node color indicates betweenness centrality. The center of the network displays the top five hub nodes according to degree centrality.
Figure 3. Regulatory networks for the four sets of differentially expressed miRNAs including the top 20 hub nodes. (A) Upregulated DE-miRNAs–mRNA network. (B) Downregulated DE-miRNAs–mRNA network. (C) Substantia nigra DE-miRNAs–mRNA network. (D) Putamen DE-miRNAs–mRNA network. The node size indicates degree centrality, and the scale of the node color indicates betweenness centrality. The center of the network displays the top five hub nodes according to degree centrality.
Cells 10 01410 g003
Cells 10 01410 g004 550
Figure 4. Heatmaps showing the hierarchical clustering of the global expression of the target genes regulated by the four sets of differentially expressed miRNAs across human brain tissues from the Genotype-Tissue Expression. (A) Upregulated DE-miRNAs’ hub genes expression. (B) Downregulated DE-miRNAs’ hub genes expression. (C) Substantia nigra DE-miRNAs’ hub gene expression. (D) Putamen DE-miRNAs’ hub gene expression.
Figure 4. Heatmaps showing the hierarchical clustering of the global expression of the target genes regulated by the four sets of differentially expressed miRNAs across human brain tissues from the Genotype-Tissue Expression. (A) Upregulated DE-miRNAs’ hub genes expression. (B) Downregulated DE-miRNAs’ hub genes expression. (C) Substantia nigra DE-miRNAs’ hub gene expression. (D) Putamen DE-miRNAs’ hub gene expression.
Cells 10 01410 g004
Table
Table 1. The main characteristics of 19 studies involving brain-related differentially expressed miRNA in Parkinson’s disease.
Table 1. The main characteristics of 19 studies involving brain-related differentially expressed miRNA in Parkinson’s disease.
Author, YearCountryBrain RegionSample SizeAge at DeathDisease Duration (years)Postmortem Interval (hours)PD Braak StagingmiRNAsUpreg miRNAsDownreg miRNAs
Kim et al., 2007 [15]USAMidbrain, cerebellum, frontal and prefrontal cortex370NANANA101
Sethi and Lukiw, 2009 [16]USATemporal cortex469NA1.2NA000
Miñones-Moyano et al., 2011 [17]SpainSN, amygdala, cerebellum, frontal cortex1472NA6.44202
Cho et al., 2013 [18]USAFrontal cortex1580NA8.23.5101
Alvarez-Erviti et al., 2013 [19]SpainSN, amygdala676NA4.8NA660
Kim et al., 2014 [20]USASN878NA20.7NA110
Schlaudraff et al., 2014 [21]GermanySN578NA16NA000
Villar-Menéndez et al., 2014 [22]SpainPutamen676NA7.94101
Cardo et al., 2014 [23]UKSN8774,2545.8NA1091
Briggs et al., 2015 [24]USASN8NANANANA17152
Pantano et al., 2015 [25]SpainAmygdala770NANANA000
Wake et al., 2016 [26]USAPrefrontal cortex2977NA8NA000
Tatura et al., 2016 [27]GermanyAnterior cingulate cortex2273NA30.6NA550
