Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease
Open accessAbstract
BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. However, despite extensive clinical and genomic studies, the molecular basis of AD development and progression remains elusive. METHODS: To elucidate molecular systems associated with AD, we developed a large scale gene expression dataset from 1053 postmortem brain samples across 19 cortical regions of 125 individuals with a severity spectrum of dementia and neuropathology of AD. We excluded brain specimens that evidenced neuropathology other than that characteristic of AD. For the first time, we performed a pan-cortical brain region genomic analysis, characterizing the gene expression changes associated with a measure of dementia severity and multiple measures of the severity of neuropathological lesions associated with AD (neuritic plaques and neurofibrillary tangles) and constructing region-specific co-expression networks. We rank-ordered 44,692 gene probesets, 1558 co-expressed gene modules and 19 brain regions based upon their association with the disease traits. RESULTS: The neurobiological pathways identified through these analyses included actin cytoskeleton, axon guidance, and nervous system development. Using public human brain single-cell RNA-sequencing data, we computed brain cell type-specific marker genes for human and determined that many of the abnormally expressed gene signatures and network modules were specific to oligodendrocytes, astrocytes, and neurons. Analysis based on disease severity suggested that: many of the gene expression changes, including those of oligodendrocytes, occurred early in the progression of disease, making them potential translational/treatment development targets and unlikely to be mere bystander result of degeneration; several modules were closely linked to cognitive compromise with lesser association with traditional measures of neuropathology. The brain regional analyses identified temporal lobe gyri as sites associated with the greatest and earliest gene expression abnormalities. CONCLUSIONS: This transcriptomic network analysis of 19 brain regions provides a comprehensive assessment of the critical molecular pathways associated with AD pathology and offers new insights into molecular mechanisms underlying selective regional vulnerability to AD at different stages of the progression of cognitive compromise and development of the canonical neuropathological lesions of AD.
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RNA-Seq offers an unbiased view of known and novel transcripts and has identified candidate hub genes within networks linked to lipid metabolism and innate immunity.
“RNA-Seq offers an unbiased view of known and novel transcripts and has identified candidate “hub” genes within networks linked to lipid metabolism and innate immunity122,130,159,160.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [122]
The Background (page unavailable) describes genome-wide RNA-expression analysis as unbiased, and the Results, under “Gene co-expression network analysis” (page unavailable), identify immune-response modules containing TYROBP. But the study’s primary brain-tissue profiles used microarrays, not RNA-seq; our copy does not establish that RNA-seq identified hub genes in lipid-metabolism networks or provided a view of novel transcripts. (LLM, conf=0.86)
Experimental — accuracy not yet measuredBulk RNA-Seq cannot resolve cell-specific transcriptional changes due to tissue heterogeneity.
“Despite these advances, bulk RNA-Seq cannot resolve cell-specific transcriptional changes due to tissue heterogeneity122,124.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [122]
There is a clear separation of molecular signatures between the five AD subtypes.
“As shown by Fig. 1D, there is a clear separation of molecular signatures between the five AD subtypes that can be visually appreciated from the whole-transcriptome gene expression heatmap after identifying gene modules using weighted interaction network analysis (WINA) (24, 27).”
From Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets (2021) · cites this paper as [24]
The Results section on gene co-expression network analysis describes modules identified separately for brain regions, not a separation of five AD subtypes. Searches for “five AD subtypes” and “subtypes” found no passage in our copy reporting the claimed separation; the paper’s discussion of five groups concerns brain cell types instead. No page numbers were available in our copy. (LLM, conf=0.76)
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Self-citationHaure-Mirande, J.-V., Wang, M., Audrain, M., Fanutza, T., Kim, S. H., Heja, S., Readhead, B., Dudley, J. T., Blitzer, R. D., Schadt, E. E., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-018-0255-6 · Cited by 84 · see citing works
Self-citationIdentification of Conserved Proteomic Networks in Neurodegenerative Dementia
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Identification of conserved proteomic networks in neurodegenerative dementia
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The three-dimensional landscape of cortical chromatin accessibility in Alzheimer’s disease
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Self-citationSex specific molecular networks and key drivers of Alzheimer’s disease
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Self-citationCDT2‐controlled cell cycle reentry regulates the pathogenesis of Alzheimer's disease
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Self-citationUsing blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification
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Self-citationComprehensive characterization of the transcriptional landscape in Alzheimer’s disease (AD) brains
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“To calculate the genes correlated with the mTOR pathway, we used the hippocampal gene expression data from our recent microarray transcriptomic analysis of AD and control brains in the MSBB-AD cohort [24].”
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- Authors
- Wang, M., Roussos, P., McKenzie, A., Zhou, X., Kajiwara, Y., Brennand, K. J., De Luca, G. C., Crary, J. F., Casaccia, P., Buxbaum, J. D., Ehrlich, M., Gandy, S., Goate, A., Katsel, P., Schadt, E., Haroutunian, V., & Zhang, B.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- James J. Peters VA Medical Center (US)
- Corresponding author
- Haroutunian, Vahram
- Zhang, Bin
- ORCID
- Venue
- Genome Medicine
- Published
- 2016-11-01
- Volume
- 8
- Issue
- 1
- Pages
- 104-104
- ISSN
- 1756-994X (ISSN-L)
- PMC
- PMC5088659
- OpenAlex
- W2531181314
- Cited by
- 314
- References
- 67
- Citations by year
- 2026: 26 · 2025: 28 · 2024: 46 · 2023: 40 · 2022: 33 · 2021: 37 · 2020: 40 · 2019: 29 · 2018: 26 · 2017: 9
- FWCI
- 17.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Aging (R01AG046170)
- Topics
- Health Sciences › Medicine › Physiology › 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, regional brain vulnerability, cortical gene expression, co-expression network, dementia severity, neuritic plaques, neurofibrillary tangles, oligodendrocytes, astrocytes, neurons, temporal lobe, actin cytoskeleton, axon guidance, brain development
- MeSH terms
- Alzheimer Disease, Brain, Dementia, Gene Expression Regulation, Genetic Markers, Humans, Severity of Illness Index, Transcription, Genetic, Cohort Studies, Genetic Predisposition to Disease, Gene Expression Profiling, Gene Regulatory Networks
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