The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Conserved brain myelination networks are altered in Alzheimer’s and other neurodegenerative diseases
Open accessAbstract
Introduction: Comparative transcriptome analyses in Alzheimer's disease (AD) and other neurodegenerative proteinopathies can uncover both shared and distinct disease pathways. Methods: We analyzed 940 brain transcriptomes including patients with AD, progressive supranuclear palsy (PSP; a primary tauopathy), and control subjects. Results: We identified transcriptional coexpression networks implicated in myelination, which were lower in PSP temporal cortex (TCX) compared with AD. Some of these associations were retained even after adjustments for brain cell population changes. These TCX myelination network structures were preserved in cerebellum but they were not differentially expressed in cerebellum between AD and PSP. Myelination networks were downregulated in both AD and PSP, when compared with control TCX samples. Discussion: Downregulation of myelination networks may underlie both PSP and AD pathophysiology, but may be more pronounced in PSP. These data also highlight conservation of transcriptional networks across brain regions and the influence of cell type changes on these networks.
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“Results from RNA sequencing (RNA-seq) of human postmortem brain tissue further reinforce the importance of microglial and inflammatory mechanisms in AD pathogenesis, among other pathways (Allen et al., 2018; Conway et al., 2018; McKenzie et al., 2017; Patrick et al., 2017; Wang et al., 2016, 2019; Zhang et al., 2013).”
From Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models (2020) · cites this paper as Allen et al., 2018
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“independent analyses of RNA-seq from the Mayo and MSSM cohorts previously identified AD-associated coexpression modules enriched for oligodendrocyte expression signatures (Allen et al., 2018; McKenzie et al., 2017), and these gene sets significantly overlapped with consensus cluster D (Figure 1C).”
From Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models (2020) · cites this paper as Allen et al., 2018
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McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
The amyloid hypothesis of Alzheimer's disease at 25 years
Selkoe, D. J., & Hardy, J. (2016) · EMBO Molecular Medicine · doi:10.15252/emmm.201606210 · Cited by 6,164
Jack, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., Shaw, L. M., Vemuri, P., Wiste, H. J., Weigand, S. D., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70291-0 · Cited by 4,396
Self-citationZhang, Y., Sloan, S. A., Clarke, L. E., Caneda, C., Plaza, C. A., Blumenthal, P. D., Vogel, H., Steinberg, G. K., Edwards, M. S. B., Li, G., et al. (2016) · Neuron · doi:10.1016/j.neuron.2015.11.013 · Cited by 2,295
Tau in physiology and pathology
Wang, Y., & Mandelkow, E. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2015.1 · Cited by 2,251
Immune attack: the role of inflammation in Alzheimer disease
Heppner, F. L., Ransohoff, R. M., & Becher, B. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3880 · Cited by 2,134
Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease
Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al. (2013) · Cell · doi:10.1016/j.cell.2013.03.030 · Cited by 1,657
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Metadata
- Authors
- Allen, M., Wang, X., Burgess, J. D., Watzlawik, J., Serie, D. J., Younkin, C. S., Nguyen, T., Malphrus, K. G., Lincoln, S., Carrasquillo, M. M., Ho, C., Chakrabarty, P., Strickland, S., Murray, M. E., Swarup, V., Geschwind, D. H., Seyfried, N. T., Dammer, E. B., Lah, J. J., Levey, A. I., Golde, T. E., Funk, C., Li, H., Price, N. D., Petersen, R. C., Graff‐Radford, N. R., Younkin, S. G., Dickson, D. W., Crook, J. R., Asmann, Y. W., & Ertekin‐Taner, N.
- Affiliations
- Mayo Clinic (US)
- University of Florida (US)
- University of California, Los Angeles (US)
- Emory University (US)
- Institute for Systems Biology (US)
- Corresponding author
- Ertekin‐Taner, Nilüfer
- ORCID
- Allen, Mariet
- Wang, Xue
- Burgess, Jeremy D.
- Watzlawik, Jens
- Nguyen, Thuy
- Carrasquillo, Minerva M.
- Ho, Charlotte
- Chakrabarty, Paramita
- Strickland, Samantha
- Murray, Melissa E.
- Swarup, Vivek
- Geschwind, Daniel H.
- Seyfried, Nicholas T.
- Dammer, Eric B.
- Lah, James J.
- Levey, Allan I.
- Golde, Todd E.
- Funk, Cory
- Price, Nathan D.
- Petersen, Ronald C.
- Graff‐Radford, Neill R.
- Younkin, Steven G.
- Dickson, Dennis W.
- Asmann, Yan W.
- Ertekin‐Taner, Nilüfer
- Venue
- Alzheimer's & Dementia
- Publisher
- Wiley
- Published
- 2017-10-26
- Volume
- 14
- Issue
- 3
- Pages
- 352-366
- ISSN
- 1552-5260 (ISSN-L)
- 1552-5279
- PMC
- PMC5866744
- OpenAlex
- W2765616742
- Cited by
- 143
- References
- 47
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 15 · 2023: 9 · 2022: 26 · 2021: 25 · 2020: 29 · 2019: 12 · 2018: 6
- FWCI
- 9.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Alzheimer s & Dementia (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Arizona Biomedical Research Commission (4001, 0011, 05-901, 1001)
- Arizona Department of Health Services (211002)
- National Institute on Aging (P30 AG19610, R01 AG032990, RF AG051504, U01 AG046139, P50 AG0016574, U01 AG006786)
- National Institute of Neurological Disorders and Stroke (U24 NS072026, R01 NS080820)
- Center for Individualized Medicine, Mayo Clinic
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- Alzheimer's disease, progressive supranuclear palsy
- MeSH terms
- Alzheimer Disease, Brain, Female, Gene Expression Regulation, Humans, Male, Myelin Sheath, Neurons, Supranuclear Palsy, Progressive, Cohort Studies, Computational Biology, Genetic Predisposition to Disease, Transcriptome
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