Cited by 21

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Differentially Expressed miRNAs in Age-Related Neurodegenerative Diseases: A Meta-Analysis

Noronha, Ocana, Mesarosovo, Lucia, Anink, Jasper J., Iyer, Anand, Aronica, Eleonora, Mills, James D.·2022·Genes

Abstract

To date, no neurodegenerative diseases (NDDs) have cures, and the underlying mechanism of their pathogenesis is undetermined. As miRNAs extensively regulate all biological processes and are crucial regulators of healthy brain function, miRNAs differentially expressed in NDDs may provide insight into the factors that contribute to the emergence of protein inclusions and the propagation of deleterious cellular environments. A meta-analysis of miRNAs dysregulated in Alzheimer’s disease, Parkinson’s disease, multiple system atrophy, progressive supranuclear palsy, corticobasal degeneration, dementia with Lewy bodies and frontotemporal lobar degeneration (TDP43 variant) was performed to determine if diseases within a proteinopathy have distinct or shared mechanisms of action leading to neuronal death, and if proteinopathies can be classified on the basis of their miRNA profiles. Our results identified both miRNAs distinct to the anatomy, disease type and pathology, and miRNAs consistently dysregulated within single proteinopathies and across neurodegeneration in general. Our results also highlight the necessity to minimize the variability between studies. These findings showcase the need for more transcriptomic research on infrequently occurring NDDs, and the need for the standardization of research techniques and platforms utilized across labs and diseases.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/genes13061034

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 The average number of differentially expressed miRNAs per profile in Parkinson’s disease, multiple system atrophy, corticobasal degeneration, progressive supranuclear palsy, dementia with Lewy bodies, frontotemporal lobar degeneration and Alzheimer’s disease. A total of 17 ± 21, 11 ± 16, 8, 2, 3, 6 ± 1 and 17 ± 21 miRNAs were upregulated in PD, MSA, CBD, PSP, DLB FTLD and AD, respectively. A total of 16 ± 20, 25 ± 25, 4, 6 ± 6, 18, 10 ± 4 and 18 ± 27 miRNAs were downregulated in PD, MSA, CBD, PSP, DLB FTLD and AD, respectively. Data are represented as mean ± SD. N = 9, 8, 1, 2, 1, 2, 22 upregulated miRNA profiles, and 12, 7, 1, 2, 1, 2, 22 downregulated miRNA profiles in PD, MSA, CBD, PSP, DLB FTLD and AD, respectively. The SD observed is lowest in NDDs with fewer profiles considered.
Figure 1 The average number of differentially expressed miRNAs per profile in Parkinson’s disease, multiple system atrophy, corticobasal degeneration, progressive supranuclear palsy, dementia with Lewy bodies, frontotemporal lobar degeneration and Alzheimer’s disease. A total of 17 ± 21, 11 ± 16, 8, 2, 3, 6 ± 1 and 17 ± 21 miRNAs were upregulated in PD, MSA, CBD, PSP, DLB FTLD and AD, respectively. A total of 16 ± 20, 25 ± 25, 4, 6 ± 6, 18, 10 ± 4 and 18 ± 27 miRNAs were downregulated in PD, MSA, CBD, PSP, DLB FTLD and AD, respectively. Data are represented as mean ± SD. N = 9, 8, 1, 2, 1, 2, 22 upregulated miRNA profiles, and 12, 7, 1, 2, 1, 2, 22 downregulated miRNA profiles in PD, MSA, CBD, PSP, DLB FTLD and AD, respectively. The SD observed is lowest in NDDs with fewer profiles considered.CC BY · PubMed Central
Figure 2 Distribution of upregulated (red) (A) and downregulated (blue) (B) miRNAs. N = 45 (upregulated) and 47 (downregulated) expression profiles. The X-axis in each heatmap corresponds to a miRNA profile, while the Y-axis corresponds to individual miRNAs differentially expressed. The heatmap classifies profiles on the basis of miRNA differential expression in a specific disease, pathology or anatomy. The heatmap demonstrated minimal clustering as per these specified criteria. A greater number of upregulated miRNAs were repeatedly expressed. The distance between profiles was calculated using the asymmetric binary similarity measure.
