Hostname: page-component-848d4c4894-4hhp2 Total loading time: 0 Render date: 2024-05-01T09:30:58.647Z Has data issue: false hasContentIssue false
  • English
  • Français

Extracellular vesicle microRNA-mediated transcriptional regulation may contribute to dementia with Lewy bodies molecular pathology

Published online by Cambridge University Press:  21 June 2023

Fatma Busra Isik ,
Helen Miranda Knight  and
Anto P. Rajkumar Open the ORCID record for Anto P. Rajkumar [Opens in a new window]
Fatma Busra Isik
Affiliation:
School of Life Science, Queen’s Medical Centre, University of Nottingham, Nottingham, UK
Helen Miranda Knight
Affiliation:
School of Life Science, Queen’s Medical Centre, University of Nottingham, Nottingham, UK
Anto P. Rajkumar*
Affiliation:
Institute of Mental Health, Mental Health and Clinical Neurosciences Academic Unit, University of Nottingham, Nottingham, UK Mental Health Services for Older People, Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, UK
*
Corresponding author: Anto P. Rajkumar; Email: Anto.Rajamani@nottingham.ac.uk
Rights & Permissions [Opens in a new window]

Abstract

Objective:

Dementia with Lewy bodies (DLB) is the second most common dementia. Advancing our limited understanding of its molecular pathogenesis is essential for identifying novel biomarkers and therapeutic targets for DLB. DLB is an α-synucleinopathy, and small extracellular vesicles (SEV) from people with DLB can transmit α-synuclein oligomerisation between cells. Post-mortem DLB brains and serum SEV from those with DLB share common miRNA signatures, and their functional implications are uncertain. Hence, we aimed to investigate potential targets of DLB-associated SEV miRNA and to analyse their functional implications.

Methods:

We identified potential targets of six previously reported differentially expressed miRNA genes in serum SEV of people with DLB (MIR26A1, MIR320C2, MIR320D2, MIR548BA, MIR556, and MIR4722) using miRBase and miRDB databases. We analysed functional implications of these targets using EnrichR gene set enrichment analysis and analysed their protein interactions using Reactome pathway analysis.

Results:

These SEV miRNA may regulate 4278 genes that were significantly enriched among the genes involved in neuronal development, cell-to-cell communication, vesicle-mediated transport, apoptosis, regulation of cell cycle, post-translational protein modifications, and autophagy lysosomal pathway, after Benjamini-Hochberg false discovery rate correction at 5%. The miRNA target genes and their protein interactions were significantly associated with several neuropsychiatric disorders and with multiple signal transduction, transcriptional regulation, and cytokine signalling pathways.

Conclusion:

Our findings provide in-silico evidence that potential targets of DLB-associated SEV miRNAs may contribute to Lewy pathology by transcriptional regulation. Experimental validation of these dysfunctional pathways is warranted and could lead to novel therapeutic avenues for DLB.

Keywords

alpha-synucleinextracellular vesiclesgene ontologymicroRNAsLewy body disease
Type
Original Article
Information
Acta Neuropsychiatrica , Volume 36 , Issue 1 , February 2024 , pp. 29 - 38
Check for updates
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Scandinavian College of Neuropsychopharmacology

Significant outcomes

  • Differentially expressed miRNA in serum SEV from people with DLB may contribute to the molecular pathogenesis of DLB by regulating expression of genes involved in neuronal development, cell-to-cell communication, vesicle-mediated transport, apoptosis, regulation of cell cycle, post-translational protein modifications, and autophagy lysosomal pathway.

  • Potential target genes of the DLB-associated SEV miRNA were significantly enriched among the genes involved in axon guidance, focal adhesion, endocytosis, autophagy, mRNA surveillance, long-term potentiation, ubiquitin-mediated proteolysis, cholinergic and glutaminergic synapses, and neurotrophin, Wnt, mTOR, ErbB, and FoxO signalling pathways.

  • Protein interactions of the potential target genes may contribute to DLB molecular pathogenesis by regulating cell cycle, protein phosphorylation, interleukin-6 signalling, transcriptional regulation, and post-transcriptional silencing by small RNA.

Limitations

  • This study did not investigate the potential targets of other classes of SEV non-coding small RNA that were differentially expressed in people with DLB.

  • miRNA target prediction is still an evolving scientific discipline.

  • Further experimental validation of identified dysfunctional molecular processes and pathways is needed.

Introduction

Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia (Rajkumar and Aarsland, Reference Rajkumar, Aarsland, Geddes, Andreasen and Goodwin2020). Lewy bodies are intraneuronal eosinophilic cytoplasmic inclusion bodies. Post-mortem neuropathological examination of brains of people, who were clinically diagnosed to have DLB antemortem, often reveals Lewy bodies in brainstem, limbic system, and/or cerebral cortex (Garcia-Esparcia et al., Reference Garcia-Esparcia, Lopez-Gonzalez, Grau-Rivera, Garcia-Garrido, Konetti, Llorens, Zafar, Carmona, Del Rio, Zerr, Gelpi and Ferrer2017). DLB is a primary α-synucleinopathy (Rajkumar and Aarsland, Reference Rajkumar, Aarsland, Geddes, Andreasen and Goodwin2020), and α-synuclein aggregation is widely recognised as the key initial step in the formation of Lewy bodies (Beyer et al., Reference Beyer, Domingo-Sabat and Ariza2009). DLB causes earlier mortality (Oesterhus et al., Reference Oesterhus, Soennesyn, Rongve, Ballard, Aarsland and Vossius2014), earlier nursing home admissions (Rongve et al., Reference Rongve, Vossius, Nore, Testad and Aarsland2014), poorer quality-of-life (Bostrom et al., Reference Bostrom, Jonsson, Minthon and Londos2007), more frequent falls, higher care costs (Vossius et al., Reference Vossius, Rongve, Testad, Wimo and Aarsland2014), and more caregivers’ burden (Svendsboe et al., Reference Svendsboe, Terum, Testad, Aarsland, Ulstein, Corbett and Rongve2016) than Alzheimer’s disease (AD). Accurate distinction between DLB and AD is essential because they differ in their prognosis, neuropsychiatric symptoms, and pharmacological management (Watts et al., Reference Watts, Storr, Barr and Rajkumar2023). Both AD and DLB are currently diagnosed by their clinical diagnostic criteria (McKeith et al., Reference Mckeith, Boeve, Dickson, Halliday, Taylor, Weintraub, Aarsland, Galvin, Attems, Ballard, Bayston, Beach, Blanc, Bohnen, Bonanni, Bras, Brundin, Burn, Chen-Plotkin, Duda, El-Agnaf, Feldman, Ferman, Ffytche, Fujishiro, Galasko, Goldman, Gomperts, Graff-Radford, Honig, Iranzo, Kantarci, Kaufer, Kukull, Lee, Leverenz, Lewis, Lippa, Lunde, Masellis, Masliah, Mclean, Mollenhauer, Montine, Moreno, Mori, Murray, O’brien, Orimo, Postuma, Ramaswamy, Ross, Salmon, Singleton, Taylor, Thomas, Tiraboschi, Toledo, Trojanowski, Tsuang, Walker, Yamada and Kosaka2017; WHO, 2018), and DLB is often misdiagnosed as AD or other dementia in clinical settings (Freer, Reference Freer2017). Failing to diagnose DLB early, and treating visual hallucinations and challenging behaviours, which are more frequent in DLB than AD, with any antipsychotic medication risk potentially fatal adverse events like neuroleptic malignant syndrome. Reliable biomarkers from clinically collectable biological fluids (Ashton et al., Reference Ashton, Hye, Rajkumar, Leuzy, Snowden, Suarez-Calvet, Karikari, Scholl, La Joie, Rabinovici, Hoglund, Ballard, Hortobagyi, Svenningsson, Blennow, Zetterberg and Aarsland2020) and safer effective treatments (Stinton et al., Reference Stinton, Mckeith, Taylor, Lafortune, Mioshi, Mak, Cambridge, Mason, Thomas and O’brien2015; Velayudhan et al., Reference Velayudhan, Ffytche, Ballard and Aarsland2017; Watts et al., Reference Watts, Storr, Barr and Rajkumar2023) are urgently needed for DLB. Improving our current limited understanding of DLB molecular pathology is prerequisite for addressing this need and for improving clinical diagnosis and management of DLB (Zhang et al., Reference Zhang, Kim and Narayanan2015).

