Cited by 26

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

MicroRNA expression within neuronal-derived small extracellular vesicles in frontotemporal degeneration

Pounders, Jonathan, Hill, Emily J., Hooper, Destiny, Zhang, Xiang, Biesiada, Jacek, Kuhnell, Damaris, Greenland, Hannah L., Esfandiari, Leyla, Timmerman, Emerlee, Foster, Forrest, Wang, Chenran, Walsh, Kyle B., Shatz, Rhonna, Woo, Daniel, Medvedovic, Mario, Langevin, Scott, Sawyer, Russell P.·2022·Medicine

Abstract

MicroRNAs (miRNAs) are small non-coding RNA that are powerful regulators of gene expression and can affect the expression of hundreds of genes. miRNAs can be packed in small extracellular vesicles (SEV) and released into the extracellular space by neurons and microglia to act locally as well as pass through the blood-brain barrier and act systemically. We sought to understand the differences in neuronal SEV miRNA expression between frontotemporal dementia (FTD), Alzheimer’s disease (AD), and healthy aging. Plasma was obtained from FTD, AD, and healthy aging participants that were matched based on age, sex, and race/ethnicity. Additionally, a subset of participants also provided paired cerebrospinal fluid samples to compare neuronal SEV miRNAs in plasma and cerebrospinal fluid. Neuronal SEV were isolated using differential ultracentrifugation and antibody conjugated Dynabeads® for the neuronal surface marker, L1CAM. RNA sequencing was performed. 12 FTD, 11 with AD, and 10 healthy aging participants were enrolled in the study. In FTD, SEV miRNA-181c was downregulated compared to healthy controls. In AD, miRNA-122 and miRNA-3591 were downregulated compared to those in healthy controls and FTD. Using an FDR <0.2, only miRNA-21-5p was found to have increased expression in the cerebrospinal fluid compared to plasma in a group of AD and FTD participants. SEV miRNA-181c is significantly downregulated in FTD compared to healthy controls and may mediate its effects through microglial-directed neuroinflammation and interaction with TAR DNA-binding protein 43 (TDP-43) based on pathway analysis. Additionally, the FOXO and Hippo pathways may be important mediators of FTD, based on pathway analysis. Lastly, because only one SEV miRNA was differentially expressed between the plasma and cerebrospinal fluid in paired samples, plasma represents an appropriate biofluid for studying neuronal SEV miRNA.

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 · 10.1097/md.0000000000030854

