The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
Gene Microarrays in Hippocampal Aging: Statistical Profiling Identifies Novel Processes Correlated with Cognitive Impairment
Open accessAbstract
Gene expression microarrays provide a powerful new tool for studying complex processes such as brain aging. However, inferences from microarray data are often hindered by multiple comparisons, small sample sizes, and uncertain relationships to functional endpoints. Here we sought gene expression correlates of aging-dependent cognitive decline, using statistical profiling of gene microarrays in well powered groups of young, mid-aged, and aged rats (n = 10 per group). Animals were trained on two memory tasks, and the hippocampal CA1 region of each was analyzed on an individual microarray (one chip per animal). Aging- and cognition-related genes were identified by testing each gene by ANOVA (for aging effects) and then by Pearson's test (correlating expression with memory). Genes identified by this algorithm were associated with several phenomena known to be aging-dependent, including inflammation, oxidative stress, altered protein processing, and decreased mitochondrial function, but also with multiple processes not previously linked to functional brain aging. These novel processes included downregulated early response signaling, biosynthesis and activity-regulated synaptogenesis, and upregulated myelin turnover, cholesterol synthesis, lipid and monoamine metabolism, iron utilization, structural reorganization, and intracellular Ca2+ release pathways. Multiple transcriptional regulators and cytokines also were identified. Although most gene expression changes began by mid-life, cognition was not clearly impaired until late life. Collectively, these results suggest a new integrative model of brain aging in which genomic alterations in early adulthood initiate interacting cascades of decreased signaling and synaptic plasticity in neurons, extracellular changes, and increased myelin turnover-fueled inflammation in glia that cumulatively induce aging-related cognitive impairment.
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“Gene expression profiling studies of ageing mouse, rat, monkey and human brains have shown significant changes in the expression of synaptic genes (7–13).”
From Neural mechanisms of ageing and cognitive decline (2010) · cites this paper as [10]
In the citing paper
“In particular, organ-specific analysis of brain ageing has revealed a progressive decline in mitochondrial gene expression in rats, rhesus macaques and humans (7,10,13).”
From Neural mechanisms of ageing and cognitive decline (2010) · cites this paper as [10]
In the citing paper
“Markers of inflammation in the blood and in specific tissues are associated with impaired motor and cognitive function (Blalock, Chen, Sharrow, Herman, Porter, Foster, and Landfield 2003;Speisman, Kumar, Rani, Foster, and Ormerod 2013;Zeier, Madorsky, Xu, Ogle, Notterpek, and Foster 2011) suggesting that the pro-inflammatory phenotype impairs the ability of older individuals to cope with age”
From Successful aging: Advancing the science of physical independence in older adults (2015) · cites this paper as Blalock et al. 2003
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Cites
Memory - A century of consolidation
McGaugh, J. L. (2000) · Science · doi:10.1126/science.287.5451.248 · Cited by 4,250
Controlling the false discovery rate in behavior genetics research
Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N., & Golani, I. (2001) · Behavioural Brain Research · doi:10.1016/s0166-4328(01)00297-2 · Cited by 4,146
The molecular biology of memory storage: a dialogue between genes and synapses
Kandel, E. R. (2001) · Science · doi:10.1126/science.1067020 · Cited by 3,917
Excitatory Amino Acids as a Final Common Pathway for Neurologic Disorders
Epstein, F. H., Lipton, S. A., & Rosenberg, P. A. (1994) · New England Journal of Medicine · doi:10.1056/nejm199403033300907 · Cited by 2,676
Parallel human genome analysis: microarray-based expression monitoring of 1000 genes.