Extracellular vesicles (EV) are cell-derived vesicles with lipid bilayer membrane that cannot replicate (Thery et al., Reference Théry, Witwer, Aikawa, Alcaraz, Anderson, Andriantsitohaina, Antoniou, Arab, Archer, Atkin-Smith, Ayre, Bach, Bachurski, Baharvand, Balaj, Baldacchino, Bauer, Baxter, Bebawy, Beckham, Bedina Zavec, Benmoussa, Berardi, Bergese, Bielska, Blenkiron, Bobis-Wozowicz, Boilard, Boireau, Bongiovanni, Borràs, Bosch, Boulanger, Breakefield, Breglio, Brennan, Brigstock, Brisson, Broekman, Bromberg, Bryl-Górecka, Buch, Buck, Burger, Busatto, Buschmann, Bussolati, Buzás, Byrd, Camussi, Carter, Caruso, Chamley, Chang, Chen, Chen, Cheng, Chin, Clayton, Clerici, Cocks, Cocucci, Coffey, Cordeiro-da-Silva, Couch, Coumans, Coyle, Crescitelli, Criado, D’Souza-Schorey, Das, Datta Chaudhuri, de Candia, De Santana, De Wever, del Portillo, Demaret, Deville, Devitt, Dhondt, Di Vizio, Dieterich, Dolo, Dominguez Rubio, Dominici, Dourado, Driedonks, Duarte, Duncan, Eichenberger, Ekström, EL Andaloussi, Elie-Caille, Erdbrügger, Falcón-Pérez, Fatima, Fish, Flores-Bellver, Försönits, Frelet-Barrand, Fricke, Fuhrmann, Gabrielsson, Gámez-Valero, Gardiner, Gärtner, Gaudin, Gho, Giebel, Gilbert, Gimona, Giusti, Goberdhan, Görgens, Gorski, Greening, Gross, Gualerzi, Gupta, Gustafson, Handberg, Haraszti, Harrison, Hegyesi, Hendrix, Hill, Hochberg, Hoffmann, Holder, Holthofer, Hosseinkhani, Hu, Huang, Huber, Hunt, Ibrahim, Ikezu, Inal, Isin, Ivanova, Jackson, Jacobsen, Jay, Jayachandran, Jenster, Jiang, Johnson, Jones, Jong, Jovanovic-Talisman, Jung, Kalluri, Kano, Kaur, Kawamura, Keller, Khamari, Khomyakova, Khvorova, Kierulf, Kim, Kislinger, Klingeborn, Klinke, Kornek, Kosanović, Kovács, Krämer-Albers, Krasemann, Krause, Kurochkin, Kusuma, Kuypers, Laitinen, Langevin, Languino, Lannigan, Lässer, Laurent, Lavieu, Lázaro-Ibáñez, Le Lay, Lee, Lee, Lemos, Lenassi, Leszczynska, Li, Liao, Libregts, Ligeti, Lim, Lim, Linē, Linnemannstöns, Llorente, Lombard, Lorenowicz, Lörincz, Lötvall, Lovett, Lowry, Loyer, Lu, Lukomska, Lunavat, Maas, Malhi, Marcilla, Mariani, Mariscal, Martens-Uzunova, Martin-Jaular, Martinez, Martins, Mathieu, Mathivanan, Maugeri, McGinnis, McVey, Meckes, Meehan, Mertens, Minciacchi, Möller, Møller Jørgensen, Morales-Kastresana, Morhayim, Mullier, Muraca, Musante, Mussack, Muth, Myburgh, Najrana, Nawaz, Nazarenko, Nejsum, Neri, Neri, Nieuwland, Nimrichter, Nolan, Nolte-’t Hoen, Noren Hooten, O’Driscoll, O’Grady, O’Loghlen, Ochiya, Olivier, Ortiz, Ortiz, Osteikoetxea, Østergaard, Ostrowski, Park, Pegtel, Peinado, Perut, Pfaffl, Phinney, Pieters, Pink, Pisetsky, Pogge von Strandmann, Polakovicova, Poon, Powell, Prada, Pulliam, Quesenberry, Radeghieri, Raffai, Raimondo, Rak, Ramirez, Raposo, Rayyan, Regev-Rudzki, Ricklefs, Robbins, Roberts, Rodrigues, Rohde, Rome, Rouschop, Rughetti, Russell, Saá, Sahoo, Salas-Huenuleo, Sánchez, Saugstad, Saul, Schiffelers, Schneider, Schøyen, Scott, Shahaj, Sharma, Shatnyeva, Shekari, Shelke, Shetty, Shiba, Siljander, Silva, Skowronek, Snyder, Soares, Sódar, Soekmadji, Sotillo, Stahl, Stoorvogel, Stott, Strasser, Swift, Tahara, Tewari, Timms, Tiwari, Tixeira, Tkach, Toh, Tomasini, Torrecilhas, Tosar, Toxavidis, Urbanelli, Vader, van Balkom, van der Grein, Van Deun, van Herwijnen, Van Keuren-Jensen, van Niel, van Royen, van Wijnen, Vasconcelos, Vechetti, Veit, Vella, Velot, Verweij, Vestad, Viñas, Visnovitz, Vukman, Wahlgren, Watson, Wauben, Weaver, Webber, Weber, Wehman, Weiss, Welsh, Wendt, Wheelock, Wiener, Witte, Wolfram, Xagorari, Xander, Xu, Yan, Yáñez-Mó, Yin, Yuana, Zappulli, Zarubova, Žėkas, Zhang, Zhao, Zheng, Zheutlin, Zickler, Zimmermann, Zivkovic, Zocco and Zuba-Surma2018). They are secreted into extracellular environment (Yanez-Mo et al., Reference Yanez-Mo, Siljander, Andreu, Zavec, Borras, Buzas, Buzas, Casal, Cappello, Carvalho, Colas, Cordeiro-Da Silva, Fais, Falcon-Perez, Ghobrial, Giebel, Gimona, Graner, Gursel, Gursel, Heegaard, Hendrix, Kierulf, Kokubun, Kosanovic, Kralj-Iglic, Kramer-Albers, Laitinen, Lasser, Lener, Ligeti, Line, Lipps, Llorente, Lotvall, Mancek-Keber, Marcilla, Mittelbrunn, Nazarenko, Nolte-’T Hoen, Nyman, O’driscoll, Olivan, Oliveira, Pallinger, Del Portillo, Reventos, Rigau, Rohde, Sammar, Sanchez-Madrid, Santarem, Schallmoser, Ostenfeld, Stoorvogel, Stukelj, Van Der Grein, Vasconcelos, Wauben and De Wever2015; Ibrahim and Khan, Reference Ibrahim and Khan2022), and they play an important role in communication between cells (Saint-Pol et al., Reference Saint-Pol, Gosselet, Duban-Deweer, Pottiez and Karamanos2020). EV carry biologically active proteins, lipids and nucleic acids including messenger RNA (mRNA), microRNA (miRNA), other small non-coding RNA, mitochondrial DNA, and genomic DNA fragments from their cells of origin (Trotta et al., Reference Trotta, Panaro, Cianciulli, Mori, Di Benedetto and Porro2018). Because of their potential for transferring biological information, several drug delivery systems using EV are being developed (Vader et al., Reference Vader, Mol, Pasterkamp and Schiffelers2016; Mustajab et al., Reference Mustajab, Kwamboka, Choi, Kang, Kim, Han, Han, Lee, Song and Chwae2022). EV are usually classified on the basis of their physical or biochemical characteristics. The Minimal information for studies of extracellular vesicles (MISEV2018) guidelines (Thery et al., Reference Théry, Witwer, Aikawa, Alcaraz, Anderson, Andriantsitohaina, Antoniou, Arab, Archer, Atkin-Smith, Ayre, Bach, Bachurski, Baharvand, Balaj, Baldacchino, Bauer, Baxter, Bebawy, Beckham, Bedina Zavec, Benmoussa, Berardi, Bergese, Bielska, Blenkiron, Bobis-Wozowicz, Boilard, Boireau, Bongiovanni, Borràs, Bosch, Boulanger, Breakefield, Breglio, Brennan, Brigstock, Brisson, Broekman, Bromberg, Bryl-Górecka, Buch, Buck, Burger, Busatto, Buschmann, Bussolati, Buzás, Byrd, Camussi, Carter, Caruso, Chamley, Chang, Chen, Chen, Cheng, Chin, Clayton, Clerici, Cocks, Cocucci, Coffey, Cordeiro-da-Silva, Couch, Coumans, Coyle, Crescitelli, Criado, D’Souza-Schorey, Das, Datta Chaudhuri, de Candia, De Santana, De Wever, del Portillo, Demaret, Deville, Devitt, Dhondt, Di Vizio, Dieterich, Dolo, Dominguez Rubio, Dominici, Dourado, Driedonks, Duarte, Duncan, Eichenberger, Ekström, EL Andaloussi, Elie-Caille, Erdbrügger, Falcón-Pérez, Fatima, Fish, Flores-Bellver, Försönits, Frelet-Barrand, Fricke, Fuhrmann, Gabrielsson, Gámez-Valero, Gardiner, Gärtner, Gaudin, Gho, Giebel, Gilbert, Gimona, Giusti, Goberdhan, Görgens, Gorski, Greening, Gross, Gualerzi, Gupta, Gustafson, Handberg, Haraszti, Harrison, Hegyesi, Hendrix, Hill, Hochberg, Hoffmann, Holder, Holthofer, Hosseinkhani, Hu, Huang, Huber, Hunt, Ibrahim, Ikezu, Inal, Isin, Ivanova, Jackson, Jacobsen, Jay, Jayachandran, Jenster, Jiang, Johnson, Jones, Jong, Jovanovic-Talisman, Jung, Kalluri, Kano, Kaur, Kawamura, Keller, Khamari, Khomyakova, Khvorova, Kierulf, Kim, Kislinger, Klingeborn, Klinke, Kornek, Kosanović, Kovács, Krämer-Albers, Krasemann, Krause, Kurochkin, Kusuma, Kuypers, Laitinen, Langevin, Languino, Lannigan, Lässer, Laurent, Lavieu, Lázaro-Ibáñez, Le Lay, Lee, Lee, Lemos, Lenassi, Leszczynska, Li, Liao, Libregts, Ligeti, Lim, Lim, Linē, Linnemannstöns, Llorente, Lombard, Lorenowicz, Lörincz, Lötvall, Lovett, Lowry, Loyer, Lu, Lukomska, Lunavat, Maas, Malhi, Marcilla, Mariani, Mariscal, Martens-Uzunova, Martin-Jaular, Martinez, Martins, Mathieu, Mathivanan, Maugeri, McGinnis, McVey, Meckes, Meehan, Mertens, Minciacchi, Möller, Møller Jørgensen, Morales-Kastresana, Morhayim, Mullier, Muraca, Musante, Mussack, Muth, Myburgh, Najrana, Nawaz, Nazarenko, Nejsum, Neri, Neri, Nieuwland, Nimrichter, Nolan, Nolte-’t Hoen, Noren Hooten, O’Driscoll, O’Grady, O’Loghlen, Ochiya, Olivier, Ortiz, Ortiz, Osteikoetxea, Østergaard, Ostrowski, Park, Pegtel, Peinado, Perut, Pfaffl, Phinney, Pieters, Pink, Pisetsky, Pogge von Strandmann, Polakovicova, Poon, Powell, Prada, Pulliam, Quesenberry, Radeghieri, Raffai, Raimondo, Rak, Ramirez, Raposo, Rayyan, Regev-Rudzki, Ricklefs, Robbins, Roberts, Rodrigues, Rohde, Rome, Rouschop, Rughetti, Russell, Saá, Sahoo, Salas-Huenuleo, Sánchez, Saugstad, Saul, Schiffelers, Schneider, Schøyen, Scott, Shahaj, Sharma, Shatnyeva, Shekari, Shelke, Shetty, Shiba, Siljander, Silva, Skowronek, Snyder, Soares, Sódar, Soekmadji, Sotillo, Stahl, Stoorvogel, Stott, Strasser, Swift, Tahara, Tewari, Timms, Tiwari, Tixeira, Tkach, Toh, Tomasini, Torrecilhas, Tosar, Toxavidis, Urbanelli, Vader, van Balkom, van der Grein, Van Deun, van Herwijnen, Van Keuren-Jensen, van Niel, van Royen, van Wijnen, Vasconcelos, Vechetti, Veit, Vella, Velot, Verweij, Vestad, Viñas, Visnovitz, Vukman, Wahlgren, Watson, Wauben, Weaver, Webber, Weber, Wehman, Weiss, Welsh, Wendt, Wheelock, Wiener, Witte, Wolfram, Xagorari, Xander, Xu, Yan, Yáñez-Mó, Yin, Yuana, Zappulli, Zarubova, Žėkas, Zhang, Zhao, Zheng, Zheutlin, Zickler, Zimmermann, Zivkovic, Zocco and Zuba-Surma2018) encourage using the term ’small extracellular vesicles’ (SEV) for the EV that are smaller than 200 nm in size. SEV consist of heterogeneous population of EV including those called as exosomes (Kalluri and LeBleu, Reference Kalluri and Lebleu2020).

SEV RNA cargo includes miRNA that can regulate expression of thousands of genes (Lu and Rothenberg, Reference Lu and Rothenberg2018). miRNA are approximately 22 nucleotides-long single-stranded non-coding RNA (Liu et al., Reference Liu, Li and Cairns2014). Several hundred nucleotides-long miRNA precursors, called pri-miRNA, are transcribed from miRNA genes usually by RNA polymerase-II. The pri-miRNA are processed first in nuclei and then in cytoplasm to generate small mature miRNA that regulate transcription of other genes (Vishnoi and Rani, Reference Vishnoi and Rani2023). miRNA exert important regulatory control over neuronal development and homeostasis (Liu et al., Reference Liu, Li and Cairns2014). Differentially expressed EV miRNA have been associated with neurodegenerative diseases such as Alzheimer’s disease (Gamez-Valero et al., Reference Gamez-Valero, Campdelacreu, Vilas, Ispierto, Rene, Alvarez, Armengol, Borras and Beyer2019; Wang et al., Reference Wang, Yuan, Ding, Zhu, Qi, Zhang, Li, Xia and Zheng2022), demyelinating diseases like multiple sclerosis (Ovchinnikova et al., Reference Ovchinnikova, Zalevsky and Lomakin2022), and psychiatric disorders such as schizophrenia and bipolar disorder (Du et al., Reference Du, Yu, Hu, Li, Wei, Pan, Li, Zheng, Qin, Liu and Cheng2019; Gruzdev et al., Reference Gruzdev, Yakovlev, Druzhkova, Guekht and Gulyaeva2019).

SEV, separated from cerebrospinal fluid (CSF) of people with DLB, has been demonstrated to induce alpha-synuclein oligomerisation in H4 neuro-glioma cells (Stuendl et al., Reference Stuendl, Kunadt, Kruse, Bartels, Moebius, Danzer, Mollenhauer and Schneider2016). Moreover, it has been shown that injection of brain-derived SEV from people with DLB into the brains of mice could induce α-synuclein oligomerisation (Ngolab et al., Reference Ngolab, Trinh, Rockenstein, Mante, Florio, Trejo, Masliah, Adame, Masliah and Rissman2017). As SEV may transmit disease-associated nucleic acids and proteins between cells (Candelario and Steindler, Reference Candelario and Steindler2014), we hypothesise that differentially expressed SEV miRNA from people with DLB may contribute to DLB pathology by regulating expression of genes involved in the molecular pathways that are known to be associated with DLB. We have already reported an RNA sequencing (RNA-Seq) study that investigated differentially expressed genes (DEG) in post-mortem anterior cingulate and dorsolateral prefrontal cortices of people with DLB (Rajkumar et al., Reference Rajkumar, Bidkhori, Shoaie, Clarke, Morrin, Hye, Williams, Ballard, Francis and Aarsland2020) and another RNA-Seq study that investigated serum SEV DEG in people living with DLB (Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021). Four miRNA genes, MIR320C2, MIR320D2, MIR548BA and MIR4722, were found significantly (p < 0.05) differentially expressed in both post-mortem DLB brains and serum SEV from people living with DLB (Rajkumar et al., Reference Rajkumar, Bidkhori, Shoaie, Clarke, Morrin, Hye, Williams, Ballard, Francis and Aarsland2020; Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021). Differential expression levels of two more serum SEV miRNA DEG in DLB, MIR26A1 and MIR556, could be replicated by high-throughput quantitative polymerase chain reaction (qPCR) (Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021). MIR26A1 and MIR556 were significantly upregulated, and other four miRNA genes were significantly downregulated in serum SEV from people living with DLB. In this study, we aimed to investigate the potential targets of the miRNA, transcribed by these six miRNA genes, and to analyse their downstream functional implications.