Figure 2 Distribution of upregulated (red) (A) and downregulated (blue) (B) miRNAs. N = 45 (upregulated) and 47 (downregulated) expression profiles. The X-axis in each heatmap corresponds to a miRNA profile, while the Y-axis corresponds to individual miRNAs differentially expressed. The heatmap classifies profiles on the basis of miRNA differential expression in a specific disease, pathology or anatomy. The heatmap demonstrated minimal clustering as per these specified criteria. A greater number of upregulated miRNAs were repeatedly expressed. The distance between profiles was calculated using the asymmetric binary similarity measure.CC BY · PubMed Central
Figure 3 Venn diagrams representing the overlap of miRNAs differentially expressed in individual neurodegenerative diseases, sharing a common underlying protein pathology. Overlap is displayed between amyloidopathies (A,B), ɑ-synucleinopathies (C,D), tauopathies (E,F) and TDP-43 proteinopathies (G,H). (A,C,E,G) demonstrate upregulated miRNAs, while (B,D,F,H) demonstrate downregulated miRNAs in the individual proteinopathies. A 0–1% overlap was observed between diseases included within a single proteinopathy.
Figure 3 Venn diagrams representing the overlap of miRNAs differentially expressed in individual neurodegenerative diseases, sharing a common underlying protein pathology. Overlap is displayed between amyloidopathies (A,B), ɑ-synucleinopathies (C,D), tauopathies (E,F) and TDP-43 proteinopathies (G,H). (A,C,E,G) demonstrate upregulated miRNAs, while (B,D,F,H) demonstrate downregulated miRNAs in the individual proteinopathies. A 0–1% overlap was observed between diseases included within a single proteinopathy.CC BY · PubMed Central
Figure 4 Venn diagrams representing the overlap of miRNAs differentially expressed within a proteinopathy (A,B), and in a specific anatomical region or biofluid (C,D). (A,C) demonstrate upregulated miRNAs, while (B,D) demonstrate downregulated miRNAs. Few miRNAs were unique to each proteinopathy and anatomy. 15% and 5% of DE miRNAs were commonly down- and upregulated, respectively, between all protein pathologies. Less than 5% of DE miRNAs obtained from the expression profiles were commonly DE in both brain material and biofluids.
Figure 4 Venn diagrams representing the overlap of miRNAs differentially expressed within a proteinopathy (A,B), and in a specific anatomical region or biofluid (C,D). (A,C) demonstrate upregulated miRNAs, while (B,D) demonstrate downregulated miRNAs. Few miRNAs were unique to each proteinopathy and anatomy. 15% and 5% of DE miRNAs were commonly down- and upregulated, respectively, between all protein pathologies. Less than 5% of DE miRNAs obtained from the expression profiles were commonly DE in both brain material and biofluids.CC BY · PubMed Central
Figure 5 Venn diagrams representing miRNAs consistently differentially expressed post RRA analyses. The overlap between miRNAs from brain tissue and biofluids: upregulated (A), downregulated (B). Overlap of DE miRNAs between proteinopathies: upregulated (C), downregulated (D).