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

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

Flagged first, then by citations

Show all 2 references

Research works printed but not linked: 44

  1. Bang, 2015, Frontotemporal dementia., Lancet, 386, 1672, 10.1016/S0140-6736(15)00461-4 · DOI · Paper page
  2. Sheinerman, 2017, Circulating brain-enriched microRNAs as novel biomarkers for detection and differentiation of neurodegenerative diseases., Alzheimers Res Ther, 9, 89, 10.1186/s13195-017-0316-0 · DOI · Paper page
  3. Kozomara, 2019, miRBase: from microRNA sequences to function., Nucleic Acids Res, 47, D155, 10.1093/nar/gky1141 · DOI · Paper page
  4. Claassen, 2018, CSF microRNA in patients with Huntington disease., Neurology, 90, 151, 10.1212/WNL.0000000000004871 · DOI
  5. Han, 2020, Mechanism of microRNA-22 in regulating neuroinflammation in Alzheimer’s disease., Brain Behav, 10, e01627, 10.1002/brb3.1627 · DOI
  6. Foggin, 2019, Biological significance of microRNA biomarkers in ALS-Innocent bystanders or disease culprits?, Front Neurol, 10, 578, 10.3389/fneur.2019.00578 · DOI
  7. Eitan, 2016, Vulnerability of microRNA biogenesis in FTD-ALS., Brain Res, 1647, 105, 10.1016/j.brainres.2015.12.063 · DOI
  8. Zhang, 2018, FUS regulates activity of MicroRNA-Mediated gene silencing., Mol Cell, 69, 787, 10.1016/j.molcel.2018.02.001 · DOI
  9. Gascon, 2014, Alterations in microRNA-124 and AMPA receptors contribute to social behavioral deficits in frontotemporal dementia., Nat Med, 20, 1444, 10.1038/nm.3717 · DOI · Paper page
  10. Veremeyko, 2019, Neuronal extracellular microRNAs miR-124 and miR-9 mediate cell-cell communication between neurons and microglia., J Neurosci Res, 97, 162, 10.1002/jnr.24344 · DOI · Paper page
  11. Colombo, 2014, Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles., Annu Rev Cell Dev Biol, 30, 255, 10.1146/annurev-cellbio-101512-122326 · DOI · Paper page
  12. Fowler, 2019, NeuroEVs: Characterizing extracellular vesicles generated in the neural domain., J Neurosci, 39, 9262, 10.1523/JNEUROSCI.0146-18.2019 · DOI · Paper page
  13. Gorno-Tempini, 2011, Classification of primary progressive aphasia and its variants., Neurology, 76, 1006, 10.1212/WNL.0b013e31821103e6 · DOI · Paper page
  14. Rascovsky, 2011, Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia., Brain, 134, 2456, 10.1093/brain/awr179 · DOI · Paper page
  15. Langevin, 2019, Balancing yield, purity and practicality: a modified differential ultracentrifugation protocol for efficient isolation of small extracellular vesicles from human serum., RNA Biol, 16, 5, 10.1080/15476286.2018.1564465 · DOI
  16. Thery, 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., J Extracell Vesicles, 7, 1535750, 10.1080/20013078.2018.1535750 · DOI · Paper page
  17. Oksvold, 2015, Magnetic bead-based isolation of exosomes., Methods Mol Biol, 1218, 465, 10.1007/978-1-4939-1538-5_27 · DOI
  18. Shi, 2014, Plasma exosomal alpha-synuclein is likely CNS-derived and increased in Parkinson’s disease., Acta Neuropathol, 128, 639, 10.1007/s00401-014-1314-y · DOI · Paper page
  19. Langmead, 2012, Fast gapped-read alignment with Bowtie 2., Nat Methods, 9, 357, 10.1038/nmeth.1923 · DOI · Paper page
  20. Huber, 2015, Orchestrating high-throughput genomic analysis with Bioconductor., Nat Meth, 12, 115, 10.1038/nmeth.3252 · DOI
  21. Kozomara, 2014, miRBase: annotating high confidence microRNAs using deep sequencing data., Nucleic Acids Res, 42, D68, 10.1093/nar/gkt1181 · DOI · Paper page
  22. Robinson, 2010, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data., Bioinformatics, 26, 139, 10.1093/bioinformatics/btp616 · DOI · Paper page
  23. Storey, 2003, Statistical significance for genomewide studies., Proc Natl Acad Sci USA, 100, 9440, 10.1073/pnas.1530509100 · DOI · Paper page
  24. Ru, 2014, The multiMiR R package and database: integration of microRNA–target interactions along with their disease and drug associations., Nucleic Acids Res, 42, e133, 10.1093/nar/gku631 · DOI
  25. Vlachos, 2015, DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions., Nucleic Acids Res, 43, D153, 10.1093/nar/gku1215 · DOI