Nair and Ge, 2016 [28]USAPutamen1275NA13.4NA1367
Hoss et al., 2016 [29]USAPrefrontal cortex297710,511.1NA291118
Chatterjee and Roy, 2017 [30]IndiaPrefrontal cortex29NANANANA1192
McMillan et al., 2017 [31]UKSN68316,1NANA101
Xing et al., 2020 [32]ChinaPrefrontal cortex15705,5NANA303
Hu et al., 2020 [33]ChinaSN4NANANANA101
Abbreviations: Downreg miRNAs, number of downregulated miRNAs described by the study; miRNAs, number of differentially expressed miRNAs described by the study; NA, not available; PD, Parkinson’s disease; SN, substantia nigra; Upreg miRNAs, number of upregulated miRNAs described by the study.
Table
Table 2. Brain-related differentially expressed miRNAs in Parkinson’s disease described by previous studies.
Table 2. Brain-related differentially expressed miRNAs in Parkinson’s disease described by previous studies.
Upregulated miRNAsDownregulated miRNAsSN DE-miRNAsPutamen DE-miRNAs
hsa-let-7bhsa-miR-10b-5phsa-miR-133bhsa-miR-155-5p
hsa-let-7d-5phsa-miR-124hsa-miR-34bhsa-miR-219-2-3p
hsa-let-7f-5phsa-miR-1294hsa-miR-34chsa-miR-3200-3p
hsa-miR-106a §hsa-miR-129-5phsa-miR-425hsa-miR-34b
hsa-miR-106b-5phsa-miR-132-3phsa-miR-532-5phsa-miR-382-5p
hsa-miR-126hsa-miR-132-5phsa-miR-548dhsa-miR-421
hsa-miR-132hsa-miR-133bhsa-miR-7hsa-miR-423-5p
hsa-miR-135ahsa-miR-144hsa-miR-774hsa-miR-4421
hsa-miR-135bhsa-miR-145-5phsa-let-7bhsa-miR-204-5p
hsa-miR-144hsa-miR-148b-3phsa-miR-106a §hsa-miR-221-3p
hsa-miR-144-3phsa-miR-155-5phsa-miR-126hsa-miR-3195
hsa-miR-144-5phsa-miR-205hsa-miR-132hsa-miR-425-5p
hsa-miR-145hsa-miR-212-5phsa-miR-135ahsa-miR-485-3p
hsa-miR-148ahsa-miR-217hsa-miR-135bhsa-miR-95
hsa-miR-151bhsa-miR-218hsa-miR-145
hsa-miR-15b-5phsa-miR-219-2-3phsa-miR-148a
hsa-miR-16-2-3phsa-miR-3200-3phsa-miR-184
hsa-miR-181a-5phsa-miR-320bhsa-miR-198
hsa-miR-184hsa-miR-324-5phsa-miR-208b
hsa-miR-198hsa-miR-338-5phsa-miR-21 *
hsa-miR-199bhsa-miR-34b §hsa-miR-223
hsa-miR-204-5phsa-miR-34chsa-miR-224
hsa-miR-208bhsa-miR-362-5phsa-miR-26a
hsa-miR-21 *hsa-miR-378chsa-miR-26b
hsa-miR-216b-5phsa-miR-380-5phsa-miR-27a
hsa-miR-221hsa-miR-382-5phsa-miR-28-5p
hsa-miR-221-3phsa-miR-421hsa-miR-299-5p
hsa-miR-223hsa-miR-423-5phsa-miR-301b
hsa-miR-224hsa-miR-425hsa-miR-330-5p
hsa-miR-26ahsa-miR-4421hsa-miR-335
hsa-miR-26bhsa-miR-490-5phsa-miR-337-5p
hsa-miR-27ahsa-miR-491-5phsa-miR-339-5p
hsa-miR-28-5phsa-miR-532-5phsa-miR-373 *
hsa-miR-299-5phsa-miR-548dhsa-miR-374a
hsa-miR-301bhsa-miR-6511a-5phsa-miR-485-5p
hsa-miR-3117-3phsa-miR-670-3phsa-miR-542-3p
hsa-miR-3195hsa-miR-671-5phsa-miR-92a
hsa-miR-330-5phsa-miR-7hsa-miR-95
hsa-miR-335hsa-miR-774
hsa-miR-337-5p
hsa-miR-339-5p
hsa-miR-373 *
hsa-miR-374a
hsa-miR-376c-5p
hsa-miR-425-5p
hsa-miR-4443