Figure 5 Venn diagrams representing miRNAs consistently differentially expressed post RRA analyses. The overlap between miRNAs from brain tissue and biofluids: upregulated (A), downregulated (B). Overlap of DE miRNAs between proteinopathies: upregulated (C), downregulated (D).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 7 references

Research works printed but not linked: 85

  1. Lee, 2001, Neurodegenerative Tauopathies, Annu. Rev. Neurosci., 24, 1121, 10.1146/annurev.neuro.24.1.1121 · DOI · Paper page
  2. Gomperts, 2016, Tau Positron Emission Tomographic Imaging in the Lewy Body Diseases, JAMA Neurol., 73, 1334, 10.1001/jamaneurol.2016.3338 · DOI
  3. Wenning, 2016, Glia and α-synuclein in neurodegeneration: A complex interaction, Neurobiol. Dis., 85, 262, 10.1016/j.nbd.2015.03.003 · DOI
  4. Prasansuklab, 2013, Amyloidosis in Alzheimer’s Disease: The Toxicity of Amyloid β (Aβ), Mechanisms of Its Accumulation and Implications of Medicinal Plants for Therapy, Evid.-Based Complement. Altern. Med., 2013, 413808, 10.1155/2013/413808 · DOI
  5. Lim, 2019, Amyloid-β and Parkinson’s disease, J. Neurol., 266, 2605, 10.1007/s00415-018-9100-8 · DOI
  6. Gomperts, 2008, Imaging amyloid deposition in Lewy body diseases, Neurology, 71, 903, 10.1212/01.wnl.0000326146.60732.d6 · DOI · Paper page
  7. Scheltens, 2019, Amyloid-β peptides in cerebrospinal fluid of patients with dementia with Lewy bodies, Alzheimers Res. Ther., 11, 83, 10.1186/s13195-019-0537-5 · DOI
  8. Hasegawa, 2008, Phosphorylated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis, Ann. Neurol., 64, 60, 10.1002/ana.21425 · DOI · Paper page
  9. Josephs, 2016, Updated TDP-43 in Alzheimer’s disease staging scheme, Acta Neuropathol., 131, 571, 10.1007/s00401-016-1537-1 · DOI
  10. Thal, 2002, Phases of Aβ-deposition in the human brain and its relevance for the development of AD, Neurology, 58, 1791, 10.1212/WNL.58.12.1791 · DOI · Paper page
  11. Valera, 2018, The neuropathology of multiple system atrophy and its therapeutic implications, Auton. Neurosci., 211, 1, 10.1016/j.autneu.2017.11.002 · DOI
  12. Ahmed, 2012, The neuropathology, pathophysiology and genetics of multiple system atrophy, Neuropathol. Appl. Neurobiol., 38, 4, 10.1111/j.1365-2990.2011.01234.x · DOI
  13. Braak, 2003, Staging of brain pathology related to sporadic Parkinson’s disease, Neurobiol. Aging, 24, 197, 10.1016/S0197-4580(02)00065-9 · DOI · Paper page
  14. Ebashi, 2019, How to demix Alzheimer-type and PSP-type tau lesions out of their mixture -hybrid approach to dissect comorbidity-, Acta Neuropathol. Commun., 7, 71, 10.1186/s40478-019-0708-4 · DOI
  15. Ling, 2016, Astrogliopathy predominates the earliest stage of corticobasal degeneration pathology, Brain, 139, 3237, 10.1093/brain/aww256 · DOI
  16. MacKenzie, 2016, Molecular neuropathology of frontotemporal dementia: Insights into disease mechanisms from postmortem studies, J. Neurochem., 138, 54, 10.1111/jnc.13588 · DOI
  17. Marui, 2002, Progression and staging of Lewy pathology in brains from patients with dementia with Lewy bodies, J. Neurol. Sci., 195, 153, 10.1016/S0022-510X(02)00006-0 · DOI
  18. Allen, 2016, Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases, Sci. Data, 3, 160089, 10.1038/sdata.2016.89 · DOI · Paper page