Material and methods

Differentially expressed serum SEV miRNA in DLB

Our prior RNA-Seq study investigated serum SEV RNA profiles in people with DLB (n = 10) and age- and gender-matched comparisons (n = 10). That study obtained the 20 serum samples from the biobanks of three Norwegian cohorts that have obtained generic ethical approval for further studies using their serum samples (Selnes et al., Reference Selnes, Aarsland, Bjornerud, Gjerstad, Wallin, Hessen, Reinvang, Grambaite, Auning, Kjaervik, Due-Tonnessen, Stenset and Fladby2013; Rongve et al., Reference Rongve, Soennesyn, Skogseth, Oesterhus, Hortobagyi, Ballard, Auestad and Aarsland2016; Fladby et al., Reference Fladby, Palhaugen, Selnes, Waterloo, Brathen, Hessen, Almdahl, Arntzen, Auning, Eliassen, Espenes, Grambaite, Grontvedt, Johansen, Johnsen, Kalheim, Kirsebom, Muller, Nakling, Rongve, Sando, Siafarikas, Stav, Tecelao, Timon, Bekkelund and Aarsland2017). Serum samples from 10 people living with DLB and three gender- and age (±3 years)-matched comparisons without cognitive impairment or Parkinson’s disease were obtained from the dementia study of western Norway (DemWest) (Rongve et al., Reference Rongve, Soennesyn, Skogseth, Oesterhus, Hortobagyi, Ballard, Auestad and Aarsland2016). Another seven gender- and age-matched comparison serum samples were obtained from two cohorts at Akershus University Hospital dementia research centres (Selnes et al., Reference Selnes, Aarsland, Bjornerud, Gjerstad, Wallin, Hessen, Reinvang, Grambaite, Auning, Kjaervik, Due-Tonnessen, Stenset and Fladby2013; Fladby et al., Reference Fladby, Palhaugen, Selnes, Waterloo, Brathen, Hessen, Almdahl, Arntzen, Auning, Eliassen, Espenes, Grambaite, Grontvedt, Johansen, Johnsen, Kalheim, Kirsebom, Muller, Nakling, Rongve, Sando, Siafarikas, Stav, Tecelao, Timon, Bekkelund and Aarsland2017). 70% (n = 14) of the samples were from men. The mean age of the people with DLB was 77.3 (SD = 3.7) years, and the mean age of the people in comparison group was 76.8 (SD = 4.1) years. Further details of the cohorts have been published elsewhere (Selnes et al., Reference Selnes, Aarsland, Bjornerud, Gjerstad, Wallin, Hessen, Reinvang, Grambaite, Auning, Kjaervik, Due-Tonnessen, Stenset and Fladby2013; Rongve et al., Reference Rongve, Soennesyn, Skogseth, Oesterhus, Hortobagyi, Ballard, Auestad and Aarsland2016; Fladby et al., Reference Fladby, Palhaugen, Selnes, Waterloo, Brathen, Hessen, Almdahl, Arntzen, Auning, Eliassen, Espenes, Grambaite, Grontvedt, Johansen, Johnsen, Kalheim, Kirsebom, Muller, Nakling, Rongve, Sando, Siafarikas, Stav, Tecelao, Timon, Bekkelund and Aarsland2017).

Our prior study separated SEV using an ultracentrifugation and OptiPrepTM (Sigma-Aldrich, UK) density gradient approach based on their buoyant density. Size distribution and concentration of separated EVs were verified using the Malvern NanoSight LM10 nanoparticle analyser (Malvern Instruments Ltd., UK). Separation of SEVs was confirmed by Western blotting using antibodies against Flotillin-1 and CD63. Total RNA was isolated from SEV, and RNA samples were sequenced using the Illumina Hiseq-2500. We identified DEG using a previously experimentally validated edgeR algorithm (Anders et al., Reference Anders, Mccarthy, Chen, Okoniewski, Smyth, Huber and Robinson2013; Rajkumar et al., Reference Rajkumar, Qvist, Lazarus, Lescai, Ju, Nyegaard, Mors, Borglum, Li and Christensen2015). Further details of this study have been published elsewhere (Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021). Those RNA-Seq raw data files are available at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA530121.

MiRNA target discovery

We first identified the mature miRNA sequences, transcribed by the six differentially expressed miRNA genes (MIR26A1, MIR320C2, MIR320D2, MIR548BA, MIR556, and MIR4722) in DLB serum SEV, using the miRbase database (Liu et al., Reference Liu, Li and Cairns2014). ‘MiRbase’ is a comprehensive database for miRNA sequences and annotations (Kozomara et al., Reference Kozomara, Birgaoanu and Griffiths-Jones2019). Expression of these six miRNA genes leads to nine mature miRNA sequences (hsa-miR-26a-5p, hsa-miR-26a-1-3p, hsa-miR-320c, hsa-miR-320d, hsa-mir-548ba, hsa-miR-556-5p, hsa-miR-556-3p, hsa-miR-4722-5p, and hsa-miR-4722-3p). We next identified potential targets of these miRNA using the MiRDB miRNA target prediction database (Liu and Wang, Reference Liu and Wang2019). MiRDB analyses miRNA-target interactions using MirTarget2 bioinformatics tool and data derived from high-throughput RNA sequencing experiments (Liu and Wang, Reference Liu and Wang2019; Chen and Wang, Reference Chen and Wang2020). miRTarget2 can perform genome-wide miRNA target prediction using support vector machine framework (Benson et al., Reference Benson, Karsch-Mizrachi, Lipman, Ostell and Wheeler2007; Maglott et al., Reference Maglott, Ostell, Pruitt and Tatusova2007; Wang and El Naqa, Reference Wang and El Naqa2008; Chen et al., Reference Chen, Tan, Kou, Duan, Wang, Meirelles, Clark and Ma’ayan2013). We combined all potential target genes of these miRNA and removed duplicates (Supplementary information-1).

Functional enrichment analyses

We employed the EnrichR functional enrichment analysis tool and identified the biological processes, molecular pathways, and disease processes that were significantly enriched among the potential target genes of these miRNA (Chen et al., Reference Chen, Tan, Kou, Duan, Wang, Meirelles, Clark and Ma’ayan2013; Kuleshov et al., Reference Kuleshov, Jones, Rouillard, Fernandez, Duan, Wang, Koplev, Jenkins, Jagodnik, Lachmann, Mcdermott, Monteiro, Gundersen and Ma’ayan2016). We repeated the EnrichR analyses for understanding the functional implications of the potential target genes of each miRNA gene of interest individually. EnrichR considers at least 204 gene set libraries that are constructed from prior published studies and major biological and biomedical online databases including BioCarta, the database of Genotypes and Phenotypes (dbGaP), DisGeNET (Pinero et al., Reference Pinero, Sauch, Sanz and Furlong2021), GeneSigDB (Culhane et al., Reference Culhane, Schroder, Sultana, Picard, Martinelli, Kelly, Haibe-Kains, Kapushesky, St Pierre, Flahive, Picard, Gusenleitner, Papenhausen, O’connor, Correll and Quackenbush2012), Online Mendelian Inheritance in Man (OMIM), and UK Biobank genome-wide association study EnrichR includes Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway information (Kanehisa and Goto, Reference Kanehisa and Goto2000; Kanehisa et al., Reference Kanehisa, Furumichi, Sato, Ishiguro-Watanabe and Tanabe2021). EnrichR calculates its p-values using Fisher’s exact test and employs appropriate corrections for multiple testing using the Benjamini-Hochberg false discovery rate (FDR) correction at 5% (q-values) (Benjamini and Hochberg, Reference Benjamini and Hochberg1995).

Protein interactions of potential target genes

Interactome implies the total molecular interactions that occur within a cell (Yan et al., Reference Yan, Nagle, Zhou, Zhang and Zhang2018), and interactome analyses map these networks. Interactome analyses help improving our understanding of molecular pathways involved in neurodegenerative diseases (Haenig et al., Reference Haenig, Atias, Taylor, Mazza, Schaefer, Russ, Riechers, Jain, Coughlin, Fontaine, Freibaum, Brusendorf, Zenkner, Porras, Stroedicke, Schnoegl, Arnsburg, Boeddrich, Pigazzini, Heutink, Taylor, Kirstein, Andrade-Navarro, Sharan and Wanker2020). First, we identified significantly enriched molecular interaction pathways of the potential miRNA target genes using the Reactome pathway database (Fabregat et al., Reference Fabregat, Sidiropoulos, Viteri, Forner, Marin-Garcia, Arnau, D’eustachio, Stein and Hermjakob2017). We then expanded the Reactome enrichment analyses including all relevant protein-protein interactions from the IntAct protein interaction analysis database (Hermjakob et al., Reference Hermjakob, Montecchi-Palazzi, Lewington, Mudali, Kerrien, Orchard, Vingron, Roechert, Roepstorff, Valencia, Margalit, Armstrong, Bairoch, Cesareni, Sherman and Apweiler2004). Reactome calculates its p-values using over-representation analysis and hypergeometric distribution. It employs the Benjamini-Hochberg FDR correction at 5% (Benjamini and Hochberg, Reference Benjamini and Hochberg1995).

Results

Biological processes

We identified 4278 potential target genes of these miRNA (Supplementary information-1). These potential target genes significantly (Fisher’s exact test p < 0.0001) overlapped with the previously reported post-mortem DLB brain DEG (Rajkumar et al., Reference Rajkumar, Bidkhori, Shoaie, Clarke, Morrin, Hye, Williams, Ballard, Francis and Aarsland2020) and with serum SEV DEG in people living with DLB (Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021). The potential target genes were significantly (q < 0.05) enriched among the genes associated with 62 biological processes after Benjamini-Hochberg FDR correction at 5% (Table 1; Supplementary information-2). The enriched biological processes clustered around communication between cells, post-translational protein modification, apoptosis, neuronal development, transcriptional regulation, regulation of cell cycle, autophagy lysosomal pathway, vesicle-mediated transport, plasma membrane organisation, and cellular macromolecule biosynthesis. The potential target genes were significantly enriched among the genes associated with several transcriptional regulation processes including regulation of transcription from RNA polymerase-II promoter (q = 7.31 × 10−8), negative regulation of gene expression (q = 1.50 × 10−6), regulation of DNA-templated transcription (q = 1.55 × 10−6), and regulation of gene expression (q = 9.80 × 10−5). Significantly enriched biological processes included apoptosis-associated regulation of apoptotic process (q = 1.59 × 10−5), negative regulation of programmed cell death (q = 5.30 × 10−4) and negative regulation of apoptotic process (q = 0.0017), as well as autophagy lysosomal pathway associated lysosomal transport (q = 0.0196) and negative regulation of autophagy (q = 0.0065) processes. Supplementary information-2 provides further details of all 62 significantly enriched biological processes with their gene ontology (GO) numbers, p-values, FDR-adjusted q-values, odds ratios, and overlapping genes.

Table 1. Biological processes that were enriched among the potential target genes of differentially expressed small extracellular vesicle miRNA in people with dementia with Lewy bodies

GO: Gene Ontology.

* Benjamini-Hochberg false discovery rate at 5%.

Molecular functions

The potential target genes of these miRNA were significantly enriched among the genes associated with 35 molecular functions after Benjamini-Hochberg FDR correction at 5% (Supplementary information-2). These enriched molecular functions clustered around transcriptional regulation, protein phosphorylation, ubiquitin protease system (UPS), and histone acetylation. The potential target genes were significantly enriched among the genes associated with several molecular functions regulating transcription such as transcription coactivator activity (q = 3.34 × 10−5), transcription regulatory region DNA binding (q = 0.0024), RNA polymerase-II core promoter proximal region sequence-specific binding (q = 0.0240) transcription factor (q = 0.0124) and transcriptional activator (q = 0.0139) activities, activating transcription factor binding (q = 0.0134), RNA polymerase-II transcription cofactor activity (q = 0.0329), and transcription corepressor activity (q = 0.0439). The potential SEV miRNA target genes were significantly enriched among the genes involved in the UPS-related molecular functions, ubiquitin-protein transferase activity (q = 0.0035), and ubiquitin-protein ligase activity (q = 0.0342), that have been known to be associated with DLB pathology (Chowdhury and Rajkumar, Reference Chowdhury and Rajkumar2020). Significantly enriched molecular functions included histone acetyltransferase activity (q = 0.0188) and several protein phosphorylation regulating functions such as protein serine/threonine kinase activity (q = 8.65 × 10−7), protein kinase activity (q = 3.17 × 10−6), protein kinase binding (q = 4.30 × 10−4), and protein kinase A regulatory subunit binding (q = 0.0044). Supplementary information-2 provides further details of all 35 significantly enriched molecular functions with their GO numbers, p-values, FDR-adjusted q-values, odds ratios, and overlapping genes.