Schena, M., Shalon, D., Heller, R. A., Chai, A., Brown, P. O., & Davis, R. W. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.20.10614 · Cited by 1,599
Inflammation in Neurodegenerative Disease—A Double-Edged Sword
Wyss-Coray, T., & Mucke, L. (2002) · Neuron · doi:10.1016/s0896-6273(02)00794-8 · Cited by 1,235
Gene-expression profile of the ageing brain in mice
Lee, C.-K., Weindruch, R., & Prolla, T. A. (2000) · Nature Genetics · doi:10.1038/77046 · Cited by 1,055
Guzowski, J. F., Lyford, G. L., Stevenson, G. D., Houston, F. P., McGaugh, J. L., Worley, P. F., & Barnes, C. A. (2000) · The Journal of Neuroscience · doi:10.1523/jneurosci.20-11-03993.2000 · Cited by 992
Mirnics, K., Middleton, F. A., Marquez, A., Lewis, D. A., & Levitt, P. (2000) · Neuron · doi:10.1016/s0896-6273(00)00085-4 · Cited by 882
Steward, O., Wallace, C. S., Lyford, G., & Worley, P. (1998) · Neuron · doi:10.1016/s0896-6273(00)80591-7 · Cited by 845
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Miot, S., Chancel, R., & Blain, H. (2022) · Lessons from the ICU · doi:10.1007/978-3-030-94133-8_3
Ciric, J., Tešić, V., Milovanovic, N., Macura, I. J., Ivković, S., Kanazir, S., & Perović, M. (2022) · Brain Sciences · doi:10.3390/brainsci12101297
Ma, D., Nutt, C. L., Shanehsaz, P., Peng, X., Louis, D. N., & Kaetzel, D. M. (2023) · doi:10.1158/0008-5472.c.6493748
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Chiavellini, P. (2023) · El Servicio de Difusión de la Creación Intelectual (National University of La Plata) · doi:10.35537/10915/151503
Jia, Y., Son, K., & Matthews, J. C. (2023) · Frontiers in Animal Science · doi:10.3389/fanim.2023.1165321
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Mei, H., Simino, J. M., Li, L., Jiang, F., Bis, J. C., Davies, G., Hill, W. D., Xia, C., Gudnason, V. G., Yang, Q., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25036297
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Dorgans, K. (2017) · theses.fr (ABES) · doi:10.70675/031e34e5zd5b7z4debzab0dzcd77f69b8c71
Chataigner, M. (2021) · theses.fr (ABES) · doi:10.70675/a4317690zc4c8z4ba6za738zd457c6bb9b74
Martin, I. (2008) · VCU Scholars Compass (Virginia Commonwealth University) · doi:10.25772/pgcp-c821
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Aenlle, K. K., & Foster, T. C. (2010) · Advances in neurobiology · doi:10.1007/978-1-4419-7197-5_6
Self-citationBrinton, R. D. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511635700.007 · Cited by 1
Metadata
- Authors
- Blalock, E. M., Chen, K.-C., Sharrow, K., Herman, J. P., Porter, N. M., Foster, T. C., & Landfield, P. W.
- Affiliations
- University of Kentucky (US)
- University of Cincinnati (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2003-05-01
- Volume
- 23
- Issue
- 9
- Pages
- 3807-3819
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6742177
- OpenAlex
- W2132977458
- Cited by
- 570
- References
- 85
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 8 · 2023: 5 · 2022: 23 · 2021: 15 · 2020: 17 · 2019: 27 · 2018: 22 · 2017: 28 · 2016: 28 · 2015: 19 · 2014: 22 · 2013: 32 · 2012: 39
- FWCI
- 22.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01ag018228-02)
- National Institute on Aging (P01AG010836, R01AG014979, AG 14979, AG 10836, AG18228, R37AG004542, AG 04542)
- National Institute of Mental Health (MH 59891, R01MH059891)
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Neuroscience › Neurology › Neurological Disease Mechanisms and Treatments
- Keywords
- brain aging, hippocampal CA1, cognitive impairment, gene expression profiling, memory, inflammation, oxidative stress, mitochondrial function, myelin turnover, synaptogenesis, cholesterol biosynthesis, lipid metabolism, monoamine metabolism, iron utilization, intracellular calcium release, transcription factors, cytokines, synaptic plasticity, rats
- MeSH terms
- Aging, Algorithms, Animals, Behavior, Animal, Cognition, Cognition Disorders, Exploratory Behavior, Gene Expression Regulation, Hippocampus, Male, Memory, Psychomotor Performance, Rats, Inbred F344, Sensitivity and Specificity, Reproducibility of Results, DNA Probes, Biomarkers, Maze Learning, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Rats
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