  19. Fatemi, 2016, Transcriptomic Profiling of Extracellular RNAs Present in Cerebrospinal Fluid Identifies Differentially Expressed Transcripts in Parkinson’s Disease, J. Park. Dis., 6, 109
  20. Jiao, 2017, Expression signatures of long non-coding RNA in the substantia nigra of pre-symptomatic mouse model of Parkinson’s disease, Behav. Brain Res., 331, 123, 10.1016/j.bbr.2017.04.044 · DOI
  21. Karim, 2014, Transcriptomics Study of Neurodegenerative Disease: Emphasis on Synaptic Dysfunction Mechanism in Alzheimer’s Disease, CNS Neurol. Disord.-Drug Targets, 13, 1202, 10.2174/1871527313666140917113446 · DOI
  22. Majidinia, 2016, The roles of non-coding RNAs in Parkinson’s disease, Mol. Biol. Rep., 43, 1193, 10.1007/s11033-016-4054-3 · DOI
  23. Watson, 2019, Small Non-coding RNAs: New Class of Biomarkers and Potential Therapeutic Targets in Neurodegenerative Disease, Front. Genet., 10, 364, 10.3389/fgene.2019.00364 · DOI
  24. Ha, 2014, Regulation of microRNA biogenesis, Nat. Rev. Mol. Cell Biol., 15, 509, 10.1038/nrm3838 · DOI · Paper page
  25. Vasudevan, 2012, Posttranscriptional Upregulation by MicroRNAs, Wiley Interdiscip. Rev. RNA, 3, 311, 10.1002/wrna.121 · DOI
  26. Krol, 2010, The widespread regulation of microRNA biogenesis, function and decay, Nat. Rev. Genet., 11, 597, 10.1038/nrg2843 · DOI · Paper page
  27. Friedman, 2009, Most mammalian mRNAs are conserved targets of microRNAs, Genome Res., 19, 92, 10.1101/gr.082701.108 · DOI · Paper page
  28. Hu, 2017, Corrigendum: Diagnostic Value of microRNA for Alzheimer’s Disease: A Systematic Review and Meta-Analysis, Front. Aging Neurosci., 9, 35, 10.3389/fnagi.2017.00035 · DOI
  29. Moradifard, 2018, Analysis of microRNA and Gene Expression Profiles in Alzheimer’s Disease: A Meta-Analysis Approach, Sci. Rep., 8, 4767, 10.1038/s41598-018-20959-0 · DOI
  30. Su, 2019, Meta-Analysis of Gene Expression and Identification of Biological Regulatory Mechanisms in Alzheimer’s Disease, Front. Neurosci., 13, 633, 10.3389/fnins.2019.00633 · DOI
  31. Takousis, 2019, Differential expression of microRNAs in Alzheimer’s disease brain, blood, and cerebrospinal fluid, Alzheimer’s Dement., 15, 1468, 10.1016/j.jalz.2019.06.4952 · DOI · Paper page
  32. Zhang, 2019, Blood circulating miRNAs as biomarkers of Alzheimer’s disease: A systematic review and meta-analysis, Biomark. Med., 13, 1045, 10.2217/bmm-2018-0341 · DOI
  33. Chi, 2018, Integrated Analysis and Identification of Novel Biomarkers in Parkinson’s Disease, Front. Aging Neurosci., 10, 178, 10.3389/fnagi.2018.00178 · DOI
  34. Schulz, 2019, Meta-analyses identify differentially expressed microRNAs in Parkinson’s disease, Ann. Neurol., 85, 835, 10.1002/ana.25490 · DOI
  35. Su, L., Wang, C., Zheng, C., Wei, H., and Song, X. (2018). A meta-analysis of public microarray data identifies biological regulatory networks in Parkinson’s disease. BMC Med. Genom., 11. · DOI
  36. Wang, 2019, The landscape of multiscale transcriptomic networks and key regulators in Parkinson’s disease, Nat. Commun., 10, 5234, 10.1038/s41467-019-13144-y · DOI · Paper page
  37. Durrenberger, 2015, Common mechanisms in neurodegeneration and neuroinflammation: A BrainNet Europe gene expression microarray study, J. Neural Transm., 122, 1055, 10.1007/s00702-014-1293-0 · DOI
