Transcriptional signatures of brain aging and Alzheimer’s disease: What are our rodent models telling us?
Open accessAbstract
Aging is the biggest risk factor for idiopathic Alzheimer's disease (AD). Recently, the National Institutes of Health released AD research recommendations that include: appreciating normal brain aging, expanding data-driven research, using open-access resources, and evaluating experimental reproducibility. Transcriptome data sets for aging and AD in humans and animal models are available in NIH-curated, publically accessible databases. However, little work has been done to test for concordance among those molecular signatures. Here, we test the hypothesis that brain transcriptional profiles from animal models recapitulate those observed in the human condition. Raw transcriptional profile data from twenty-nine studies were analyzed to produce p-values and fold changes for young vs. aged or control vs. AD conditions. Concordance across profiles was assessed at three levels: (1) # of significant genes observed vs. # expected by chance; (2) proportion of significant genes showing directional agreement; (3) correlation among studies for magnitude of effect among significant genes. The highest concordance was found within subjects across brain regions. Normal brain aging was concordant across studies, brain regions, and species, despite profound differences in chronological aging among humans, rats and mice. Human studies of idiopathic AD were concordant across brain structures and studies, but were not concordant with the transcriptional profiles of transgenic AD mouse models. Further, the five transgenic AD mouse models that were assessed were not concordant with one another. These results suggest that normal brain aging is similar in humans and research animals, and that different transgenic AD model mice may reflect selected aspects of AD pathology.
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Our findings are consistent with a complementary study of mouse data from gene expression arrays.
“Our findings are consistent with a complementary study of mouse data from gene expression arrays (Hargis and Blalock, 2017).”
From Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models (2020) · cites this paper as Hargis and Blalock, 2017
Hargis and Blalock (2017) reported that DEGs were concordant between aging in humans and rodent models, a conclusion supported by our analysis.
“Hargis and Blalock (2017) reported that DEGs were concordant between aging in humans and rodent models, a conclusion supported by our analysis.”
From Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models (2020) · cites this paper as Hargis and Blalock, 2017
Traditional models created by incorporating a highly penetrant mutation in APP, PSEN1, or PSEN2 onto a single inbred background strain have largely failed to yield successful results in human clinical trials.
“traditional models created by incorporating a highly penetrant mutation in APP, PSEN1, or PSEN2 onto a single inbred background strain have largely failed to yield successful results in human clinical trials [286, 287]”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [287]
Other studies have questioned the overall degree of conservation between AD mouse models and human brains.
“Although some studies have identified selected overlaps in expression changes between AD mouse models and human brains (Bennett et al., 2018; Castillo et al., 2017; Mostafavi et al., 2018; Neuner et al., 2019; Rojo et al., 2017), other studies have questioned the overall degree of conservation (Burns et al., 2015; Galatro et al., 2017; Hargis and Blalock, 2017).”
From Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models (2020) · cites this paper as Hargis and Blalock, 2017
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Investigation of Phosphatidylserine-Transporting Activity of Human TMEM16C Isoforms
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Maladaptive aging trajectory in late-onset Alzheimer's disease
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- Authors
- Hargis, K. E., & Blalock, E. M.
- Affiliations
- University of Kentucky (US)
- Corresponding author
- Blalock, Eric M
- Venue
- Behavioural Brain Research
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 322
- Issue
- Pt B
- Pages
- 311-328
- ISSN
- 0166-4328 (ISSN-L)
- PMC
- PMC5533609
- OpenAlex
- W2346448457
- Cited by
- 90
- References
- 57
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 4 · 2023: 6 · 2022: 5 · 2021: 20 · 2020: 21 · 2019: 12 · 2018: 11 · 2017: 5 · 2016: 2
- FWCI
- 1.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Behavioural Brain Research (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UKnowledge (University of Kentucky) (submittedVersion, cc-by-nc-nd)
- Funders
- NIH/NIA (AG037868, AG034605)
- 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 › Aging › Genetics, Aging, and Longevity in Model Organisms
- Keywords
- Alzheimer's disease, brain aging, gene expression signatures, transcriptome, transgenic mouse model, gene expression, brain regions
- MeSH terms
- Aging, Alzheimer Disease, Animals, Disease Models, Animal, Humans, Species Specificity, Gene Expression Profiling, Mice, Rats, Transcriptome
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