Enriched molecular pathways

The potential target genes were significantly enriched for 59 KEGG pathways after Benjamini-Hochberg FDR correction at 5% (Table 2; Supplementary information-2). These enriched molecular pathways clustered around neuronal development, cell signalling, synaptic activity, programmed cell death, autophagy, UPS, and vesicle-mediated transport. They included axon guidance (q = 1.69 × 10−6), focal adhesion (q = 5.08 × 10−4), autophagy (q = 8.78 × 10−4), mRNA surveillance (q = 0.0016), long-term potentiation (q = 0.0053), ubiquitin-mediated proteolysis (q = 0.0283), cholinergic (q = 0.0493) and glutaminergic (q = 0.0283) synapses, ferroptosis (q = 0.0350), endocytosis (q = 0.0493), as well as Wnt (q = 3.22 × 10−4), mTOR (q = 5.08 × 10−4), ErbB (q = 0.0018), FoxO (q = 0.0025), and neurotrophin (q = 0.0076) signalling pathways. Further details of all 59 significantly enriched KEGG pathways with their pathway (hsa) numbers, p-values, FDR-adjusted q-values, odds ratios, and overlapping genes are presented in the supplementary information-2.

Table 2. Molecular pathways that were enriched among the potential target genes of differentially expressed small extracellular vesicle miRNA in people with dementia with Lewy bodies

KEGG: Kyoto Encyclopaedia of Genes and Genomes; hsa: Homo sapiens.

* Benjamini-Hochberg false discovery rate at 5%.

Disease processes

We assessed how the potential target genes were related to the genes that are known to be associated with specific disease processes in OMIM and DisGeNET (Pinero et al., Reference Pinero, Sauch, Sanz and Furlong2021) databases. The potential target genes were significantly enriched among the genes that are associated with 74 disease processes in the DisGeNET after Benjamini-Hochberg FDR correction at 5% (Supplementary information-2). These enriched disease processes included several neurodevelopmental disorders such as intellectual disability (q = 7.58 × 10−8), delayed speech and language development (q = 1.47 × 10−5), and autism (q = 1.47 × 10−5), as well as neuropsychiatric disorders such as schizophrenia (q = 7.23 × 10−4), speech impairment (q = 6.37 × 10−5), epilepsy (q = 7.49 × 10−4), cognitive disorders (q = 0.0192), bipolar disorder (q = 0.0353), and impaired cognition (q = 0.0446). Besides, the potential target genes were significantly (p < 0.05) enriched only among the genes associated with encephalopathy (p = 0.0037) in OMIM database, and this enrichment was not statistically significant after FDR correction (Supplementary information-2).

Protein interactions

We assessed the protein interaction pathways of the potential miRNA target genes using the Reactome pathway database (Fabregat et al., Reference Fabregat, Sidiropoulos, Viteri, Forner, Marin-Garcia, Arnau, D’eustachio, Stein and Hermjakob2017). The potential target genes were significantly (p < 0.05) overrepresented among 13 protein interaction pathways, and none of these associations were significant after FDR correction (Table 3; Supplementary information-2). These 13 protein interaction pathways indicated that the protein interactions of the potential target genes may contribute to DLB pathology by regulating transcription, cell cycle, signal transduction, and interleukin-6 signalling. Moreover, the potential target genes were significantly (p < 0.05) overrepresented among four protein interaction pathways after considering protein-protein interactions, and none of these associations were significant after FDR correction (Table 3; Supplementary information-2). These four protein interaction pathways indicated the possibility of the potential target genes influencing cell cycle, post-transcriptional silencing by small RNA, and phosphorylation of β-catenin that is one of the core molecules in the Wnt signalling pathway.

Table 3. Protein interaction pathways* that were enriched among the potential target genes of differentially expressed small extracellular vesicle miRNA in people with dementia with Lewy bodies

* Derived from Reactome pathway database (https://reactome.org/).

Secondary analyses of target genes of individual miRNA

We repeated the functional enrichment and protein interaction analyses for the potential target genes of each of the six miRNA genes individually. These secondary analyses identified additional significantly (q < 0.05) enriched biological processes, molecular functions, and KEGG pathways, as well as nominally significant (p < 0.05) protein interaction pathways. Supplementary information-3 presents these additional findings. The potential target genes of hsa-miR-26a-5p and hsa-miR-26a-1-3p were significantly enriched among the genes associated with signal transduction, post-translational protein modifications, nucleotide binding, regulation of cellular catabolic process (q = 0.0335), thiol-dependent ubiquitinyl hydrolase activity (q = 0.0085), ubiquitin-like protein ligase activity (q = 0.0461), and RNA polymerase-II distal enhancer sequence-specific binding transcription factor activity (q = 0.0461). The potential target genes of hsa-miR-320d were significantly enriched among the genes involved in longevity regulating pathway (q = 0.0019) and transcriptional misregulation in cancer (q = 0.0451). They were significantly (p < 0.05) overrepresented among nuclear receptor transcription (p = 8.99 × 10−5) and Cohesin loading onto chromatin (p = 0.0464) protein interaction pathways. Moreover, the potential target genes of hsa-mir-548ba were significantly enriched for ubiquitin-like protein ligase activity (q = 0.019) and protein carboxyl O-methyltransferase activity (q = 0.0359). Their protein interactions were significantly (p < 0.05) overrepresented among 19 protein interaction pathways including activation of G-protein gated potassium channels (p = 0.0034), inhibition of voltage-gated Ca2+ channels via Gbeta/gamma subunits (p = 0.0034), GABA receptor activation (p = 0.0132), GABA-B receptor activation (p = 0.0267), RNA Polymerase-III transcription initiation from type-1 (p = 0.0410) and type-2 (p = 0.0367) promoters, and GRB2 events in ERBB2 signalling (p = 0.0397). Furthermore, functional enrichment and protein interaction analyses for the potential target genes of hsa-miR-556-5p and hsa-miR-556-3p did not reveal any additional significantly (q < 0.05) enriched biological processes, molecular functions, and KEGG pathways. Their protein interactions were significantly (p < 0.05) overrepresented among seven protein interaction pathways including FGFR2 ligand binding and activation (p = 0.0068) and voltage-gated potassium channels (p = 0.0094) (Supplementary information-3).

Discussion

This is the first study that systematically investigated the functional implications of differentially expressed miRNA in serum SEV from people with DLB. It adds evidence supporting the hypothesis that differentially expressed SEV miRNA may contribute to DLB molecular pathogenesis by widespread transcriptional regulation (Gamez-Valero et al., Reference Gamez-Valero, Campdelacreu, Vilas, Ispierto, Rene, Alvarez, Armengol, Borras and Beyer2019; Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021). The potential target genes of the differentially expressed SEV miRNA were significantly enriched among the genes involved in post-translational protein modifications (Manzanza et al., Reference Manzanza, Sedlackova and Kalaria2021), transcriptional regulation (Chowdhury and Rajkumar, Reference Chowdhury and Rajkumar2020; Feleke et al., Reference Feleke, Reynolds, Smith, Tilley, SaG, Hardy, Matthews, Gentleman, Owen, Johnson, Srivastava and Ryten2021), autophagy lysosomal pathway (Crews et al., Reference Crews, Spencer, Desplats, Patrick, Paulino, Rockenstein, Hansen, Adame, Galasko and Masliah2010; Arotcarena et al., Reference Arotcarena, Teil and Dehay2019), UPS (Bedford et al., Reference Bedford, Hay, Paine, Rezvani, Mee, Lowe and Mayer2008; Zheng et al., Reference Zheng, Huang, Zhang, Zhou, Luo, Xu and Wang2016), apoptosis (Mahul-Mellier et al., Reference Mahul-Mellier, Burtscher, Maharjan, Weerens, Croisier, Kuttler, Leleu, Knott and Lashuel2020), regulation of cell cycle (Lee et al., Reference Lee, Kim, Junn, Lee, Park, Tanaka, Ronchetti, Quezado and Mouradian2003), histone acetylation (Urbizu and Beyer, Reference Urbizu and Beyer2020), cell signalling (Beyer et al., Reference Beyer, Domingo-Sabat and Ariza2009; Garcia-Esparcia et al., Reference Garcia-Esparcia, Lopez-Gonzalez, Grau-Rivera, Garcia-Garrido, Konetti, Llorens, Zafar, Carmona, Del Rio, Zerr, Gelpi and Ferrer2017), synaptic dysfunction (Overk and Masliah, Reference Overk and Masliah2014), and vesicle-mediated transport (Kurzawa-Akanbi et al., Reference Kurzawa-Akanbi, Tammireddy, Fabrik, Gliaudelyte, Doherty, Heap, Matecko-Burmann, Burmann, Trost, Lucocq, Gherman, Fairfoul, Singh, Burte, Green, Mckeith, Hartlova, Whitfield and Morris2021; Longobardi et al., Reference Longobardi, Nicsanu, Bellini, Squitti, Catania, Tiraboschi, Saraceno, Ferrari, Zanardini, Binetti, Di Fede, Benussi and Ghidoni2022) that have been associated with DLB pathology. Strengths of this study include employing appropriate FDR corrections, focusing on differentially expressed SEV miRNA that were either differentially expressed in both post-mortem DLB brains and serum SEV or replicated by high-throughput qPCR, and considering protein interactions. However, there were limitations including the methodological difficulties in miRNA target prediction (Riolo et al., Reference Riolo, Cantara, Marzocchi and Ricci2020), limited information regarding potential targets of other differentially expressed SEV small RNA in DLB, and investigating miRNA that were identified in total serum SEV population that was not enriched for neuronal origin (Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021).

Several lines of evidence indicate that post-mortem brain and CSF-derived EV carry pathogenic cargo that are capable of inducing alpha-synuclein oligomerisation and propagating Lewy pathology (Danzer et al., Reference Danzer, Kranich, Ruf, Cagsal-Getkin, Winslow, Zhu, Vanderburg and Mclean2012; Stuendl et al., Reference Stuendl, Kunadt, Kruse, Bartels, Moebius, Danzer, Mollenhauer and Schneider2016; Ngolab et al., Reference Ngolab, Trinh, Rockenstein, Mante, Florio, Trejo, Masliah, Adame, Masliah and Rissman2017; Kurzawa-Akanbi et al., Reference Kurzawa-Akanbi, Tammireddy, Fabrik, Gliaudelyte, Doherty, Heap, Matecko-Burmann, Burmann, Trost, Lucocq, Gherman, Fairfoul, Singh, Burte, Green, Mckeith, Hartlova, Whitfield and Morris2021; Herman et al., Reference Herman, Djaldetti, Mollenhauer and Offen2022). However, the molecular mechanisms by which the pathogenic EV propagate Lewy pathology remain uncertain (Choi et al., Reference Choi, Park and Park2021). SEV miRNA, derived from people with AD or Parkinson’s disease (PD), have been shown to contribute towards neurodegeneration by transcriptional regulation of genes involved in neuroinflammation and autophagy (Vassileff et al., Reference Vassileff, Cheng and Hill2020; Mavroeidi et al., Reference Mavroeidi, Vetsi, Dionysopoulou and Xilouri2022). Current evidence supporting the uptake of SEV miRNA by cells and their transcriptional regulation in recipient cells (Hu et al., Reference Hu, Drescher and Chen2012; Li et al., Reference Li, Cao, Sun and Feng2021; Mustajab et al., Reference Mustajab, Kwamboka, Choi, Kang, Kim, Han, Han, Lee, Song and Chwae2022) outweigh the evidence to the contrary (Albanese et al., Reference Albanese, Chen, Huls, Gartner, Tagawa, Mejias-Perez, Keppler, Gobel, Zeidler, Shein, Schutz and Hammerschmidt2021). Moreover, several miRNA are known to regulate alpha-synuclein levels, functions and oligomerisation in people with PD (Zhao and Wang, Reference Zhao and Wang2019), and pertinent research involving people with DLB remains sparse. Such prior evidence and the findings of this study highlight the need for further experiments investigating the importance of SEV miRNA in the molecular pathogenesis of DLB.