  38. Labadorf, 2018, Evidence for a Pan-Neurodegenerative Disease Response in Huntington’s and Parkinson’s Disease Expression Profiles, Front. Mol. Neurosci., 10, 430, 10.3389/fnmol.2017.00430 · DOI
  39. Noori, 2021, Systematic review and meta-analysis of human transcriptomics reveals neuroinflammation, deficient energy metabolism, and proteostasis failure across neurodegeneration, Neurobiol. Dis., 149, 105225, 10.1016/j.nbd.2020.105225 · DOI · Paper page
  40. Li, 2014, Integrated multi-cohort transcriptional meta-analysis of neurodegenerative diseases, Acta Neuropathol. Commun., 2, 93, 10.1186/s40478-014-0093-y · DOI
  41. Soldatov, 2018, RNA velocity of single cells, Nature, 560, 494, 10.1038/s41586-018-0414-6 · DOI · Paper page
  42. Zeisel, 2015, Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq, Science (80-), 347, 1138, 10.1126/science.aaa1934 · DOI · Paper page
  43. Kolde, 2012, Robust rank aggregation for gene list integration and meta-analysis, Bioinformatics, 28, 573, 10.1093/bioinformatics/btr709 · DOI
  44. Sticht, C., de la Torre, C., Parveen, A., and Gretz, N. (2018). miRWalk: An online resource for prediction of microRNA binding sites. PLoS ONE, 13. · DOI · Paper page
  45. Mootha, 2003, PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes, Nat. Genet., 34, 267, 10.1038/ng1180 · DOI · Paper page
  46. Schulze, 2018, Sporadic Parkinson’s disease derived neuronal cells show disease-specific mRNA and small RNA signatures with abundant deregulation of piRNAs, Acta Neuropathol. Commun., 6, 58, 10.1186/s40478-018-0561-x · DOI
  47. Hoss, 2016, microRNA Profiles in Parkinson’s Disease Prefrontal Cortex, Front. Aging Neurosci., 8, 36, 10.3389/fnagi.2016.00036 · DOI · Paper page
  48. Wake, C., Labadorf, A., Dumitriu, A., Hoss, A.G., Bregu, J., Albrecht, K.H., DeStefano, A.L., and Myers, R.H. (2016). Novel microRNA discovery using small RNA sequencing in post-mortem human brain. BMC Genom., 17. · DOI
  49. Ding, 2016, Identification of a panel of five serum miRNAs as a biomarker for Parkinson’s disease, Park. Relat. Disord., 22, 68, 10.1016/j.parkreldis.2015.11.014 · DOI · Paper page
  50. Dong, 2016, A panel of four decreased serum microRNAs as a novel biomarker for early Parkinson’s disease, Biomarkers, 21, 129, 10.3109/1354750X.2015.1118544 · DOI
  51. Compta, 2014, Identification of blood serum micro-RNAs associated with idiopathic and LRRK2 Parkinson’s disease, J. Neurosci. Res., 92, 1071, 10.1002/jnr.23377 · DOI
  52. Annese, 2018, Whole transcriptome profiling of Late-Onset Alzheimer’s Disease patients provides insights into the molecular changes involved in the disease, Sci. Rep., 8, 4282, 10.1038/s41598-018-22701-2 · DOI
  53. Burgos, K., Malenica, I., Metpally, R., Courtright, A., Rakela, B., Beach, T., Shill, H., Adler, C., Sabbagh, M., and Villa, S. (2014). Profiles of Extracellular miRNA in Cerebrospinal Fluid and Serum from Patients with Alzheimer’s and Parkinson’s Diseases Correlate with Disease Status and Features of Pathology. PLoS ONE, 9, Corrigendum in PLoS ONE 2014, 9, e106174. · DOI · Paper page
  54. Vallelunga, 2014, Identification of circulating microRNAs for the differential diagnosis of Parkinson’s disease and Multiple System Atrophy, Front. Cell. Neurosci., 8, 156, 10.3389/fncel.2014.00156 · DOI
