The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
AGEMAP: A Gene Expression Database for Aging in Mice
Open accessAbstract
We present the AGEMAP (Atlas of Gene Expression in Mouse Aging Project) gene expression database, which is a resource that catalogs changes in gene expression as a function of age in mice. The AGEMAP database includes expression changes for 8,932 genes in 16 tissues as a function of age. We found great heterogeneity in the amount of transcriptional changes with age in different tissues. Some tissues displayed large transcriptional differences in old mice, suggesting that these tissues may contribute strongly to organismal decline. Other tissues showed few or no changes in expression with age, indicating strong levels of homeostasis throughout life. Based on the pattern of age-related transcriptional changes, we found that tissues could be classified into one of three aging processes: (1) a pattern common to neural tissues, (2) a pattern for vascular tissues, and (3) a pattern for steroid-responsive tissues. We observed that different tissues age in a coordinated fashion in individual mice, such that certain mice exhibit rapid aging, whereas others exhibit slow aging for multiple tissues. Finally, we compared the transcriptional profiles for aging in mice to those from humans, flies, and worms. We found that genes involved in the electron transport chain show common age regulation in all four species, indicating that these genes may be exceptionally good markers of aging. However, we saw no overall correlation of age regulation between mice and humans, suggesting that aging processes in mice and humans may be fundamentally different.
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In the citing paper
“Genome-wide gene expression studies during ageing of the nematode Caenorhabditis elegans, the common fruitfly (Drosophila melanogaster) and the brains of mice, rats, chimpanzees and humans have revealed a few broadly conserved functional categories of genes with age-dependent expression changes (6,19) (Table 2 and Fig. 2).”
From Neural mechanisms of ageing and cognitive decline (2010) · cites this paper as [19]
In the citing paper
“In particular, most of these studies provide evidence of reduced mitochondrial function during ageing.”
From Neural mechanisms of ageing and cognitive decline (2010) · cites this paper as [19]
In the citing paper
“Gene expression studies suggest that reduced expression of mitochondrial genes is a strongly conserved feature of ageing in organisms ranging from C. elegans to humans (6,19).”
From Neural mechanisms of ageing and cognitive decline (2010) · cites this paper as [19]
In the citing paper
“Another universally conserved feature of ageing is increased expression of genes involved in stress-response pathways.”
From Neural mechanisms of ageing and cognitive decline (2010) · cites this paper as [19]
In the citing paper
“Indeed, gene expression has been shown to change drastically during aging (for example, Baumgart et al., 2014; Budovskaya et al., 2008; Zahn et al., 2007).”
From The Aging Epigenome (2016) · cites this paper as Zahn et al., 2007
In the citing paper
“Aging-associated expression changes in genes associated with mitochondrial function, protein metabolism, and immune response have been identified in several tissues (173) and are consistent with some proposed mechanisms for late-onset neurodegenerative diseases (98, 113, 124, 166).”
From Unraveling a Multifactorial Late-Onset Disease: From Genetic Susceptibility to Disease Mechanisms for Age-Related Macular Degeneration (2009) · cites this paper as [173]
In the citing paper
“Large mouse studies have observed that the transcriptomes of different organs and cell types have starkly different ageing trajectories41,42,156.”
From Measuring biological age using omics data (2022) · cites this paper as [156]
In the citing paper
“As expected, this analysis verified previously identified top aging-related genes (such as Apod, B2m, C1qa, C4b, Ctss, Il33, Rpl8).”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [5]
In the citing paper
“By aggregating all our sequenced single cells and performing DGE analysis comparable to prior bulk sequencing studies, we validated many of the previously identified aging-related genes 5,7,37 and extended the list to include numerous additional gene signatures.”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [5]
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Metadata
- Authors
- Zahn, J. M., Poosala, S., Owen, A. B., Ingram, D. K., Lustig, A., Carter, A., Weeraratna, A. T., Taub, D. D., Gorospe, M., Mazan-Mamczarz, K., Lakatta, E. G., Boheler, K. R., Xu, X., Mattson, M. P., Falco, G., Ko, M. S. H., Schlessinger, D., Firman, J., Kummerfeld, S. K., Wood, W. H., Zonderman, A. B., Kim, S. K., & Becker, K. G.
- Affiliations
- Stanford Medicine (US)
- National Institutes of Health (US)
- Stanford University (US)
- Corresponding author
- Kim, Stuart K
- Becker, Kevin G
- ORCID
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2007-11-30
- Volume
- 3
- Issue
- 11
- Pages
- e201
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC2098796
- OpenAlex
- W2069049877
- Cited by
- 431
- References
- 39
- Citations by year
- 2026: 3 · 2025: 14 · 2024: 18 · 2023: 23 · 2022: 16 · 2021: 44 · 2020: 22 · 2019: 18 · 2018: 20 · 2017: 26 · 2016: 27 · 2015: 33 · 2014: 14 · 2013: 29 · 2012: 43
- FWCI
- 5.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Genetics (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Funders
- National Science Foundation (0604939)
- Ellison Medical Foundation
- National Institutes of Health
- National Institute on Aging (R01AG025941)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- gene expression database, mouse aging, age-related gene expression, tissue-specific aging, transcriptional heterogeneity, aging biomarkers
- MeSH terms
- Aging, Animals, Diptera, Gene Expression Regulation, Helminths, Humans, Organ Specificity, Species Specificity, Gene Expression Profiling, Databases, Genetic, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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