MIR26A1, also known as MIR26A, is well known to regulate neuronal development and synaptic plasticity (Li and Sun, Reference Li and Sun2013). hsa-miR-26a-5p has been included in the blood-based 12 miRNA signature panel for AD (Leidinger et al., Reference Leidinger, Backes, Deutscher, Schmitt, Mueller, Frese, Haas, Ruprecht, Paul, Stahler, Lang, Meder, Bartfai, Meese and Keller2013), and its expression level has been found to be significantly higher in people with frontotemporal dementia, when compared to people without dementia (Martinez and Peplow, Reference Martinez and Peplow2022). The diagnostic biomarker potential of hsa-miR-26a-5p in DLB (Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021) warrants further investigation. Moreover, this study has identified PTGS2 encoding cyclooxygenase-2 as one of the potential target genes of hsa-miR-26a-5p. Dual-luciferase and qPCR experiments have confirmed that hsa-miR-26a-5p directly targets and regulates transcription of PTGS2 (Xie et al., Reference Xie, Pei, Shan, Xiao, Zhou, Huang and Wang2022). Transcriptional regulation of PTGS2 by hsa-miR-26a-5p has been reported to lead to proliferation of AD pathology (Xie et al., Reference Xie, Pei, Shan, Xiao, Zhou, Huang and Wang2022), and further investigation investigating the contribution of this miRNA-mRNA pair in DLB pathology is needed.

Expression level of MIR320C2 is reportedly significantly higher in plasma of people with AD (Nagaraj et al., Reference Nagaraj, Laskowska-Kaszub, Debski, Wojsiat, Dabrowski, Gabryelewicz, Kuznicki and Wojda2017), and it has been found to be significantly downregulated in post-mortem PD brains (Briggs et al., Reference Briggs, Wang, Kong, Woo, Iyer and Sonntag2015) as well as post-mortem DLB brains and serum SEV from people living with DLB (Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021). This supports the need for investigating the biomarker potential of hsa-miR-320c for differentiating DLB from AD in large cohorts. Similarly, MIR320D2 has been found to be significantly upregulated in plasma of people with PD (Chen et al., Reference Chen, Deng, Lu, Liao, Long, Gou, Bi and Zhou2021), and it was significantly downregulated in post-mortem DLB brains and serum SEV from people with DLB (Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021). hsa-miR-320d may help differentiating DLB from PD dementia. Furthermore, hsa-miR-4722-5p is likely to regulate genes involved in signal transduction and autophagy (Liu et al., Reference Liu, Xu and Yu2022). It has been reported to be significantly higher in serum of people with AD and has been identified as a potential blood-based biomarker for AD (Soleimani Zakeri et al., Reference Soleimani Zakeri, Pashazadeh and Motieghader2020; Liu et al., Reference Liu, Xu and Yu2022). MIR4722 was significantly downregulated in post-mortem DLB brains (Rajkumar et al., Reference Rajkumar, Bidkhori, Shoaie, Clarke, Morrin, Hye, Williams, Ballard, Francis and Aarsland2020) as well as serum SEV from people with DLB (Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021), and this may contribute to the impairment of autophagy lysosomal pathway in DLB (Crews et al., Reference Crews, Spencer, Desplats, Patrick, Paulino, Rockenstein, Hansen, Adame, Galasko and Masliah2010; Arotcarena et al., Reference Arotcarena, Teil and Dehay2019). Diagnostic biomarker and therapeutic potential of hsa-miR-4722-5p in DLB need further evaluation.

The potential target genes of the investigated SEV miRNA in DLB include UBE3A, UBE3B, and 11 ubiquitin-conjugating enzyme genes. The potential target genes were significantly enriched among the genes involved in ubiquitin-protein transferase activity, ubiquitin-protein ligase activity, ubiquitin-mediated proteolysis, lysosomal transport, and negative regulation of autophagy. Dysfunction of autophagy lysosomal pathway and/or UPS leads to accumulation of pathologically misfolded proteins and progression of Lewy pathology (Zheng et al., Reference Zheng, Huang, Zhang, Zhou, Luo, Xu and Wang2016). Recent transcriptomic studies (Pietrzak et al., Reference Pietrzak, Papp, Curtis, Handelman, Kataki, Scharre, Rempala and Sadee2016; Nelson et al., Reference Nelson, Wang, Janse and Thompson2018; Santpere et al., Reference Santpere, Garcia-Esparcia, Andres-Benito, Lorente-Galdos, Navarro and Ferrer2018; Rajkumar et al., Reference Rajkumar, Bidkhori, Shoaie, Clarke, Morrin, Hye, Williams, Ballard, Francis and Aarsland2020) and a systematic review (Chowdhury and Rajkumar, Reference Chowdhury and Rajkumar2020) have reported statistically significant downregulation of several autophagy lysosomal pathway and UPS-associated genes in post-mortem DLB brains. Statistically significant downregulation of UPS-associated genes, UBE3A, USP47, and PSMD4, and of the protein ubiquitination pathway in serum SEV from people with DLB have been reported (Rajkumar et al., Reference Rajkumar, Hye, Lange, Manesh, Ballard, Fladby and Aarsland2021). The UPS closely interacts with the autophagy lysosomal pathway, and their dysfunction was shown to be sufficient for causing Lewy body-like inclusions in mice models (Zheng et al., Reference Zheng, Li and Wang2009). Their dysfunction leads to cytoplasmic accumulation of alpha-synuclein and other misfolded proteins that can set off a vicious cycle by inhibiting neuronal lysosomal activity further (Mazzulli et al., Reference Mazzulli, Xu, Sun, Knight, McLean, Caldwell, Sidransky, Grabowski and Krainc2011). Moreover, our findings showed that the potential target genes were likely to regulate transcription of genes involved in protein phosphorylation and post-translational protein modifications. Phosphorylation and other post-translational modifications of alpha-synuclein contribute substantially towards molecular pathogenesis of DLB (Outeiro et al., Reference Outeiro, Koss, Erskine, Walker, Kurzawa-Akanbi, Burn, Donaghy, Morris, Taylor, Thomas, Attems and Mckeith2019; Manzanza et al., Reference Manzanza, Sedlackova and Kalaria2021). Additionally, the potential target genes may play a role in DLB pathogenesis by impacting Wnt signalling pathway and associated β-catenin phosphorylation cascade that regulate microglia-mediated neuroinflammation and synaptic plasticity (Rajkumar et al., Reference Rajkumar, Bidkhori, Shoaie, Clarke, Morrin, Hye, Williams, Ballard, Francis and Aarsland2020; Yang and Zhang, Reference Yang and Zhang2020).

Our findings support the biological plausibility of the differentially expressed SEV miRNA contributing towards DLB pathogenesis by transcriptional regulation of other genes. Therapeutic potential of these differentially expressed SEV miRNA and their target genes are warranted. Further experiments are required for verifying predicted miRNA targets as well as identified dysfunctional molecular processes and pathways. The potential regulation of post-translational modifications by SEV miRNA-mediated transcription, which directly act upon alpha-synuclein and other proteins and their functions, provides additional layers of regulatory cross-talk that could be targeted for therapeutic benefit. Moreover, SEV miRNA have opened a novel avenue for identifying blood-based diagnostic biomarkers for DLB. There is an urgent need for an adequately powered blood-based SEV RNA sequencing study that is not biased by post-transcriptional RNA modifications (Shi et al., Reference Shi, Zhang, Tan, Zhang, Yan, Zhang, Franklin, Shahbazi, Mackinlay, Liu, Kuhle, James, Zhang, Qu, Zhai, Zhao, Zhao, Zhou, Gu, Murn, Guo, Carrell, Wang, Chen, Cairns, Yang, Schimmel, Zernicka-Goetz, Cheloufi, Zhang, Zhou and Chen2021). Discovered potential diagnostic biomarker miRNA and other small RNA should be evaluated in large replication cohorts. As gene expression changes often differ with disease progression, measuring the expression levels of potential diagnostic biomarkers at various clinical stages of DLB is preferred. Besides, investigating SEV that are enriched for neuronal (Mustapic et al., Reference Mustapic, Eitan, Werner, Berkowitz, Lazaropoulos, Tran, Goetzl and Kapogiannis2017) and/or microglial (Winston et al., Reference Winston, Sarsoza, Spencer and Rissman2021) origin by immunoprecipitation may facilitate the discovery of novel diagnostic biomarkers and therapeutic targets for DLB.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/neu.2023.27.

Authors’ contribution

APR conceived this study, and all authors were involved in designing the study. APR identified the differentially expressed serum SEV miRNA in DLB. FBI performed all bioinformatic analyses and wrote the initial draft. All authors were involved in interpretation of the results and further critical revisions of the manuscript. All authors have approved the final version of the manuscript.

Financial support

This research was supported by a Ph.D. studentship, funded by the Republic of Turkey Ministry for National Education.

Competing interests

All authors declare that they do not have any competing interests.

