CoChat
Sign Up
Cited by 14

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Multiple Transcriptomic Analyses Explore Potential Synaptic Biomarker Rabphilin-3A for Alzheimer's Disease

Nguyen, Doan Phuong Quy, Pham, Son, Jallow, Amadou Wurry, Ho, Nguyen-Tuong, Le, Bao, Quang, Hung Tran, Lin, Yi-Fang, Lin, Yung-Feng·2024·Scientific Reports
DOI 10.1038/s41598-024-66693-8PMC PMC11319786
Open this paper in CoChat — free account

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/s41598-024-66693-8

Cites

9 of 9 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 9 references

Research works printed but not linked: 55

  1. Wong, W. Economic burden of Alzheimer disease and managed care considerations. Am. J. Manag. Care 26, S177–S183. https://doi.org/10.37765/ajmc.2020.88482 (2020). · DOI
  2. Hampel, H. et al. The amyloid-β pathway in Alzheimer’s disease. Mol. Psychiatry 26, 5481–5503. https://doi.org/10.1038/s41380-021-01249-0 (2021). · DOI · Paper page
  3. Cho, Y., Bae, H.-G., Okun, E., Arumugam, T. V. & Jo, D.-G. Physiology and pharmacology of amyloid precursor protein. Pharmacol. Ther. 235, 108122. https://doi.org/10.1016/j.pharmthera.2022.108122 (2022). · DOI
  4. LaFerla, F. M., Green, K. N. & Oddo, S. Intracellular amyloid-β in Alzheimer’s disease. Nat. Rev. Neurosci. 8, 499–509. https://doi.org/10.1038/nrn2168 (2007). · DOI · Paper page
  5. Walsh, D. M. et al. Naturally secreted oligomers of amyloid beta protein potently inhibit hippocampal long-term potentiation in vivo. Nature 416, 535–539. https://doi.org/10.1038/416535a (2002). · DOI · Paper page
  6. Tsai, Y. F. et al. Ganglioside Hp-s1 analogue inhibits amyloidogenic toxicity in Alzheimer’s disease model cells. ACS Chem. Neurosci. 10, 528–536. https://doi.org/10.1021/acschemneuro.8b00406 (2019). · DOI
  7. Ting, L. L. et al. Expression of AHI1 rescues amyloidogenic pathology in Alzheimer’s disease model cells. Mol. Neurobiol. 56, 7572–7582. https://doi.org/10.1007/s12035-019-1587-1 (2019). · DOI
  8. Coronel, R. et al. Role of amyloid precursor protein (APP) and its derivatives in the biology and cell fate specification of neural stem cells. Mol. Neurobiol. 55, 7107–7117. https://doi.org/10.1007/s12035-018-0914-2 (2018). · DOI
  9. Gautam, V., D’Avanzo, C., Berezovska, O., Tanzi, R. E. & Kovacs, D. M. Synaptotagmins interact with APP and promote Aβ generation. Mol. Neurodegener. 10, 31. https://doi.org/10.1186/s13024-015-0028-5 (2015). · DOI
  10. Barbier, P. et al. Role of tau as a microtubule-associated protein: Structural and functional aspects. Front. Aging Neurosci. 11, 204. https://doi.org/10.3389/fnagi.2019.00204 (2019). · DOI
  11. Dehmelt, L. & Halpain, S. The MAP2/Tau family of microtubule-associated proteins. Genome Biol. 6, 204. https://doi.org/10.1186/gb-2004-6-1-204 (2005). · DOI · Paper page
  12. de Paula, V. J. R., Guimarães, F. M., Diniz, B. S. & Forlenza, O. V. Neurobiological pathways to Alzheimer’s disease: Amyloid-beta, TAU pro tein or both?. Dement. Neuropsychol. 3, 188–194. https://doi.org/10.1590/S1980-57642009DN30300003 (2009). · DOI
  13. Mamun, A. A., Uddin, M. S., Mathew, B. & Ashraf, G. M. Toxic tau: Structural origins of tau aggregation in Alzheimer’s disease. Neural Regen. Res. 15, 1417–1420. https://doi.org/10.4103/1673-5374.274329 (2020). · DOI
  14. Shafiei, S. S., Guerrero-Muñoz, M. J. & Castillo-Carranza, D. L. Tau oligomers: Cytotoxicity, propagation, and mitochondrial damage. Front. Aging Neurosci. 9, 83. https://doi.org/10.3389/fnagi.2017.00083 (2017). · DOI · Paper page
  15. Patow, G. et al. Whole-brain modeling of the differential influences of amyloid-beta and tau in Alzheimer’s disease. Alzheimer’s Res. Ther. 15, 210. https://doi.org/10.1186/s13195-023-01349-9 (2023). · DOI
