Cited by 48

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

Differential microRNA expression analyses across two brain regions in Alzheimer’s disease

Dobricic, Valerija, Schilling, Marcel, Schulz, Jessica, Zhu, Ling-Shuang, Zhou, Chao-Wen, Fuß, Janina, Franzenburg, Sören, Zhu, Ling-Qiang, Parkkinen, Laura, Lill, Christina M., Bertram, Lars·2022·Translational Psychiatry

Published version of a preprint

Abstract

Dysregulation of microRNAs (miRNAs) is involved in the pathogenesis of neurodegenerative diseases, including Alzheimer’s disease (AD). Hitherto, sample sizes from differential miRNA expression studies in AD are exceedingly small aggravating any biological inference. To overcome this limitation, we investigated six candidate miRNAs in a large collection of brain samples. Brain tissue was derived from superior temporal gyrus (STG) and entorhinal cortex (EC) from 99 AD patients and 91 controls. MiRNA expression was examined by qPCR (STG) or small RNA sequencing (EC). Brain region-dependent differential miRNA expression was investigated in a transgenic AD mouse model using qPCR and FISH. Total RNA sequencing was used to assess differential expression of miRNA target genes. MiR-129-5p, miR-132-5p, and miR-138-5p were significantly downregulated in AD vs. controls both in STG and EC, while miR-125b-5p and miR-501-3p showed no evidence for differential expression in this dataset. In addition, miR-195-5p was significantly upregulated in EC but not STG in AD patients. The brain region-specific pattern of miR-195-5p expression was corroborated in vivo in transgenic AD mice. Total RNA sequencing identified several novel and functionally interesting target genes of these miRNAs involved in synaptic transmission ( GABRB1 ), the immune-system response ( HCFC2 ) or AD-associated differential methylation ( SLC16A3 ). Using two different methods (qPCR and small RNA-seq) in two separate brain regions in 190 individuals we more than doubled the available sample size for most miRNAs tested. Differential gene expression analyses confirm the likely involvement of miR-129-5p, miR-132-5p, miR-138-5p, and miR-195-5p in AD pathogenesis and highlight several novel potentially relevant target mRNAs.

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 · 10.1038/s41398-022-02108-4