References

Albanese, M, Chen, YA, Huls, C, Gartner, K, Tagawa, T, Mejias-Perez, E, Keppler, OT, Gobel, C, Zeidler, R, Shein, M, Schutz, AK and Hammerschmidt, W (2021) MicroRNAs are minor constituents of extracellular vesicles that are rarely delivered to target cells. PLOS Genetics 17, e1009951.CrossRefGoogle ScholarPubMed
Anders, S, Mccarthy, DJ, Chen, Y, Okoniewski, M, Smyth, GK, Huber, W and Robinson, MD (2013) Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nature Protocols 8, 1765–1786.CrossRefGoogle ScholarPubMed
Arotcarena, ML, Teil, M and Dehay, B (2019) Autophagy in synucleinopathy: the overwhelmed and defective machinery. Cells 8, 565.CrossRefGoogle ScholarPubMed
Ashton, NJ, Hye, A, Rajkumar, AP, Leuzy, A, Snowden, S, Suarez-Calvet, M, Karikari, TK, Scholl, M, La Joie, R, Rabinovici, GD, Hoglund, K, Ballard, C, Hortobagyi, T, Svenningsson, P, Blennow, K, Zetterberg, H and Aarsland, D (2020) An update on blood-based biomarkers for non-Alzheimer neurodegenerative disorders. Nature reviews. Neurology 16, 265–284.CrossRefGoogle ScholarPubMed
Bedford, L, Hay, D, Paine, S, Rezvani, N, Mee, M, Lowe, J and Mayer, RJ (2008) Is malfunction of the ubiquitin proteasome system the primary cause of alpha-synucleinopathies and other chronic human neurodegenerative disease? Biochimica et Biophysica Acta 1782, 683–690.CrossRefGoogle ScholarPubMed
Benjamini, Y and Hochberg, Y (1995) Controlling the false discovery rate - a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B-Statistical Methodology 57, 289–300.Google Scholar
Benson, DA, Karsch-Mizrachi, I, Lipman, DJ, Ostell, J and Wheeler, DL (2007) GenBank. Nucleic Acids Research 35, D21–D25.CrossRefGoogle Scholar
Beyer, K, Domingo-Sabat, M and Ariza, A (2009) Molecular pathology of Lewy body diseases. International Journal of Molecular Sciences 10, 724–745.CrossRefGoogle ScholarPubMed
Bostrom, F, Jonsson, L, Minthon, L and Londos, E (2007) Patients with dementia with lewy bodies have more impaired quality of life than patients with Alzheimer disease. Alzheimer Disease & Associated Disorders 21, 150–154.CrossRefGoogle ScholarPubMed
Briggs, CE, Wang, Y, Kong, B, Woo, TU, Iyer, LK and Sonntag, KC (2015) Midbrain dopamine neurons in Parkinson’s disease exhibit a dysregulated miRNA and target-gene network. Brain Research 1618, 111–121.CrossRefGoogle ScholarPubMed
Candelario, KM and Steindler, DA (2014) The role of extracellular vesicles in the progression of neurodegenerative disease and cancer. Trends in Molecular Medicine 20, 368–374.CrossRefGoogle ScholarPubMed
Chen, EY, Tan, CM, Kou, Y, Duan, Q, Wang, Z, Meirelles, GV, Clark, NR and Ma’ayan, A (2013) Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14(1), 128.CrossRefGoogle ScholarPubMed
Chen, Q, Deng, N, Lu, K, Liao, Q, Long, X, Gou, D, Bi, F and Zhou, J (2021) Elevated plasma miR-133b and miR-221-3p as biomarkers for early Parkinson’s disease. Scientific Reports 11, 15268.CrossRefGoogle ScholarPubMed
Chen, Y and Wang, X (2020) miRDB: an online database for prediction of functional microRNA targets. Nucleic acids research 48, D127–D131.CrossRefGoogle ScholarPubMed
Choi, YR, Park, SJ and Park, SM (2021) Molecular events underlying the cell-to-cell transmission of alpha-synuclein. The FEBS Journal 288, 6593–6602.CrossRefGoogle ScholarPubMed
Chowdhury, A and Rajkumar, AP (2020) Systematic review of gene expression studies in people with Lewy body dementia. Acta Neuropsychiatrica 32, 281–292.CrossRefGoogle ScholarPubMed
Crews, L, Spencer, B, Desplats, P, Patrick, C, Paulino, A, Rockenstein, E, Hansen, L, Adame, A, Galasko, D, Masliah, E (2010) Selective molecular alterations in the autophagy pathway in patients with Lewy body disease and in models of alpha-synucleinopathy. PLoS One 5, e9313.CrossRefGoogle ScholarPubMed
Culhane, AC, Schroder, MS, Sultana, R, Picard, SC, Martinelli, EN, Kelly, C, Haibe-Kains, B, Kapushesky, M, St Pierre, AA, Flahive, W, Picard, KC, Gusenleitner, D, Papenhausen, G, O’connor, N, Correll, M and Quackenbush, J (2012) GeneSigDB: a manually curated database and resource for analysis of gene expression signatures. Nucleic Acids Research 40, D1060–D1066.CrossRefGoogle ScholarPubMed
Danzer, KM, Kranich, LR, Ruf, WP, Cagsal-Getkin, O, Winslow, AR, Zhu, L, Vanderburg, CR and Mclean, PJ (2012) Exosomal cell-to-cell transmission of alpha synuclein oligomers. Molecular Neurodegeneration 7, 42.CrossRefGoogle ScholarPubMed
Du, Y, Yu, Y, Hu, Y, Li, XW, Wei, ZX, Pan, RY, Li, XS, Zheng, GE, Qin, XY, Liu, QS, Cheng, Y (2019) Genome-wide, integrative analysis implicates exosome-derived microRNA dysregulation in schizophrenia. Schizophrenia Bulletin 45, 1257–1266.CrossRefGoogle ScholarPubMed
Fabregat, A, Sidiropoulos, K, Viteri, G, Forner, O, Marin-Garcia, P, Arnau, V, D’eustachio, P, Stein, L and Hermjakob, H (2017) Reactome pathway analysis: a high-performance in-memory approach. BMC Bioinformatics 18(1), 142.CrossRefGoogle ScholarPubMed
Feleke, R, Reynolds, RH, Smith, AM, Tilley, B, SaG, Taliun, Hardy, J, Matthews, PM, Gentleman, S, Owen, DR, Johnson, MR, Srivastava, PK and Ryten, M (2021) Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy body diseases. Acta Neuropathologica 142, 449–474.CrossRefGoogle ScholarPubMed
Fladby, T, Palhaugen, L, Selnes, P, Waterloo, K, Brathen, G, Hessen, E, Almdahl, IS, Arntzen, KA, Auning, E, Eliassen, CF, Espenes, R, Grambaite, R, Grontvedt, GR, Johansen, KK, Johnsen, SH, Kalheim, LF, Kirsebom, BE, Muller, KI, Nakling, AE, Rongve, A, Sando, SB, Siafarikas, N, Stav, AL, Tecelao, S, Timon, S, Bekkelund, SI and Aarsland, D (2017) Detecting at-risk Alzheimer’s disease cases. Journal of Alzheimers Disease 60, 97–105.CrossRefGoogle ScholarPubMed
Freer, J (2017) UK lags far behind Europe on diagnosis of dementia with Lewy bodies. BMJ 358, j3319.CrossRefGoogle ScholarPubMed
Gamez-Valero, A, Campdelacreu, J, Vilas, D, Ispierto, L, Rene, R, Alvarez, R, Armengol, MP, Borras, FE and Beyer, K (2019) Exploratory study on microRNA profiles from plasma-derived extracellular vesicles in Alzheimer’s disease and dementia with Lewy bodies. Translational Neurodegeneration 8, 31.CrossRefGoogle Scholar
Garcia-Esparcia, P, Lopez-Gonzalez, I, Grau-Rivera, O, Garcia-Garrido, MF, Konetti, A, Llorens, F, Zafar, S, Carmona, M, Del Rio, JA, Zerr, I, Gelpi, E and Ferrer, I (2017) Dementia with Lewy bodies: molecular pathology in the frontal cortex in typical and rapidly progressive forms. Frontiers in Neurology 8, 89.CrossRefGoogle ScholarPubMed
Gruzdev, SK, Yakovlev, AA, Druzhkova, TA, Guekht, AB and Gulyaeva, NV (2019) The missing link: how exosomes and miRNAs can help in bridging psychiatry and molecular biology in the context of depression, bipolar disorder and schizophrenia. Cellular and Molecular Neurobiology 39, 729–750.CrossRefGoogle ScholarPubMed
Haenig, C, Atias, N, Taylor, AK, Mazza, A, Schaefer, MH, Russ, J, Riechers, SP, Jain, S, Coughlin, M, Fontaine, JF, Freibaum, BD, Brusendorf, L, Zenkner, M, Porras, P, Stroedicke, M, Schnoegl, S, Arnsburg, K, Boeddrich, A, Pigazzini, L, Heutink, P, Taylor, JP, Kirstein, J, Andrade-Navarro, MA, Sharan, R and Wanker, EE (2020) Interactome mapping provides a network of neurodegenerative disease proteins and uncovers widespread protein aggregation in affected brains. Cell Reports 32, 108050.CrossRefGoogle ScholarPubMed
Herman, S, Djaldetti, R, Mollenhauer, B and Offen, D (2022) CSF-derived extracellular vesicles from patients with Parkinson’s disease induce symptoms and pathology. Brain 146, 209–224.CrossRefGoogle Scholar
Hermjakob, H, Montecchi-Palazzi, L, Lewington, C, Mudali, S, Kerrien, S, Orchard, S, Vingron, M, Roechert, B, Roepstorff, P, Valencia, A, Margalit, H, Armstrong, J, Bairoch, A, Cesareni, G, Sherman, D and Apweiler, R (2004) IntAct: an open source molecular interaction database. Nucleic Acids Research 32, D452–D455.CrossRefGoogle ScholarPubMed
Hu, G, Drescher, KM and Chen, XM (2012) Exosomal miRNAs: biological properties and therapeutic potential. Frontiers in Genetics 3, 56.CrossRefGoogle ScholarPubMed
Ibrahim, SA and Khan, YS (2022) Histology, Extracellular Vesicles. Treasure Island, FL: StatPearls.Google Scholar
Kalluri, R and Lebleu, VS (2020) The biology, function, and biomedical applications of exosomes. Science 367, eaau6977.CrossRefGoogle ScholarPubMed
Kanehisa, M, Furumichi, M, Sato, Y, Ishiguro-Watanabe, M and Tanabe, M (2021) KEGG: integrating viruses and cellular organisms. Nucleic Acids Research 49, D545–D551.CrossRefGoogle ScholarPubMed
Kanehisa, M and Goto, S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research 28, 27–30.CrossRefGoogle Scholar
Kozomara, A, Birgaoanu, M and Griffiths-Jones, S (2019) miRBase: from microRNA sequences to function. Nucleic Acids Research 47, D155–D162.CrossRefGoogle Scholar
Kuleshov, MV, Jones, MR, Rouillard, AD, Fernandez, NF, Duan, Q, Wang, Z, Koplev, S, Jenkins, SL, Jagodnik, KM, Lachmann, A, Mcdermott, MG, Monteiro, CD, Gundersen, GW and Ma’ayan, A (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research 44, W90–W97.CrossRefGoogle ScholarPubMed
Kurzawa-Akanbi, M, Tammireddy, S, Fabrik, I, Gliaudelyte, L, Doherty, MK, Heap, R, Matecko-Burmann, I, Burmann, BM, Trost, M, Lucocq, JM, Gherman, AV, Fairfoul, G, Singh, P, Burte, F, Green, A, Mckeith, IG, Hartlova, A, Whitfield, PD and Morris, CM (2021) Altered ceramide metabolism is a feature in the extracellular vesicle-mediated spread of alpha-synuclein in Lewy body disorders. Acta Neuropathologica 142, 961–984.CrossRefGoogle ScholarPubMed
Lee, SS, Kim, YM, Junn, E, Lee, G, Park, KH, Tanaka, M, Ronchetti, RD, Quezado, MM and Mouradian, MM (2003) Cell cycle aberrations by alpha-synuclein over-expression and cyclin B immunoreactivity in Lewy bodies. Neurobiology of Aging 24(5), 687–696.CrossRefGoogle ScholarPubMed
Leidinger, P, Backes, C, Deutscher, S, Schmitt, K, Mueller, SC, Frese, K, Haas, J, Ruprecht, K, Paul, F, Stahler, C, Lang, CJ, Meder, B, Bartfai, T, Meese, E and Keller, A (2013) A blood based 12-miRNA signature of Alzheimer disease patients. Genome Biology 14, R78.CrossRefGoogle ScholarPubMed
Li, B, Cao, Y, Sun, M and Feng, H (2021) Expression, regulation, and function of exosome-derived miRNAs in cancer progression and therapy. The FASEB Journal 35, e21916.CrossRefGoogle ScholarPubMed
Li, B and Sun, H (2013) MiR-26a promotes neurite outgrowth by repressing PTEN expression. Molecular Medicine Reports 8, 676–680.CrossRefGoogle ScholarPubMed
Liu, B, Li, J and Cairns, MJ (2014) Identifying miRNAs, targets and functions. Briefings in Bioinformatics 15, 1–19.CrossRefGoogle ScholarPubMed
Liu, W and Wang, X (2019) Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biology 20, 18.CrossRefGoogle ScholarPubMed