  55. Gui, 2015, Altered microRNA profiles in cerebrospinal fluid exosome in Parkinson disease and Alzheimer disease, Oncotarget, 6, 37043, 10.18632/oncotarget.6158 · DOI · Paper page
  56. Kume, 2018, Serum MicroRNA expression profiling in patients with multiple system atrophy, Mol. Med. Rep., 17, 852
  57. Lee, 2014, Altered Expression of miR-202 in Cerebellum of Multiple-System Atrophy, Mol. Neurobiol., 51, 180, 10.1007/s12035-014-8788-4 · DOI
  58. Kim, 2019, Alterations in Striatal microRNA-mRNA Networks Contribute to Neuroinflammation in Multiple System Atrophy, Mol. Neurobiol., 56, 7003, 10.1007/s12035-019-1577-3 · DOI
  59. Wakabayashi, 2016, MicroRNA expression profiles of multiple system atrophy from formalin-fixed paraffin-embedded samples, Neurosci. Lett., 635, 117, 10.1016/j.neulet.2016.10.034 · DOI
  60. Ubhi, 2013, Widespread micro RNA dysregulation in multiple system atrophy—Disease-related alteration in miR-96, Eur. J. Neurosci., 39, 1026, 10.1111/ejn.12444 · DOI
  61. Uwatoko, 2019, Identification of plasma microRNA expression changes in multiple system atrophy and Parkinson’s disease, Mol. Brain, 12, 49, 10.1186/s13041-019-0471-2 · DOI
  62. Tatura, 2016, microRNA profiling: Increased expression of miR-147a and miR-518e in progressive supranuclear palsy (PSP), Neurogenetics, 17, 165, 10.1007/s10048-016-0480-6 · DOI
  63. Wang, 2013, A study of small RNAs from cerebral neocortex of pathology-verified Alzheimer’s disease, dementia with lewy bodies, hippocampal sclerosis, frontotemporal lobar dementia, and non-demented human controls, J. Alzheimer’s Dis., 35, 335, 10.3233/JAD-122350 · DOI · Paper page
  64. Unger, 2012, TMEM106B, the Risk Gene for Frontotemporal Dementia, Is Regulated by the microRNA-132/212 Cluster and Affects Progranulin Pathways, J. Neurosci., 32, 11213, 10.1523/JNEUROSCI.0521-12.2012 · DOI · Paper page
  65. Kumar, 2017, MicroRNA-455-3p as a potential peripheral biomarker for Alzheimer’s disease, Hum. Mol. Genet., 26, 3808, 10.1093/hmg/ddx267 · DOI · Paper page
  66. Lugli, G., Cohen, A.M., Bennett, D.A., Shah, R.C., Fields, C.J., Hernandez, A.G., and Smalheiser, N.R. (2015). Plasma Exosomal miRNAs in Persons with and without Alzheimer Disease: Altered Expression and Prospects for Biomarkers. PLoS ONE, 10. · DOI · Paper page
  67. Wu, 2017, Lower Serum Levels of miR-29c-3p and miR-19b-3p as Biomarkers for Alzheimer’s Disease, Tohoku J. Exp. Med., 242, 129, 10.1620/tjem.242.129 · DOI
  68. Hara, 2017, Serum microRNA miR-501-3p as a potential biomarker related to the progression of Alzheimer’s disease, Acta Neuropathol. Commun., 5, 10, 10.1186/s40478-017-0414-z · DOI · Paper page
  69. Nunez-Iglesias, J., Liu, C.-C., Morgan, T.E., Finch, C.E., and Zhou, X.J. (2010). Joint Genome-Wide Profiling of miRNA and mRNA Expression in Alzheimer’s Disease Cortex Reveals Altered miRNA Regulation. PLoS ONE, 5. · DOI
  70. Patrick, 2017, Dissecting the role of non-coding RNAs in the accumulation of amyloid and tau neuropathologies in Alzheimer’s disease, Mol. Neurodegener., 12, 51, 10.1186/s13024-017-0191-y · DOI · Paper page