  16. de Vries, L. E. et al. Gene-expression profiling of individuals resilient to Alzheimer’s disease reveals higher expression of genes related to metallothionein and mitochondrial processes and no changes in the unfolded protein response. Acta Neuropathol. Commun. 12, 68. https://doi.org/10.1186/s40478-024-01760-9 (2024). · DOI
  17. Aguzzoli Heberle, B. et al. Mapping medically relevant RNA isoform diversity in the aged human frontal cortex with deep long-read RNA-seq. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02245-9 (2024). · DOI
  18. Panitch, R. et al. Blood and brain transcriptome analysis reveals APOE genotype-mediated and immune-related pathways involved in Alzheimer disease. Alzheimer’s Res. Ther. 14, 30. https://doi.org/10.1186/s13195-022-00975-z (2022). · DOI
  19. Wang, M. et al. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: Review, recommendation, implementation and application. Mol. Neurodegener. 17, 17. https://doi.org/10.1186/s13024-022-00517-z (2022). · DOI
  20. Beauchamp, A. et al. Whole-brain comparison of rodent and human brains using spatial transcriptomics. eLife 11, e79418. https://doi.org/10.7554/eLife.79418 (2022). · DOI
  21. Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. https://doi.org/10.1093/nar/28.1.27 (2000). · DOI · Paper page
  22. El-Baba, R. M. & Schury, M. P. Neuroanatomy, Frontal Cortex (StatPearls, 2020).
  23. Yang, A. et al. Associations of cortical iron accumulation with cognition and cerebral atrophy in Alzheimer’s disease. Quant. Imaging Med. Surg. 12, 4570 (2022). · DOI
  24. Yang, H. et al. Study of brain morphology change in Alzheimer’s disease and amnestic mild cognitive impairment compared with normal controls. Gen. Psychiatry 32, e100005 (2019). · DOI
  25. Igarashi, K. M. Entorhinal cortex dysfunction in Alzheimer’s disease. Trends Neurosci. 46, 124–136 (2023). · DOI
  26. Bottero, V., Powers, D., Yalamanchi, A., Quinn, J. P. & Potashkin, J. A. Key disease mechanisms linked to Alzheimer’s disease in the entorhinal cortex. Int. J. Mol. Sci. 22, 3915 (2021). · DOI
  27. Chen, Z. S. & Chan, H. Y. E. Transcriptional dysregulation in neurodegenerative diseases: Who tipped the balance of Yin Yang 1 in the brain?. Neural Regen. Res. 14, 1148–1151. https://doi.org/10.4103/1673-5374.251193 (2019). · DOI
  28. Salta, E. et al. Adult hippocampal neurogenesis in Alzheimer’s disease: A roadmap to clinical relevance. Cell Stem Cell 30, 120–136 (2023). · DOI
  29. Rao, Y. L. et al. Hippocampus and its involvement in Alzheimer’s disease: A review. 3 Biotech 12, 55 (2022). · DOI
  30. Li, C. et al. Synaptic targeting of rabphilin-3A, a synaptic vesicle Ca2+/phospholipid-binding protein, depends on rab3A/3C. Neuron 13, 885–898. https://doi.org/10.1016/0896-6273(94)90254-2 (1994). · DOI
  31. Ferrer-Orta, C. et al. Structural characterization of the Rabphilin-3A-SNAP25 interaction. Proc. Natl. Acad. Sci. USA 114, E5343–E5351. https://doi.org/10.1073/pnas.1702542114 (2017). · DOI
  32. Li, T., Cheng, Q., Wang, S. & Ma, C. Rabphilin 3A binds the N-peptide of SNAP-25 to promote SNARE complex assembly in exocytosis. eLife 11, e79926. https://doi.org/10.7554/eLife.79926 (2022). · DOI
  33. Deák, F. et al. Rabphilin regulates SNARE-dependent re-priming of synaptic vesicles for fusion. EMBO J. 25, 2856–2866. https://doi.org/10.1038/sj.emboj.7601165 (2006). · DOI
  34. Stanic, J. et al. Rabphilin 3A retains NMDA receptors at synaptic sites through interaction with GluN2A/PSD-95 complex. Nat. Commun. 6, 10181. https://doi.org/10.1038/ncomms10181 (2015). · DOI
  35. Pavinato, L. et al. Missense variants in RPH3A cause defects in excitatory synaptic function and are associated with a clinically variable neurodevelopmental disorder. Genet. Med. 25, 100922. https://doi.org/10.1016/j.gim.2023.100922 (2023). · DOI
  36. Tan, M. G. et al. Decreased rabphilin 3A immunoreactivity in Alzheimer’s disease is associated with Aβ burden. Neurochem. Int. 64, 29–36. https://doi.org/10.1016/j.neuint.2013.10.013 (2014). · DOI