Liu, Y, Xu, Y and Yu, M (2022) MicroRNA-4722-5p and microRNA-615-3p serve as potential biomarkers for Alzheimer’s disease. Experimental and Therapeutic Medicine 23, 241.CrossRefGoogle ScholarPubMed
Longobardi, A, Nicsanu, R, Bellini, S, Squitti, R, Catania, M, Tiraboschi, P, Saraceno, C, Ferrari, C, Zanardini, R, Binetti, G, Di Fede, G, Benussi, L and Ghidoni, R (2022) Cerebrospinal fluid EV concentration and size are altered in Alzheimer’s disease and dementia with Lewy bodies. Cells 11, 462.CrossRefGoogle ScholarPubMed
Lu, TX and Rothenberg, ME (2018) MicroRNA. Journal of Allergy and Clinical Immunology 141, 1202–1207.CrossRefGoogle ScholarPubMed
Maglott, D, Ostell, J, Pruitt, KD and Tatusova, T (2007) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Research 35, D26–D31.CrossRefGoogle ScholarPubMed
Mahul-Mellier, AL, Burtscher, J, Maharjan, N, Weerens, L, Croisier, M, Kuttler, F, Leleu, M, Knott, GW and Lashuel, HA (2020) The process of Lewy body formation, rather than simply alpha-synuclein fibrillization, is one of the major drivers of neurodegeneration. Proceedings of the National Academy of Sciences of the United States of America 117, 4971–4982.CrossRefGoogle ScholarPubMed
Manzanza, NO, Sedlackova, L and Kalaria, RN (2021) Alpha-synuclein post-translational modifications: implications for pathogenesis of Lewy body disorders. Frontiers in Aging Neuroscience 13, 690293.CrossRefGoogle ScholarPubMed
Martinez, B and Peplow, PV (2022) MicroRNA biomarkers in frontotemporal dementia and to distinguish from Alzheimer’s disease and amyotrophic lateral sclerosis. Neural Regeneration Research 17, 1412–1422.Google ScholarPubMed
Mavroeidi, P, Vetsi, M, Dionysopoulou, D and Xilouri, M (2022) Exosomes in alpha-synucleinopathies: propagators of pathology or potential candidates for nanotherapeutics? Biomolecules 12, 957.CrossRefGoogle ScholarPubMed
Mazzulli, JR, Xu, YH, Sun, Y, Knight, AL, McLean, PJ, Caldwell, GA, Sidransky, E, Grabowski, GA and Krainc, D (2011) Gaucher disease glucocerebrosidase and alpha-synuclein form a bidirectional pathogenic loop in synucleinopathies. Cell 146, 37–52.CrossRefGoogle Scholar
Mckeith, IG, Boeve, BF, Dickson, DW, Halliday, G, Taylor, JP, Weintraub, D, Aarsland, D, Galvin, J, Attems, J, Ballard, CG, Bayston, A, Beach, TG, Blanc, F, Bohnen, N, Bonanni, L, Bras, J, Brundin, P, Burn, D, Chen-Plotkin, A, Duda, JE, El-Agnaf, O, Feldman, H, Ferman, TJ, Ffytche, D, Fujishiro, H, Galasko, D, Goldman, JG, Gomperts, SN, Graff-Radford, NR, Honig, LS, Iranzo, A, Kantarci, K, Kaufer, D, Kukull, W, Lee, VMY, Leverenz, JB, Lewis, S, Lippa, C, Lunde, A, Masellis, M, Masliah, E, Mclean, P, Mollenhauer, B, Montine, TJ, Moreno, E, Mori, E, Murray, M, O’brien, JT, Orimo, S, Postuma, RB, Ramaswamy, S, Ross, OA, Salmon, DP, Singleton, A, Taylor, A, Thomas, A, Tiraboschi, P, Toledo, JB, Trojanowski, JQ, Tsuang, D, Walker, Z, Yamada, M and Kosaka, K (2017) Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB Consortium. Neurology 89, 88–100.CrossRefGoogle ScholarPubMed
Mustajab, T, Kwamboka, MS, Choi, DA, Kang, DW, Kim, J, Han, KR, Han, Y, Lee, S, Song, D, Chwae, YJ (2022) Update on extracellular vesicle-based vaccines and therapeutics to combat COVID-19. International Journal of Molecular Sciences 23, 11247.CrossRefGoogle Scholar
Mustapic, M, Eitan, E, Werner, JK Jr., Berkowitz, ST, Lazaropoulos, MP, Tran, J, Goetzl, EJ and Kapogiannis, D (2017) Plasma extracellular vesicles enriched for neuronal origin: a potential window into brain pathologic processes. Frontiers in Neuroscience 11, 278.CrossRefGoogle ScholarPubMed
Nagaraj, S, Laskowska-Kaszub, K, Debski, KJ, Wojsiat, J, Dabrowski, M, Gabryelewicz, T, Kuznicki, J and Wojda, U (2017) Profile of 6 microRNA in blood plasma distinguish early stage Alzheimer’s disease patients from non-demented subjects. Oncotarget 8, 16122–16143.CrossRefGoogle ScholarPubMed
Nelson, PT, Wang, WX, Janse, SA and Thompson, KL (2018) MicroRNA expression patterns in human anterior cingulate and motor cortex: a study of dementia with Lewy bodies cases and controls. Brain Research 1678, 374–383.CrossRefGoogle ScholarPubMed
Ngolab, J, Trinh, I, Rockenstein, E, Mante, M, Florio, J, Trejo, M, Masliah, D, Adame, A, Masliah, E and Rissman, RA (2017) Brain-derived exosomes from dementia with Lewy bodies propagate alpha-synuclein pathology. Acta Neuropathologica Communications 5, 46.CrossRefGoogle ScholarPubMed
Oesterhus, R, Soennesyn, H, Rongve, A, Ballard, C, Aarsland, D and Vossius, C (2014) Long-term mortality in a cohort of home-dwelling elderly with mild Alzheimer’s disease and Lewy body dementia. Dementia and Geriatric Cognitive Disorders 38, 161–169.CrossRefGoogle Scholar
Outeiro, TF, Koss, DJ, Erskine, D, Walker, L, Kurzawa-Akanbi, M, Burn, D, Donaghy, P, Morris, C, Taylor, JP, Thomas, A, Attems, J and Mckeith, I (2019) Dementia with Lewy bodies: an update and outlook. Molecular Neurodegeneration 14, 5.CrossRefGoogle ScholarPubMed
Ovchinnikova, LA, Zalevsky, AO and Lomakin, YA (2022) Extracellular vesicles in chronic demyelinating diseases: prospects in treatment and diagnosis of autoimmune neurological disorders. Life (Basel) 12, 1943.Google ScholarPubMed
Overk, CR and Masliah, E (2014) Pathogenesis of synaptic degeneration in Alzheimer’s disease and Lewy body disease. Biochemical Pharmacology 88, 508–516.CrossRefGoogle ScholarPubMed
Pietrzak, M, Papp, A, Curtis, A, Handelman, SK, Kataki, M, Scharre, DW, Rempala, G and Sadee, W (2016) Gene expression profiling of brain samples from patients with Lewy body dementia. Biochemical and Biophysical Research Communications 479, 875–880.CrossRefGoogle ScholarPubMed
Pinero, J, Sauch, J, Sanz, F and Furlong, LI (2021) The DisGeNET cytoscape app: exploring and visualizing disease genomics data. Computational and Structural Biotechnology Journal 19, 2960–2967.CrossRefGoogle ScholarPubMed
Rajkumar, AP and Aarsland, D (2020) Dementia with Lewy bodies. In Geddes, JR, Andreasen, NC and Goodwin, GM (ed), New Oxford Textbook of Psychiatry, 3rd edn. Oxford: Oxford University press.Google Scholar
Rajkumar, AP, Bidkhori, G, Shoaie, S, Clarke, E, Morrin, H, Hye, A, Williams, G, Ballard, C, Francis, P, Aarsland, D (2020) Postmortem cortical transcriptomics of Lewy body dementia reveal mitochondrial dysfunction and lack of neuroinflammation. The American Journal of Geriatric Psychiatry 28, 75–86.CrossRefGoogle ScholarPubMed
Rajkumar, AP, Hye, A, Lange, J, Manesh, YR, Ballard, C, Fladby, T and Aarsland, D (2021) Next-generation RNA-sequencing of serum small extracellular vesicles discovers potential diagnostic biomarkers for dementia with Lewy bodies. The American Journal of Geriatric Psychiatry 29, 573–584.CrossRefGoogle ScholarPubMed
Rajkumar, AP, Qvist, P, Lazarus, R, Lescai, F, Ju, J, Nyegaard, M, Mors, O, Borglum, AD, Li, Q and Christensen, JH (2015) Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq. BMC Genomics 16, 548.CrossRefGoogle ScholarPubMed
Riolo, G, Cantara, S, Marzocchi, C and Ricci, C (2020) miRNA targets: from prediction tools to experimental validation. Methods and Protocols 4, 1.CrossRefGoogle ScholarPubMed
Rongve, A, Soennesyn, H, Skogseth, R, Oesterhus, R, Hortobagyi, T, Ballard, C, Auestad, BH and Aarsland, D (2016) Cognitive decline in dementia with Lewy bodies: a 5-year prospective cohort study. BMJ Open 6, e010357.CrossRefGoogle ScholarPubMed
Rongve, A, Vossius, C, Nore, S, Testad, I and Aarsland, D (2014) Time until nursing home admission in people with mild dementia: comparison of dementia with Lewy bodies and Alzheimer’s dementia. International Journal of Geriatric Psychiatry 29, 392–398.CrossRefGoogle ScholarPubMed
Saint-Pol, J, Gosselet, F, Duban-Deweer, S, Pottiez, G and Karamanos, Y (2020) Targeting and crossing the blood-brain barrier with extracellular vesicles. Cells 9, 851–851.CrossRefGoogle ScholarPubMed
Santpere, G, Garcia-Esparcia, P, Andres-Benito, P, Lorente-Galdos, B, Navarro, A and Ferrer, I (2018) Transcriptional network analysis in frontal cortex in Lewy body diseases with focus on dementia with Lewy bodies. Brain Pathology 28(3), 315–333.CrossRefGoogle ScholarPubMed
Selnes, P, Aarsland, D, Bjornerud, A, Gjerstad, L, Wallin, A, Hessen, E, Reinvang, I, Grambaite, R, Auning, E, Kjaervik, VK, Due-Tonnessen, P, Stenset, V and Fladby, T (2013) Diffusion tensor imaging surpasses cerebrospinal fluid as predictor of cognitive decline and medial temporal lobe atrophy in subjective cognitive impairment and mild cognitive impairment. Journal of Alzheimers Disease 33, 723–736.CrossRefGoogle ScholarPubMed
Shi, J, Zhang, Y, Tan, D, Zhang, X, Yan, M, Zhang, Y, Franklin, R, Shahbazi, M, Mackinlay, K, Liu, S, Kuhle, B, James, ER, Zhang, L, Qu, Y, Zhai, Q, Zhao, W, Zhao, L, Zhou, C, Gu, W, Murn, J, Guo, J, Carrell, DT, Wang, Y, Chen, X, Cairns, BR, Yang, XL, Schimmel, P, Zernicka-Goetz, M, Cheloufi, S, Zhang, Y, Zhou, T and Chen, Q (2021) PANDORA-seq expands the repertoire of regulatory small RNAs by overcoming RNA modifications. Nature Cell Biology 23(4), 424–436.CrossRefGoogle ScholarPubMed
Soleimani Zakeri, NS, Pashazadeh, S and Motieghader, H (2020) Gene biomarker discovery at different stages of Alzheimer using gene co-expression network approach. Scientific Reports 10, 12210.CrossRefGoogle ScholarPubMed
Stinton, C, Mckeith, I, Taylor, JP, Lafortune, L, Mioshi, E, Mak, E, Cambridge, V, Mason, J, Thomas, A and O’brien, JT (2015) Pharmacological management of Lewy body dementia: a systematic review and meta-analysis. American Journal of Psychiatry 172, 731–742.CrossRefGoogle ScholarPubMed
Stuendl, A, Kunadt, M, Kruse, N, Bartels, C, Moebius, W, Danzer, KM, Mollenhauer, B and Schneider, A (2016) Induction of alpha-synuclein aggregate formation by CSF exosomes from patients with Parkinson’s disease and dementia with Lewy bodies. Brain 139, 481–494.CrossRefGoogle Scholar
Svendsboe, E, Terum, T, Testad, I, Aarsland, D, Ulstein, I, Corbett, A and Rongve, A (2016) Caregiver burden in family carers of people with dementia with Lewy bodies and Alzheimer’s disease. International Journal of Geriatric Psychiatry 31, 1075–1083.CrossRefGoogle ScholarPubMed