Cites

7 of 7 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 7 references

Research works printed but not linked: 40

  1. Karlsson IK, Escott-Price V, Gatz M, Hardy J, Pedersen NL, Shoai M, et al. Measuring heritable contributions to Alzheimer’s disease: polygenic risk score analysis with twins. Brain Commun. 2022;4:fcab308. · DOI
  2. Smith RG, Pishva E, Shireby G, Smith AR, Roubroeks JAY, Hannon E, et al. A meta-analysis of epigenome-wide association studies in Alzheimer’s disease highlights novel differentially methylated loci across cortex. Nat Commun. 2021;12:3517. · DOI
  3. Silvestro S, Bramanti P, Mazzon E. Role of miRNAs in alzheimer’s disease and possible fields of application. Int J Mol Sci. 2019;20:1–26. · DOI
  4. Bartel DP. Metazoan MicroRNAs. Cell 2018;173:20–51. · DOI · Paper page
  5. Guo H, Ingolia NT, Weissman JS, Bartel DP. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 2010;466:835–40. · DOI · Paper page
  6. Angelucci F, Cechova K, Valis M, Kuca K, Zhang B, Hort J. MicroRNAs in Alzheimer’s disease: diagnostic markers or therapeutic agents? Front Pharm. 2019;10:1–9. · DOI
  7. Ludwig N, Fehlmann T, Kern F, Gogol M, Maetzler W, Deutscher S. et al. Machine learning to detect Alzheimer’s disease from circulating non-coding RNAs. Genomics Proteom. Bioinform 2019;17:430–40. · DOI
  8. Takousis P, Sadlon A, Schulz J, Wohlers I, Dobricic V, Middleton L, et al. Differential expression of microRNAs in Alzheimer’s disease brain, blood, and cerebrospinal fluid. Alzheimer’s Dement. 2019;15:1468–77. · DOI · Paper page
  9. Clarke R, Smith AD, Jobst KA, Refsum H, Sutton L, Ueland PM. Folate, vitamin B12, and serum total homocysteine levels in confirmed Alzheimer disease. Arch Neurol. 1998;55:1449–55. · DOI
  10. Roth M, Tym E, Mountjoy CQ, Huppert FA, Hendrie H, Verma S. et al. CAMDEX. A standardised instrument for the diagnosis of mental disorder in the elderly with special reference to the early detection of dementia. Br J Psychiatry. 1986;149:698–709. · DOI · Paper page
  11. Mirra S, Heyman A, McKeel D SMS, Crain B, Brownlee L, et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology 1991;41:479–86. · DOI · Paper page
  12. Schulz J, Takousis P, Wohlers I, Itua IOG, Dobricic V, Rücker G, et al. Meta-analyses identify differentially expressed micrornas in Parkinson’s disease. Ann Neurol. 2019;85:835–51. · DOI
  13. Dodt M, Roehr JT, Ahmed R, Dieterich C. FLEXBAR-flexible barcode and adapter processing for next-generation sequencing platforms. Biology (Basel). 2012;1:895–905.
  14. Roehr JT, Dieterich C, Reinert K. Flexbar 3.0 - SIMD and multicore parallelization. Bioinformatics 2017;33:2941–2. · DOI
  15. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 2006;34:140–4. · DOI · Paper page
  16. Kozomara A, Birgaoanu M, Griffiths-Jones S. MiRBase: from microRNA sequences to function. Nucleic Acids Res. 2019;47:D155–62. · DOI · Paper page
  17. Friedländer MR, MacKowiak SD, Li N, Chen W, Rajewsky N. MiRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40:37–52. · DOI · Paper page
  18. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21. · DOI
  19. Howe KL, Achuthan P, Allen J, Allen J, Alvarez-Jarreta J, Amode MR, et al. Ensembl 2021. Nucleic Acids Res. 2021;49:D884–91. · DOI
  20. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34:525–7. · DOI · Paper page
  21. Pimentel H, Bray NL, Puente S, Melsted P, Pachter L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat Methods. 2017;14:687–90. · DOI · Paper page
  22. McGeary SE, Lin KS, Shi CY, Pham TM, Bisaria N, Kelley GM, et al. The biochemical basis of microRNA targeting efficacy. Science 2019;366:1–20. · DOI
  23. Acquarone E, Argyrousi EK, Van Den Berg M, Gulisano W, Fà M, Staniszewski A, et al. Synaptic and memory dysfunction induced by tau oligomers is rescued by up-regulation of the nitric oxide cascade. Mol Neurodegener. 2019;14:26. · DOI
  24. Fu H, Rodriguez GA, Herman M, Emrani S, Nahmani E, Barrett G, et al. Tau pathology induces excitatory neuron loss, grid cell dysfunction, and spatial memory deficits reminiscent of early Alzheimer’s disease. Neuron 2017;93:533–41.e5. · DOI · Paper page
  25. Roy DS, Arons A, Mitchell TI, Pignatelli M, Ryan TJ, Tonegawa S. Memory retrieval by activating engram cells in mouse models of early Alzheimer’s disease. Nature 2016;531:508–12. · DOI
  26. Lau P, Bossers K, Janky R, Salta E, Frigerio CS, Barbash S, et al. Alteration of the microRNA network during the progression of Alzheimer’s disease. EMBO Mol Med. 2013;5:1613–34. · DOI · Paper page
  27. Yassa MA. Ground zero in Alzheimer’s disease. Nat Neurosci. 2014;17:146–7. · DOI
  28. Patrick E, Rajagopal S, Wong HKA, McCabe C, Xu J, Tang A, et al. Dissecting the role of non-coding RNAs in the accumulation of amyloid and tau neuropathologies in Alzheimer’s disease. Mol Neurodegener. 2017;12:1–13. · DOI · Paper page
  29. Li QS, Cai D. Integrated miRNA-Seq and mRNA-Seq study to identify miRNAs associated with Alzheimer’s disease using post-mortem brain tissue samples. Front Neurosci. 2021;15:620899. · DOI
  30. Homo sapiens solute carrier family 16 member 3 (SLC16A3), transcript v - Nucleotide - NCBI [Internet]. Available from: https://www.ncbi.nlm.nih.gov/nuccore/1890259789.
  31. Li QS, Sun Y, Wang T. Epigenome-wide association study of Alzheimer’s disease replicates 22 differentially methylated positions and 30 differentially methylated regions. Clin Epigenetics. 2020;12:149. · DOI
  32. Saito ER, Miller JB, Harari O, Cruchaga C, Mihindukulasuriya KA, Kauwe JSK. et al. Alzheimer’s disease alters oligodendrocytic glycolytic and ketolytic gene expression. Alzheimers Dement. 2021;17:1474–86. · DOI
  33. Homo sapiens host cell factor C2 (HCFC2), mRNA - Nucleotide - NCBI [Internet]. Available from: https://www.ncbi.nlm.nih.gov/nuccore/NM_013320.
  34. Liu C, Zhang X, Chai H, Xu S, Liu Q, Luo Y. et al. Identification of immune cells and key genes associated with Alzheimer’s disease. Int J Med Sci. 2022;19:112–25. · DOI
  35. Homo sapiens gamma-aminobutyric acid type A receptor subunit beta1 (GA - Nucleotide - NCBI. Available from: https://www.ncbi.nlm.nih.gov/nuccore/NM_000812.
  36. Jung M, Schaefer A, Steiner I, Kempkensteffen C, Stephan C, Erbersdobler A, et al. Robust MicroRNA stability in degraded RNA preparations from human tissue and cell samples. Clin Chem. 2010;56:998–1006. · DOI
  37. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45:1452–8. · DOI · Paper page
  38. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat Genet. 2019;51:414–30. · DOI · Paper page
  39. Bertram L, Tanzi RE. Genomic mechanisms in Alzheimer’s disease. Brain Pathol. 2020;30:966–77. · DOI
  40. Bellenguez C, Küçükali F, Jansen I, Andrade V, Moreno-grau S. New insights on the genetic etiology of Alzheimer’s and related dementia. Nat Genet. 2022;54:412–36. · DOI · Paper page

Cited by

Sorted by citations

See all 48

Metadata

Authors
Dobricic, V., Schilling, M., Schulz, J., Zhu, L.-S., Zhou, C.-W., Fuß, J., Franzenburg, S., Zhu, L.-Q., Parkkinen, L., Lill, C. M., & Bertram, L.
Venue
Translational Psychiatry
Published
2022-08-29
Volume
12
Issue
1
ISSN
2158-3188
Cited by
48
References
7
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