hsa-miR-454-3p
hsa-miR-485-3p
hsa-miR-485-5p
hsa-miR-488
hsa-miR-5100
hsa-miR-516b-5p
hsa-miR-542-3p
hsa-miR-544
hsa-miR-5690
hsa-miR-92a
hsa-miR-92a-3p
hsa-miR-92b-3p
hsa-miR-93-5p
hsa-miR-95 §
Abbreviations: §, miRNA described in more than one study; SN DE-miRNAs, miRNAs differentially expressed in substantia nigra; Putamen DE-miRNAs, miRNAs differentially expressed in putamen. miRNAs in green indicate upregulated miRNAs, and red indicate downregulated miRNAs.
Table
Table 3. Predicted genes targeted by brain-related differentially expressed miRNAs in Parkinson’s disease.
Table 3. Predicted genes targeted by brain-related differentially expressed miRNAs in Parkinson’s disease.
For Upreg miRNAsFor Downreg miRNAsFor SN DE-miRNAsFor Putamen DE-miRNAs
APCADD3BCL2APAF1
APPANXA2CCND1BCL2
ATG16L1APCCDKN1ACCND1
ATMARID2CRKETS1
BCL2ARL6IP5CXCR4FOXO3
BCL2L11CAMTA1DNMT1ITPR1
CCND1CBFBEGFRMAFB
CDKN1ACCND1FBXW7MAP2K1
CDKN1BCDH2FGFR1MEIS1
CDKN1CCDK4FOXO1MYC
CRKCDK6FOXO3PIK3R1
DDIT4CDKN1AIGF1RPTEN
DICER1CEBPAIRS1SIRT1
DNMT1CHRAC1KRASSMAD4
E2F1CPNE3MAPK1SNAI1
E2F5CSRP1MYCSSX2IP
EZRCTGFPTBP2TCF12
FBXW7CTNNB1SIRT1THRB
FOSDDX6SOX2
FOXO1DNAJB1SP1
FOXO3E2F3SP3
HIPK2EDN1VEGFA
IRS1EGFR
ITGA5EIF4E
ITGB8ERG
KAT2BETS1
KRASFLI1
MAFBFLOT2
MAP2K1FOXO3
MAP2K4FSCN1
MAPK1FZD7
MAPK9GNA13
NFE2L2GNAI2
NLKGNAI3
NOTCH1GOLGA7
NTRK3HCN2
PTENIGF1R
PURAIL6R
RAP1BJAG1
RB1JUP
RECKKLF4
RGS5KRAS
SIRT1LIN7C
SMAD4LRP1
SMAD7MECP2
SOCS3MEF2A
SP1MYC
SP3NOTCH1
STAT3NRAS
STAT5ANT5E
TCEAL1PDLIM7
TCF4PHC2
TGFBR1PICALM
TGFBR2PIK3CA
THRBPODXL
TMED7PSIP1
VEGFAPSMG1
ZBTB4PTBP1
PTBP2
PTEN
RAB11FIP2
RAC1
RHOA
ROCK1
SIRT1
SMAD3
SMAD4
SOX2
SOX9
SP1
SWAP70
SYNE1
TAGLN2
TP53
TPM1
TPM3
TWF1
VEGFA
YWHAZ
Abbreviations: DE-miRNAs, differentially expressed miRNA; Downreg; downregulated; SN; substantia nigra; Upreg, upregulated.
Table
Table 4. The top 15 hub nodes in the regulatory networks associated with brain-related differentially expressed miRNAs in Parkinson’s disease.
Table 4. The top 15 hub nodes in the regulatory networks associated with brain-related differentially expressed miRNAs in Parkinson’s disease.
Regulatory Network Targeted by Upregulated miRNAsRegulatory Network Targeted by Downregulated miRNAs
NodeDCNodeBCNodeCCNodeDCNodeBCNodeCC
CCND137CCND1199.83435CCND10.7571428TP5344EGFR648.0353TP530.6923077
STAT336NOTCH1188.92195NOTCH10.7571428EGFR43VEGFA606.03754EGFR0.6857143
PTEN36STAT3188.05045PTEN0.7571428MYC42TP53524.7005MYC0.6792453
NOTCH136KRAS162.67937KRAS0.7464788CTNNB141CTNNB1334.24814VEGFA0.6666667
KRAS36VEGFA162.09471STAT30.7464788VEGFA40RHOA282.5179PTEN0.6605505
VEGFA34PTEN157.23015VEGFA0.7361111PTEN38MYC268.5208CTNNB10.6545454
MAPK133MAPK1153.92905MAPK10.7162162KRAS37ANXA2264.8916KRAS0.6371681