Théry, C, Witwer, KW, Aikawa, E, Alcaraz, MJ, Anderson, JD, Andriantsitohaina, R, Antoniou, A, Arab, T, Archer, F, Atkin-Smith, GK, Ayre, D C, Bach, J-M, Bachurski, D, Baharvand, H, Balaj, L, Baldacchino, S, Bauer, NN, Baxter, AA, Bebawy, M, Beckham, C, Bedina Zavec, A, Benmoussa, A, Berardi, AC, Bergese, P, Bielska, E, Blenkiron, C, Bobis-Wozowicz, S, Boilard, E, Boireau, W, Bongiovanni, A, Borràs, FE, Bosch, S, Boulanger, CM, Breakefield, X, Breglio, AM, Brennan, MÁ, Brigstock, DR, Brisson, A, Broekman, MLD, Bromberg, JF, Bryl-Górecka, P, Buch, S, Buck, AH, Burger, D, Busatto, S, Buschmann, D, Bussolati, B, Buzás, EI, Byrd, JB, Camussi, G, Carter, DRF, Caruso, S, Chamley, LW, Chang, Y-T, Chen, C, Chen, S, Cheng, L, Chin, AR, Clayton, A, Clerici, SP, Cocks, A, Cocucci, E, Coffey, RJ, Cordeiro-da-Silva, A, Couch, Y, Coumans, FAW, Coyle, B, Crescitelli, R, Criado, MF, D’Souza-Schorey, C, Das, S, Datta Chaudhuri, A, de Candia, P, De Santana, EF Junior, De Wever, O, del Portillo, HA, Demaret, T, Deville, S, Devitt, A, Dhondt, B, Di Vizio, D, Dieterich, LC, Dolo, V, Dominguez Rubio, AP, Dominici, M, Dourado, MR, Driedonks, TAP, Duarte, FV, Duncan, HM, Eichenberger, RM, Ekström, K, EL Andaloussi, S, Elie-Caille, C, Erdbrügger, U, Falcón-Pérez, JM, Fatima, F, Fish, JE, Flores-Bellver, M, Försönits, Aás, Frelet-Barrand, A, Fricke, F, Fuhrmann, G, Gabrielsson, S, Gámez-Valero, A, Gardiner, C, Gärtner, K, Gaudin, R, Gho, YS, Giebel, B, Gilbert, C, Gimona, M, Giusti, I, Goberdhan, DCI, Görgens, Aé, Gorski, SM, Greening, DW, Gross, JC, Gualerzi, A, Gupta, GN, Gustafson, D, Handberg, A, Haraszti, RA, Harrison, P, Hegyesi, H, Hendrix, A, Hill, AF, Hochberg, FH, Hoffmann, KF, Holder, B, Holthofer, H, Hosseinkhani, B, Hu, G, Huang, Y, Huber, V, Hunt, S, Ibrahim, AG-E, Ikezu, T, Inal, JM, Isin, M, Ivanova, A, Jackson, HK, Jacobsen, S, Jay, SM, Jayachandran, M, Jenster, G, Jiang, L, Johnson, SM, Jones, JC, Jong, A, Jovanovic-Talisman, T, Jung, S, Kalluri, R, Kano, S-I, Kaur, S, Kawamura, Y, Keller, ET, Khamari, D, Khomyakova, E, Khvorova, A, Kierulf, P, Kim, KP, Kislinger, T, Klingeborn, M, Klinke, DJ II, Kornek, M, Kosanović, MM, Kovács, Árpád F, Krämer-Albers, E-M, Krasemann, S, Krause, M, Kurochkin, IV, Kusuma, GD, Kuypers, Sören, Laitinen, S, Langevin, SM, Languino, LR, Lannigan, J, Lässer, C, Laurent, LC, Lavieu, G, Lázaro-Ibáñez, E, Le Lay, S, Lee, M-S, Lee, YXF, Lemos, DS, Lenassi, M, Leszczynska, A, Li, ITS, Liao, K, Libregts, SF, Ligeti, E, Lim, R, Lim, SK, Linē, A, Linnemannstöns, K, Llorente, A, Lombard, CA, Lorenowicz, MJ, Lörincz, Ákos M, Lötvall, J, Lovett, J, Lowry, MC, Loyer, X, Lu, Q, Lukomska, B, Lunavat, TR, Maas, SLN, Malhi, H, Marcilla, A, Mariani, J, Mariscal, J, Martens-Uzunova, ES, Martin-Jaular, L, Martinez, M C, Martins, VR, Mathieu, M, Mathivanan, S, Maugeri, M, McGinnis, LK, McVey, MJ, Meckes, DG Jr, Meehan, KL, Mertens, I, Minciacchi, VR, Möller, A, Møller Jørgensen, M, Morales-Kastresana, A, Morhayim, J, Mullier, Fçois, Muraca, M, Musante, L, Mussack, V, Muth, DC, Myburgh, KH, Najrana, T, Nawaz, M, Nazarenko, I, Nejsum, P, Neri, C, Neri, T, Nieuwland, R, Nimrichter, L, Nolan, JP, Nolte-’t Hoen, ENM, Noren Hooten, N, O’Driscoll, L, O’Grady, T, O’Loghlen, A, Ochiya, T, Olivier, M, Ortiz, A, Ortiz, LA, Osteikoetxea, X, Østergaard, O, Ostrowski, M, Park, J, Pegtel, DM, Peinado, H, Perut, F, Pfaffl, MW, Phinney, DG, Pieters, BCH, Pink, RC, Pisetsky, DS, Pogge von Strandmann, E, Polakovicova, I, Poon, IKH, Powell, BH, Prada, I, Pulliam, L, Quesenberry, P, Radeghieri, A, Raffai, RL, Raimondo, S, Rak, J, Ramirez, MI, Raposo, Gça, Rayyan, MS, Regev-Rudzki, N, Ricklefs, FL, Robbins, PD, Roberts, DD, Rodrigues, SC, Rohde, E, Rome, S, Rouschop, KMA, Rughetti, A, Russell, AE, Saá, P, Sahoo, S, Salas-Huenuleo, E, Sánchez, C, Saugstad, JA, Saul, MJ, Schiffelers, RM, Schneider, R, Schøyen, TH, Scott, A, Shahaj, E, Sharma, S, Shatnyeva, O, Shekari, F, Shelke, GV, Shetty, AK, Shiba, K, Siljander, PR-M, Silva, AM, Skowronek, A, Snyder, OL II, Soares, RP, Sódar, BW, Soekmadji, C, Sotillo, J, Stahl, PD, Stoorvogel, W, Stott, SL, Strasser, EF, Swift, S, Tahara, H, Tewari, M, Timms, K, Tiwari, S, Tixeira, R, Tkach, M, Toh, WS, Tomasini, R, Torrecilhas, AC, Tosar, JP, Toxavidis, V, Urbanelli, L, Vader, P, van Balkom, BWM, van der Grein, SG, Van Deun, J, van Herwijnen, MJC, Van Keuren-Jensen, K, van Niel, G, van Royen, ME, van Wijnen, AJ, Vasconcelos, M H, Vechetti, IJ Jr, Veit, TD, Vella, LJ, Velot, Émilie, Verweij, FJ, Vestad, B, Viñas, JL, Visnovitz, Tás, Vukman, KV, Wahlgren, J, Watson, DC, Wauben, MHM, Weaver, A, Webber, JP, Weber, V, Wehman, AM, Weiss, DJ, Welsh, JA, Wendt, S, Wheelock, AM, Wiener, Zán, Witte, L, Wolfram, J, Xagorari, A, Xander, P, Xu, J, Yan, X, Yáñez-Mó, Mía, Yin, H, Yuana, Y, Zappulli, V, Zarubova, J, Žėkas, V, Zhang, J-Y, Zhao, Z, Zheng, L, Zheutlin, AR, Zickler, AM, Zimmermann, P, Zivkovic, AM, Zocco, D and Zuba-Surma, EK (2018) Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. Journal of Extracellular Vesicles 7, 1535750.CrossRefGoogle ScholarPubMed
Trotta, T, Panaro, MA, Cianciulli, A, Mori, G, Di Benedetto, A and Porro, C (2018) Microglia-derived extracellular vesicles in Alzheimer’s Disease: a double-edged sword. Biochemical Pharmacology 148, 184–192.CrossRefGoogle ScholarPubMed
Urbizu, A and Beyer, K (2020) Epigenetics in Lewy body diseases: impact on gene expression, utility as a biomarker, and possibilities for therapy. International Journal of Molecular Sciences 21, 4718.CrossRefGoogle Scholar
Vader, P, Mol, EA, Pasterkamp, G and Schiffelers, RM (2016) Extracellular vesicles for drug delivery. Advanced Drug Delivery Reviews 106, 148–156.CrossRefGoogle ScholarPubMed
Vassileff, N, Cheng, L and Hill, AF (2020) Extracellular vesicles - propagators of neuropathology and sources of potential biomarkers and therapeutics for neurodegenerative diseases. Journal of Cell Science 133, jcs243139.CrossRefGoogle ScholarPubMed
Velayudhan, L, Ffytche, D, Ballard, C and Aarsland, D (2017) New therapeutic strategies for Lewy body dementias. Current Neurology and Neuroscience Reports 17, 68.CrossRefGoogle Scholar
Vishnoi, A and Rani, S (2023) miRNA biogenesis and regulation of diseases: an updated overview. Methods in Molecular Biology 2595, 1–12.CrossRefGoogle ScholarPubMed
Vossius, C, Rongve, A, Testad, I, Wimo, A and Aarsland, D (2014) The use and costs of formal care in newly diagnosed dementia: a three-year prospective follow-up study. The American Journal of Geriatric Psychiatry 22, 381–388.CrossRefGoogle ScholarPubMed
WHO (2018) International Classification of Diseases for Mortality and Morbidity Statistics (11th Revision) [Online]. World Health Organization. Available at https://icd.who.int/browse11/l-m/en (accessed 02 December 2022).Google Scholar
Wang, X and El Naqa, IM (2008) Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics 24, 325–332.CrossRefGoogle ScholarPubMed
Wang, Y, Yuan, P, Ding, L, Zhu, J, Qi, X, Zhang, Y, Li, Y, Xia, X and Zheng, JC (2022) Circulating extracellular vesicle-containing microRNAs reveal potential pathogenesis of Alzheimer’s disease. Frontiers in Cellular Neuroscience 16, 955511.CrossRefGoogle ScholarPubMed
Watts, KE, Storr, NJ, Barr, PG and Rajkumar, AP (2023) Systematic review of pharmacological interventions for people with Lewy body dementia. Aging Ment Health 27, 203–216.CrossRefGoogle ScholarPubMed
Winston, CN, Sarsoza, F, Spencer, B and Rissman, RA (2021) Characterizing blood-based, microglial derived exosomes (MDEs) as biomarkers for Alzheimer’s disease. Alzheimer’s & Dementia 17, e055371.CrossRefGoogle Scholar
Xie, T, Pei, Y, Shan, P, Xiao, Q, Zhou, F, Huang, L and Wang, S (2022) Identification of miRNA-mRNA pairs in the Alzheimer’s disease expression profile and explore the effect of miR-26a-5p/PTGS2 on amyloid-beta induced neurotoxicity in Alzheimer’s disease cell model. Frontiers in Aging Neuroscience 14, 909222.CrossRefGoogle ScholarPubMed
Yan, S, Nagle, DG, Zhou, Y and Zhang, W (2018) Application of systems biology in the research of TCM formulae. In Zhang, W-D (ed), Systems Biology and Its Application in TCM Formulas Research. London: Academic Press.Google Scholar
Yanez-Mo, M, Siljander, PR, Andreu, Z, Zavec, AB, Borras, FE, Buzas, EI, Buzas, K, Casal, E, Cappello, F, Carvalho, J, Colas, E, Cordeiro-Da Silva, A, Fais, S, Falcon-Perez, JM, Ghobrial, IM, Giebel, B, Gimona, M, Graner, M, Gursel, I, Gursel, M, Heegaard, NH, Hendrix, A, Kierulf, P, Kokubun, K, Kosanovic, M, Kralj-Iglic, V, Kramer-Albers, EM, Laitinen, S, Lasser, C, Lener, T, Ligeti, E, Line, A, Lipps, G, Llorente, A, Lotvall, J, Mancek-Keber, M, Marcilla, A, Mittelbrunn, M, Nazarenko, I, Nolte-’T Hoen, EN, Nyman, TA, O’driscoll, L, Olivan, M, Oliveira, C, Pallinger, E, Del Portillo, HA, Reventos, J, Rigau, M, Rohde, E, Sammar, M, Sanchez-Madrid, F, Santarem, N, Schallmoser, K, Ostenfeld, MS, Stoorvogel, W, Stukelj, R, Van Der Grein, SG, Vasconcelos, MH, Wauben, MH and De Wever, O (2015) Biological properties of extracellular vesicles and their physiological functions. Journal of Extracellular Vesicles 4, 27066.CrossRefGoogle ScholarPubMed
Yang, Y and Zhang, Z (2020) Microglia and Wnt pathways: prospects for inflammation in Alzheimer’s disease. Frontiers in Aging Neuroscience 12, 110.CrossRefGoogle ScholarPubMed
Zhang, Q, Kim, YC and Narayanan, NS (2015) Disease-modifying therapeutic directions for Lewy-Body dementias. Frontiers in Neuroscience 9, 293.CrossRefGoogle ScholarPubMed
Zhao, L and Wang, Z (2019) MicroRNAs: game changers in the regulation of alpha-synuclein in Parkinson’s disease. Parkinson’s Disease 2019, 1–10.CrossRefGoogle ScholarPubMed
Zheng, Q, Huang, T, Zhang, L, Zhou, Y, Luo, H, Xu, H and Wang, X (2016) Dysregulation of ubiquitin-proteasome system in neurodegenerative diseases. Frontiers in Aging Neuroscience 8, 303.CrossRefGoogle ScholarPubMed
Zheng, Q, Li, J and Wang, X (2009) Interplay between the ubiquitin-proteasome system and autophagy in proteinopathies. International Journal of Physiology, Pathophysiology and Pharmacology 1, 127–142.Google ScholarPubMed
Figure 0

Table 1. Biological processes that were enriched among the potential target genes of differentially expressed small extracellular vesicle miRNA in people with dementia with Lewy bodies

Figure 1

Table 2. Molecular pathways that were enriched among the potential target genes of differentially expressed small extracellular vesicle miRNA in people with dementia with Lewy bodies

Figure 2

Table 3. Protein interaction pathways* that were enriched among the potential target genes of differentially expressed small extracellular vesicle miRNA in people with dementia with Lewy bodies

Supplementary material: File

Isik et al. supplementary material

Isik et al. supplementary material 1

Download Isik et al. supplementary material(File)
File 19.3 KB
Supplementary material: File

Isik et al. supplementary material

Isik et al. supplementary material 2

Download Isik et al. supplementary material(File)
File 98.6 KB
Supplementary material: File

Isik et al. supplementary material

Isik et al. supplementary material 3

Download Isik et al. supplementary material(File)
File 663.6 KB