  26. Paraskevopoulou, 2013, DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows., Nucleic Acids Res, 41, W169, 10.1093/nar/gkt393 · DOI
  27. Vlachos, 2015, DIANA-miRPath v3.0: deciphering microRNA function with experimental support., Nucleic Acids Res, 43, W460, 10.1093/nar/gkv403 · DOI
  28. Geistlinger, 2016, Bioconductor’s EnrichmentBrowser: seamless navigation through combined results of set-& network-based enrichment analysis., BMC Bioinf, 17, 45, 10.1186/s12859-016-0884-1 · DOI
  29. Beisser, 2010, BioNet: an R-Package for the functional analysis of biological networks., Bioinformatics, 26, 1129, 10.1093/bioinformatics/btq089 · DOI
  30. Kavakiotis, 2022, DIANA-miTED: a microRNA tissue expression database., Nucleic Acids Res, 50, D1055, 10.1093/nar/gkab733 · DOI
  31. Kos, 2016, MicroRNA-181 promotes synaptogenesis and attenuates axonal outgrowth in cortical neurons., Cell Mol Life Sci, 73, 3555, 10.1007/s00018-016-2179-0 · DOI · Paper page
  32. Fang, 2017, MicroRNA-181c ameliorates cognitive impairment induced by chronic cerebral hypoperfusion in rats., Mol Neurobiol, 54, 8370, 10.1007/s12035-016-0268-6 · DOI
  33. Hawley, 2020, Evidence of a negative feedback network between TDP-43 and miRNAs dependent on TDP-43 nuclear localization., J Mol Biol, 432, 166695, 10.1016/j.jmb.2020.10.029 · DOI
  34. Ma, 2016, MicroRNA-181c Exacerbates brain injury in acute ischemic stroke., Aging Dis, 7, 705, 10.14336/AD.2016.0320 · DOI
  35. Zhang, 2012, The microRNA miR-181c controls microglia-mediated neuronal apoptosis by suppressing tumor necrosis factor., J Neuroinflammation, 9, 211, 10.1186/1742-2094-9-211 · DOI
  36. Xue, 2011, Human activated CD4(+) T lymphocytes increase IL-2 expression by downregulating microRNA-181c., Mol Immunol, 48, 592, 10.1016/j.molimm.2010.10.021 · DOI
  37. Zhang, 2016, Decreased microRNA miR-181c expression in peripheral blood mononuclear cells correlates with elevated serum levels of IL-7 and IL-17 in patients with myasthenia gravis., Clin Exp Med, 16, 413, 10.1007/s10238-015-0358-1 · DOI
  38. Gu, 2020, LncRNA Rpph1 protects amyloid-β induced neuronal injury in SK-N-SH cells via miR-122/Wnt1 axis., Int J Neurosci, 130, 443, 10.1080/00207454.2019.1692834 · DOI
  39. Mueller, 2018, Hippo signaling pathway dysregulation in human huntington’s disease brain and neuronal stem cells., Sci Rep, 8, 11355, 10.1038/s41598-018-29319-4 · DOI
  40. Sahu, 2020, The emerging role of Hippo signaling in neurodegeneration., J Neurosci Res, 98, 796, 10.1002/jnr.24551 · DOI
  41. Sanphui, 2013, FoxO3a is activated and executes neuron death via Bim in response to β-amyloid., Cell Death Dis, 4, e625, 10.1038/cddis.2013.148 · DOI
  42. Zhang, 2014, RNA-processing protein TDP-43 regulates FOXO-dependent protein quality control in stress response., PLoS Genet, 10, e1004693, 10.1371/journal.pgen.1004693 · DOI
  43. Schneider, 2018, Downregulation of exosomal miR-204-5p and miR-632 as a biomarker for FTD: a GENFI study., J Neurol Neurosurg Psychiatry, 89, 851, 10.1136/jnnp-2017-317492 · DOI · Paper page
  44. Sproviero, 2021, Different miRNA profiles in plasma derived small and large extracellular vesicles from patients with neurodegenerative diseases., Int J Mol Sci, 22, 2737, 10.3390/ijms22052737 · DOI

Cited by

Sorted by citations

See all 26

Metadata

Authors
Pounders, J., Hill, E. J., Hooper, D., Zhang, X., Biesiada, J., Kuhnell, D., Greenland, H. L., Esfandiari, L., Timmerman, E., Foster, F., Wang, C., Walsh, K. B., Shatz, R., Woo, D., Medvedovic, M., Langevin, S., & Sawyer, R. P.
Affiliations
  • University of Cincinnati College of Medicine, Department of Neurology and Rehabilitation Medicine, Cincinnati, OH, USA
  • University of Cincinnati College of Medicine, Department of Environmental and Public Health Sciences, Cincinnati, OH, USA
  • University of Cincinnati, Department of Electrical Engineering and Computer Science, Cincinnati, OH, USA
  • University of Cincinnati College of Medicine, Department of Cancer Biology, Cincinnati, OH, USA
  • University of Cincinnati College of Medicine, Department of Emergency Medicine, Cincinnati, OH, USA.
Venue
Medicine
Published
2022-10-07
Volume
101
Issue
40
Pages
e30854
ISSN
1536-5964
Cited by
26
References
2
Language
en

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