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A Systems Level Analysis of Transcriptional Changes in Alzheimer's Disease and Normal Aging
Open accessAbstract
Alzheimer's disease (AD) is a debilitating neurodegenerative disorder affecting millions of elderly individuals worldwide. Advances in the genetics of AD have led to new levels of understanding and treatment opportunities. Here, we used a systems biology approach based on weighted gene coexpression network analysis to determine transcriptional networks in AD. This method permits a higher order depiction of gene expression relationships and identifies modules of coexpressed genes that are functionally related, rather than producing massive gene lists. Using this framework, we characterized the transcriptional network in AD, identifying 12 distinct modules related to synaptic and metabolic processes, immune response, and white matter, nine of which were related to disease progression. We further examined the association of gene expression changes with progression of AD and normal aging, and were able to compare functional modules of genes defined in both conditions. Two biologically relevant modules were conserved between AD and aging, one related to mitochondrial processes such as energy metabolism, and the other related to synaptic plasticity. We also identified several genes that were central, or hub, genes in both aging and AD, including the highly abundant signaling molecule 14.3.3 ζ (YWHAZ), whose role in AD and aging is uncharacterized. Finally, we found that presenilin 1 (PSEN1) is highly coexpressed with canonical myelin proteins, suggesting a role for PSEN1 in aspects of glial-neuronal interactions related to neurodegenerative processes.
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“Miller and colleagues149 applied network analysis to compare the transcriptome in normal ageing and Alzheimer disease, finding many shared features that were downregulated in Alzheimer disease and normal human ageing149.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [149]
In the citing paper
“Furthermore, reduced expression of genes involved in mitochondrial energy metabolism may become more pronounced in humans with cognitive decline and Alzheimer’s disease (20–22).”
From Neural mechanisms of ageing and cognitive decline (2010) · cites this paper as [22]
In the citing paper
“While transcriptome networks in AD brain have been examined (Miller et al., 2008; Miller et al., 2013; Zhang et al., 2013), network changes in the AD brain proteome, including those associated with early asymptomatic stages of disease, have not been explored.”
From A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer’s Disease (2017) · cites this paper as Miller et al., 2008
In the citing paper
“Thus, as seen in transcriptome level network analyses (Miller et al., 2008; Oldham et al., 2008a), certain modules of co-expressed proteins enrich with markers of specific brain cell types, and likely reflect changes in the abundance, activation, and/or changing phenotype(s) of these cells in AD.”
From A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer’s Disease (2017) · cites this paper as Miller et al., 2008
In the citing paper
“Namely, B-M1 and B-M4, both enriched with neuronal markers (Fig. 2B) and associated with related ontologies, were negatively correlated with CERAD and Braak scores, consistent with loss of synaptic proteins in AD (Terry et al., 1991) and observed in RNA co-expression modules (Miller et al., 2008).”
From A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer’s Disease (2017) · cites this paper as Miller et al., 2008
In the citing paper
“Protein co-expression analysis is a powerful tool to understand biological network, pathway, and cell type changes in human tissue3,4.”
In the citing paper
“Systems-based approaches such as weighted gene co-expression network analysis (WGCNA) can be used to analyze biochemical and cellular changes in brain, and are useful to help capture the complexity of perturbations in biological networks that are related to disease [2–4].”
From Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease (2018) · cites this paper as [3]
In the citing paper
“Previous work examining whole brain lysates from AD patients and non-demented individuals (Miller et al., 2008; Mostafavi et al., 2018; Zhang et al., 2013) demonstrated the promise of network analyses in AD, but these studies were limited to larger brain regions and thus could not address cell type-specific vulnerability.”
From Selective Neuronal Vulnerability in Alzheimer’s Disease: A Network-Based Analysis (2020) · cites this paper as Miller et al., 2008
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Metadata
- Authors
- Miller, J. A., Oldham, M. C., & Geschwind, D. H.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2008-02-06
- Volume
- 28
- Issue
- 6
- Pages
- 1410-1420
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC2902235
- OpenAlex
- W2011633018
- Cited by
- 385
- References
- 61
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 5 · 2023: 18 · 2022: 14 · 2021: 31 · 2020: 22 · 2019: 13 · 2018: 26 · 2017: 23 · 2016: 18 · 2015: 33 · 2014: 18 · 2013: 27 · 2012: 37
- FWCI
- 19.14
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute on Aging (F31AG031649, U01AG016976, R01AG026938)
- National Institute of Mental Health (T32MH073526)
- 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 › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Alzheimer's disease, normal aging, gene co-expression network, transcriptional regulatory networks, weighted gene co-expression network analysis, hub genes, mitochondrial bioenergetics, synaptic plasticity, presenilin 1, myelin proteins, neuron-glia interactions, immune response, white matter, disease progression
- MeSH terms
- Adult, Aged, Aged, 80 and over, Aging, Alzheimer Disease, Hippocampus, Humans, Middle Aged, Transcription, Genetic, Gene Expression Profiling, Systems Biology, Presenilin-1
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