  37. Smith, R. et al. Loss of SNAP-25 and rabphilin 3a in sensory-motor cortex in Huntington’s disease. J. Neurochem. 103, 115–123. https://doi.org/10.1111/j.1471-4159.2007.04703.x (2007). · DOI
  38. Remnestål, J. et al. Association of CSF proteins with tau and amyloid β levels in asymptomatic 70-year-olds. Alzheimer’s Res. Ther. 13, 54. https://doi.org/10.1186/s13195-021-00789-5 (2021). · DOI
  39. Wang, X.-L. & Li, L. Cell type-specific potential pathogenic genes and functional pathways in Alzheimer’s disease. BMC Neurol. 21, 381. https://doi.org/10.1186/s12883-021-02407-1 (2021). · DOI
  40. Yang, L. et al. Rabphilin-3A undergoes phase separation to regulate GluN2A mobility and surface clustering. Nat. Commun. 14, 379. https://doi.org/10.1038/s41467-023-36046-6 (2023). · DOI
  41. Ranasinghe, K. G. et al. Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer’s disease. eLife 11, e77850. https://doi.org/10.7554/eLife.77850 (2022). · DOI
  42. Szodorai, A. et al. APP anterograde transport requires Rab3A GTPase activity for assembly of the transport vesicle. J. Neurosci. 29, 14534–14544. https://doi.org/10.1523/jneurosci.1546-09.2009 (2009). · DOI
  43. Wang, Z. X., Tan, L. & Yu, J. T. Axonal transport defects in Alzheimer’s disease. Mol. Neurobiol. 51, 1309–1321. https://doi.org/10.1007/s12035-014-8810-x (2015). · DOI
  44. Shieh, J. C., Huang, P. T. & Lin, Y. F. Alzheimer’s disease and diabetes: Insulin signaling as the bridge linking two pathologies. Mol. Neurobiol. 57, 1966–1977. https://doi.org/10.1007/s12035-019-01858-5 (2020). · DOI
  45. Yang, Y. et al. Amyloid-β oligomers may impair SNARE-mediated exocytosis by direct binding to syntaxin 1a. Cell Rep. 12, 1244–1251. https://doi.org/10.1016/j.celrep.2015.07.044 (2015). · DOI · Paper page
  46. Naughton, B. J. et al. Blood genome-wide transcriptional profiles reflect broad molecular impairments and strong blood-brain links in Alzheimer’s disease. J. Alzheimers Dis. 43, 93–108 (2015). · DOI
  47. Berchtold, N. C. et al. Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer’s disease. Neurobiol. Aging 34, 1653–1661 (2013). · DOI · Paper page
  48. Liang, W. S. et al. Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiol. Genomics 28, 311–322 (2007). · DOI
  49. Leng, K. et al. Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nat. Neurosci. 24, 276–287 (2021). · DOI · Paper page
  50. Soreq, L., Bird, H., Mohamed, W. & Hardy, J. Single-cell RNA sequencing analysis of human Alzheimer’s disease brain samples reveals neuronal and glial specific cells differential expression. PLoS One 18, e0277630. https://doi.org/10.1371/journal.pone.0277630 (2023). · DOI
  51. Smyth, G. K. Bioinformatics and Computational Biology Solutions Using R and Bioconductor 397–420 (Springer, 2005). · DOI · Paper page
  52. Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550. https://doi.org/10.1073/pnas.0506580102 (2005). · DOI · Paper page
  53. Robin, X. et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 1–8 (2011). · DOI · Paper page
  54. Li, X.-W. et al. SCAD-Brain: A public database of single cell RNA-seq data in human and mouse brains with Alzheimer’s disease. Front. Aging Neurosci. 15, 1157792 (2023). · DOI
  55. Sanotra, M. R. et al. Acrolein adducts and responding autoantibodies correlate with metabolic disturbance in Alzheimer’s disease. Alzheimers Res. Ther. 15, 115. https://doi.org/10.1186/s13195-023-01261-2 (2023). · DOI

Cited by

Sorted by citations

See all 14

Metadata

Authors
Nguyen, D. P. Q., Pham, S., Jallow, A. W., Ho, N.-T., Le, B., Quang, H. T., Lin, Y.-F., & Lin, Y.-F.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2024-08-12
Volume
14
Issue
1
ISSN
2045-2322
DOI
10.1038/s41598-024-66693-8
PMC
PMC11319786
Cited by
14
References
9
Language
en
Licence
Funders
Taipei Medical University-Shuang Ho Hospital, Ministry of Health and Welfare (112FRP-29)

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