CDKN1A31CDKN1A126.64957CDKN1A0.6973684CCND136PTEN236.95053CCND10.6260869
SMAD429E2F1111.34685SMAD40.6794871NOTCH135NRAS180.74113NOTCH10.6206896
FOS27SMAD490.59089FOS0.654321PIK3CA33PIK3CA167.35359PIK3CA0.6153846
ATM26CRK57.47987SIRT10.654321SMAD432CDKN1A166.20766SMAD40.6
SIRT126FOS57.051147ATM0.654321SIRT131TPM1154.96588RHOA0.6
FOXO124ATM52.87451FOXO10.6385542RHOA30TAGLN2144.89015SIRT10.5901639
FOXO324TGFBR152.30357CDKN1B0.6309523SMAD329PSIP1142.28922CDKN1A0.5853658
BCL2L1123FOXO149.671513FOXO30.6309523CDKN2A28FLI1142.0CDKN2A0.5806451
Regulatory Network Targeted by DE-miRNAs in SNRegulatory Network Targeted by DE-miRNAs in Putamen
NodeDCNodeBCNodeCCNodeDCNodeBCNodeCC
EGFR18KRAS24.592207CCND10.9090909MYC13CCND169.53333CCND10.8421052
MYC18CCND121.90339KRAS0.9090909CCND113MYC59.533333MYC0.8421052
KRAS18MYC21.90339MYC0.9090909PTEN10BCL230.0FOXO30.6956522
CCND118EGFR20.09127EGFR0.9090909FOXO310ETS114.333333PTEN0.6956522
MAPK117MAPK119.217676MAPK10.8695652MAP2K19PIK3R16.3333335MAP2K10.6666667
VEGFA16CDKN1A17.548702VEGFA0.8333333ETS18FOXO35.2ETS10.64
CDKN1A14DNMT110.332828CDKN1A0.7692308SNAI17PTEN5.2PIK3R10.6153846
SIRT113SP110.186725FOXO30.7407407SMAD47APAF12.6666667SIRT10.6153846
IGF1R13VEGFA7.7579365IGF1R0.7407407SIRT17MAP2K12.2SMAD40.6153846
FOXO313FGFR16.8968253SIRT10.7407407PIK3R17SIRT10.3333333SNAI10.6153846
SOX212IGF1R5.0380955FOXO10.7142857APAF15SMAD40.3333333APAF10.5925926
IRS112IRS13.0833333SOX20.7142857BCL24SNAI10.3333333BCL20.5714286
FOXO112SOX23.0269842DNMT10.6896552THRB2ITPR10.0MEIS10.4848485
FGFR111FOXO32.8960319FGFR10.6896552TCF122MAFB0.0TCF120.4848485
DNMT111SIRT11.9690477IRS10.6896552MEIS12MEIS10.0THRB0.4848485
Abbreviations: BC, betweenness centrality; CC, closeness centrality; DC, degree centrality; DE-miRNAs, differentially expressed miRNA; SN; substantia nigra.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Santos-Lobato, B.L.; Vidal, A.F.; Ribeiro-dos-Santos, Â. Regulatory miRNA–mRNA Networks in Parkinson’s Disease. Cells 2021, 10, 1410. https://doi.org/10.3390/cells10061410

AMA Style

Santos-Lobato BL, Vidal AF, Ribeiro-dos-Santos Â. Regulatory miRNA–mRNA Networks in Parkinson’s Disease. Cells. 2021; 10(6):1410. https://doi.org/10.3390/cells10061410

Chicago/Turabian Style

Santos-Lobato, Bruno Lopes, Amanda Ferreira Vidal, and Ândrea Ribeiro-dos-Santos. 2021. "Regulatory miRNA–mRNA Networks in Parkinson’s Disease" Cells 10, no. 6: 1410. https://doi.org/10.3390/cells10061410

APA Style

Santos-Lobato, B. L., Vidal, A. F., & Ribeiro-dos-Santos, Â. (2021). Regulatory miRNA–mRNA Networks in Parkinson’s Disease. Cells, 10(6), 1410. https://doi.org/10.3390/cells10061410

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop