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Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways
Open accessAbstract
Because mouse models play a crucial role in biomedical research related to the human nervous system, understanding the similarities and differences between mouse and human brain is of fundamental importance. Studies comparing transcription in human and mouse have come to varied conclusions, in part because of their relatively small sample sizes or underpowered methodologies. To better characterize gene expression differences between mouse and human, we took a systems-biology approach by using weighted gene coexpression network analysis on more than 1,000 microarrays from brain. We find that global network properties of the brain transcriptome are highly preserved between species. Furthermore, all modules of highly coexpressed genes identified in mouse were identified in human, with those related to conserved cellular functions showing the strongest between-species preservation. Modules corresponding to glial and neuronal cells were sufficiently preserved between mouse and human to permit identification of cross species cell-class marker genes. We also identify several robust human-specific modules, including one strongly correlated with measures of Alzheimer disease progression across multiple data sets, whose hubs are poorly-characterized genes likely involved in Alzheimer disease. We present multiple lines of evidence suggesting links between neurodegenerative disease and glial cell types in human, including human-specific correlation of presenilin-1 with oligodendrocyte markers, and significant enrichment for known neurodegenerative disease genes in microglial modules. Together, this work identifies convergent and divergent pathways in mouse and human, and provides a systematic framework that will be useful for understanding the applicability of mouse models for human brain disorders.
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“This work also found major differences in dementia susceptibility genes between humans and mice, potentially identifying why some mouse models might not recapitulate human neuropathology.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [150]
In the citing paper
“They subsequently150 incorporated more than 1,000 microarrays from mouse models of Alzheimer disease and human patients with Alzheimer disease from public databases to reproduce and extend these results, identifying additional co-expression modules that are related to mitochondrial dysfunction and synaptic plasticity.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [150]
In the citing paper
“Applied to the entire 911 sample set from Brain 1, genes were grouped into well-defined co-expression modules with specific anatomical distributions (Fig. 3a, b), consistent with previous studies in brain tissues18, 19.”
From An anatomically comprehensive atlas of the adult human brain transcriptome (2012) · cites this paper as [19]
In the citing paper
“Modules corresponding to broad neural cell types also showed highly significant preservation compared to a previous study using human brain samples (ref. 19 and data not shown).”
From An anatomically comprehensive atlas of the adult human brain transcriptome (2012) · cites this paper as [19]
In the citing paper
“a number of important studies have compared gene expression in microglia from mice and humans, and while they have shown a significant overlap between the transcriptomes of the two species, they have also seen a number of genes are expressed selectively more abundantly in human microglia”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Miller et al., 2010
In the citing paper
“a meta-analysis of microarray studies which showed better preservation of neuronal gene co-expression compared to glial and disease-related genes36”
From Canonical genetic signatures of the adult human brain (2015) · cites this paper as [36]
In the citing paper
“Microarray studies have also revealed distinct gene expression profiles and coexpression networks associated with different human brain regions, major cell types, and cellular processes (Johnson et al., 2009; Miller et al., 2010; Oldham et al., 2006; Oldham et al., 2008).”
From Large-Scale Cellular-Resolution Gene Profiling in Human Neocortex Reveals Species-Specific Molecular Signatures (2012) · cites this paper as Miller et al., 2010
Citations
Cites
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Self-citationGene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Self-citationGenome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
Hierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224
Triple-Transgenic Model of Alzheimer’s Disease with Plaques and Tangles
Oddo, S., Caccamo, A., Shepherd, J. D., Murphy, M. P., Golde, T. E., Kayed, R., Metherate, R., Mattson, M. P., Akbari, Y., & LaFerla, F. M. (2003) · Neuron · doi:10.1016/s0896-6273(03)00434-3 · Cited by 4,126
A gene atlas of the mouse and human protein-encoding transcriptomes
Su, A. I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K. A., Block, D., Zhang, J., Soden, R., Hayakawa, M., Kreiman, G., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400782101 · Cited by 3,475
Human Protein Reference Database--2009 update
Prasad, T. S. K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, R. B., Venugopal, A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn892 · Cited by 3,252
Cahoy, J. D., Emery, B., Kaushal, A., Foo, L. C., Zamanian, J. L., Christopherson, K. S., Xing, Y., Lubischer, J. L., Krieg, P. A., Krupenko, S. A., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4178-07.2008 · Cited by 3,129
King, M.-C., & Wilson, A. C. (1975) · Science · doi:10.1126/science.1090005 · Cited by 2,815
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Bezverbnaya, K., Mathews, A., Sidhu, J., Helsen, C. W., & Bramson, J. L. (2016) · Immunotherapy · doi:10.2217/imt-2016-0103 · Cited by 9
Uddin, R., & Singh, S. M. (2017) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2017.00075 · Cited by 9
Sahu, J., Panda, D., Baruah, G., Patar, L., Sen, P., Borah, B. K., & Modi, M. K. (2019) · Gene · doi:10.1016/j.gene.2019.02.063 · Cited by 9
Malamon, J. S., & Kriete, A. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00228 · Cited by 9
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Mathur, T., Kumar, A., Flanagan, J. M., & Jain, A. (2022) · Current Protocols · doi:10.1002/cpz1.582 · Cited by 8
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Bao, H., Chen, X., Xiao, Q., Yang, S. S., Wu, S., Wang, X., Wu, X., Ding, K., & Shao, Y. W. (2022) · Genomics · doi:10.1016/j.ygeno.2022.110504 · Cited by 7
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Horvath, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8819-5_5 · Cited by 7
Self-citationGuo, C., Liu, X.-J., Cheng, Z.-X., Liu, Y., Li, H., & Peng, X. (2013) · Molecular BioSystems · doi:10.1039/c3mb70404a · Cited by 7
Ma, C., Zhou, Y., & Huang, S. (2011) · BioMed Research International · doi:10.1155/2011/926407 · Cited by 7
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Li, Y., Chen, H., Jiang, X., Li, X., Lv, J., Peng, H., Tsien, J. Z., & Liu, T. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1460-9 · Cited by 7
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Feng, X., Zhang, M., Meng, J., Wang, Y., Liu, Y., Liang, C. Y., & Fan, S. (2020) · Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics · doi:10.3727/096504020x15791676105394 · Cited by 7
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Yang, Z., Jiang, Y.-H., Ma, C., Silvestri, G., Bosinger, S. E., Li, B.-L., Jong, A., Zhou, Y., & Huang, S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156170 · Cited by 7
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Timonidis, N., Llera, A., & Tiesinga, P. H. E. (2021) · Neuroinformatics · doi:10.1007/s12021-021-09511-0 · Cited by 5
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Vogel Ciernia, A., Laufer, B. I., Dunaway, K. W., Hwang, H., Mordaunt, C. E., Coulson, R. L., Yasui, D. H., & LaSalle, J. M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/270827 · Cited by 6
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Bellini-Leite, S. D. C., & Pereira, A. (2013) · Journal of Cognitive Science · doi:10.17791/jcs.2013.14.4.335 · Cited by 5
Inglese, P., Strittmatter, N., Doria, L., Mróz, A., Speller, A. V. M., Poynter, L., Dannhorn, A., Kudo, H., Mirnezami, R., Goldin, R. D., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/230052 · Cited by 4
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Lian, B., Zhang, H., Wang, T., Wang, Y., Shang, X., Aziz, N. A., & Hu, J. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf309 · Cited by 3
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Lundin, A., Ricchiuto, P., Clausen, M., Hicks, R., Falk, A., & Herland, A. (2020) · Advanced Biosystems · doi:10.1002/adbi.201900226 · Cited by 3
Daylan, A. E. C., & Dinçer, P. (2016) · Neuromuscular Disorders · doi:10.1016/j.nmd.2016.10.011 · Cited by 3
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Pouladi, N., Sallari, R. C., & Moore, J. H. (2014) · BioData Mining · doi:10.1186/1756-0381-7-27 · Cited by 2
Ziemian, S. N., Antoinette, A. Y., Witkowski, A. M., Otero, M., Goldring, S. R., Goldring, M. B., & van der Meulen, M. C. H. (2025) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2025.01.006 · Cited by 2
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LUKENS, L. N., & Downs, G. S. (2012) · Springer eBooks · doi:10.1007/978-1-4614-0920-5_2 · Cited by 2
Yu-Qi, Z., Gong-Hua, L., & Huang, J. (2013) · Zoological Research · doi:10.3724/sp.j.1141.2013.e02e35 · Cited by 2
Wang, G.-Z., & Konopka, G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039686 · Cited by 2
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Zhang, J., & Ju, S. (2019) · IET Systems Biology · doi:10.1049/iet-syb.2019.0060 · Cited by 2
Yang, R., Daigle, J., Muhie Bernie, J., Hammamieh Seid, Y., Rasha, J., Marti, P., Linda, D., & Francis, J. (2013) · Defense Technical Information Center (DTIC) · doi:10.21236/ada596945 · Cited by 2
Krizay, D. K., Goldstein, D. B., & Boland, M. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.11.519959 · Cited by 2
Xiao, D., Wang, R., Zhang, P.-D., Riyanti, Zhao, L., & Song, N. (2025) · Environmental Research · doi:10.1016/j.envres.2025.121922 · Cited by 1
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Han, Z., Yang, C., He, H., Huang, T., Yin, Q., Tian, G., Wu, Y., Hu, W., Lu, L., Bajpai, A. K., et al. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.0252-24.2024 · Cited by 1
Human neural organoid modeling of diffuse midline glioma captures the complexity of patient tumors
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Cöllen, E., Wolfbeisz, C., Leisner, H., Grillberger, K., Kormann, J., Tanaskov, Y., Dreser, N., Karreman, C., Härtung, T., Ecker, G. F., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27135902 · Cited by 1
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Metadata
- Authors
- Miller, J. A., Horvath, S., & Geschwind, D. H.
- Affiliations
- University of California, Los Angeles (US)
- Departments of cHuman Genetics and
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2010-06-25
- Volume
- 107
- Issue
- 28
- Pages
- 12698-12703
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2906579
- OpenAlex
- W2139511828
- Cited by
- 480
- References
- 52
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 27 · 2023: 20 · 2022: 35 · 2021: 31 · 2020: 34 · 2019: 47 · 2018: 35 · 2017: 39 · 2016: 35 · 2015: 36 · 2014: 26 · 2013: 35 · 2012: 33
- FWCI
- 11.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (HL028481)
- National Institute on Aging (F31AG031649, U01AG016976, R01 AG26938-05, R01AG026938)
- National Heart, Lung, and Blood Institute (P01HL028481)
- National Institute of Mental Health (R37MH060233, R37 MH 60233-09S1)
- National Institute of Allergy and Infectious Diseases (U19AI063603)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- Alzheimer's disease, gene co-expression network, cell type markers, glial cells, microglia, oligodendrocytes, presenilin 1, network hubs
- MeSH terms
- Alzheimer Disease, Animals, Brain, Humans, Male, Gene Expression Profiling, Systems Biology, Mice, Gene Regulatory Networks, Presenilin-1
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