  71. Wang, 2010, Patterns of microRNA expression in normal and early Alzheimer’s disease human temporal cortex: White matter versus gray matter, Acta Neuropathol., 121, 193, 10.1007/s00401-010-0756-0 · DOI · Paper page
  72. Papadopoulou, 2008, Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer’s disease correlates with increased BACE1/β-secretase expression, Proc. Natl. Acad. Sci. USA, 105, 6415, 10.1073/pnas.0710263105 · DOI · Paper page
  73. Mulders, 2015, Differential Expression of microRNA in Cerebrospinal Fluid as a Potential Novel Biomarker for Alzheimer’s Disease, J. Alzheimer’s Dis., 47, 243, 10.3233/JAD-140075 · DOI · Paper page
  74. Dong, 2015, Serum MicroRNA Profiles Serve as Novel Biomarkers for the Diagnosis of Alzheimer’s Disease, Dis. Markers, 2015, 1
  75. Lv, 2018, Comparative study of microRNA profiling in one Chinese Family with PSEN1 G378E mutation, Metab. Brain Dis., 33, 1711, 10.1007/s11011-018-0279-2 · DOI
  76. Lau, 2013, Alteration of the micro RNA network during the progression of Alzheimer’s disease, EMBO Mol. Med., 5, 1613, 10.1002/emmm.201201974 · DOI · Paper page
  77. Li, 2018, MicroRNA-132 provides neuroprotection for tauopathies via multiple signaling pathways, Acta Neuropathol., 136, 537, 10.1007/s00401-018-1880-5 · DOI · Paper page
  78. Li, 2019, MiR-100 suppresses inflammatory activation of microglia and neuronal apoptosis following spinal cord injury via TLR4/NF-κB pathway, Eur. Rev. Med. Pharmacol. Sci., 23, 8713
  79. Qian, 2017, Advances in Roles of miR-132 in the Nervous System, Front. Pharmacol., 8, 770, 10.3389/fphar.2017.00770 · DOI · Paper page
  80. Zhang, 2021, Alzheimer’s Disease and microRNA-132: A Widespread Pathological Factor and Potential Therapeutic Target, Front. Neurosci., 15, 687973, 10.3389/fnins.2021.687973 · DOI
  81. Bhinge, 2016, MiR-375 is Essential for Human Spinal Motor Neuron Development and May Be Involved in Motor Neuron Degeneration, Stem Cells, 34, 124, 10.1002/stem.2233 · DOI
  82. Shen, 2016, Downregulation of miR-199b is associated with distant metastasis in colorectal cancer via activation of SIRT1 and inhibition of CREB/KISS1 signaling, Oncotarget, 7, 35092, 10.18632/oncotarget.9042 · DOI
  83. Wang, 2008, Variation in the miRNA-433 Binding Site of FGF20 Confers Risk for Parkinson Disease by Overexpression of α-Synuclein, Am. J. Hum. Genet., 82, 283, 10.1016/j.ajhg.2007.09.021 · DOI
  84. Zhang, 2021, MiR-127-3p targeting CISD1 regulates autophagy in hypoxic–ischemic cortex, Cell Death Dis., 12, 279, 10.1038/s41419-021-03541-x · DOI
  85. Nguyen, 2018, Role of miR-146a in neural stem cell differentiation and neural lineage determination: Relevance for neu-rodevelopmental disorders, Mol. Autism, 9, 38, 10.1186/s13229-018-0219-3 · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
Noronha, O., Mesarosovo, L., Anink, J. J., Iyer, A., Aronica, E., & Mills, J. D.
Affiliations
  • Department of (Neuro)Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, 1105 AZ Amsterdam, The Netherlands
  • Department of Internal Medicine, Erasmus Medicine Center, 3015 GD Rotterdam, The Netherlands
Venue
Genes
Publisher
MDPI AG
Published
2022-06-09
Volume
13
Issue
6
Pages
1034
ISSN
2073-4425
Cited by
21
References
7
Language
en
Funders
Stichting ParkinsonFonds (1862)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review