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Systems biology of autosomal dominant polycystic kidney disease (ADPKD): computational identification of gene expression pathways and integrated regulatory networks
Abstract
To elucidate the molecular pathways that modulate renal cyst growth in ADPKD, we performed global gene profiling on cysts of different size (<1 ml, n = 5; 10-20 ml, n = 5; >50 ml, n = 3) and minimally cystic tissue (MCT, n = 5) from five PKD1 human polycystic kidneys using Affymetrix HG-U133 Plus 2.0 arrays. We used gene set enrichment analysis to identify overrepresented signaling pathways and key transcription factors (TFs) between cysts and MCT. We found down-regulation of kidney epithelial restricted genes (e.g. nephron segment-specific markers and cilia-associated cystic genes such as HNF1B, PKHD1, IFT88 and CYS1) in the renal cysts. On the other hand, PKD1 cysts displayed a rich profile of gene sets associated with renal development, mitogen-mediated proliferation, cell cycle progression, epithelial-mesenchymal transition, hypoxia, aging and immune/inflammatory responses. Notably, our data suggest that up-regulation of Wnt/beta-catenin, pleiotropic growth factor/receptor tyrosine kinase (e.g. IGF/IGF1R, FGF/FGFR, EGF/EGFR, VEGF/VEGFR), G-protein-coupled receptor (e.g. PTGER2) signaling was associated with renal cystic growth. By integrating these pathways with a number of dysregulated networks of TFs (e.g. SRF, MYC, E2F1, CREB1, LEF1, TCF7, HNF1B/ HNF1A and HNF4A), our data suggest that epithelial dedifferentiation accompanied by aberrant activation and cross-talk of specific signaling pathways may be required for PKD1 cyst growth and disease progression. Pharmacological modulation of some of these signaling pathways may provide a potential therapeutic strategy for ADPKD.
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., De Paepe, A., & Speleman, F. (2002) · Genome Biology · doi:10.1186/gb-2002-3-7-research0034 · Cited by 20,038
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F., Ben Miled, S., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,803
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Irizarry, R. A. (2003) · Biostatistics · doi:10.1093/biostatistics/4.2.249 · Cited by 10,603
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-5-p3 · Cited by 9,327
Cell Signaling by Receptor Tyrosine Kinases
Lemmon, M. A., & Schlessinger, J. (2010) · Cell · doi:10.1016/j.cell.2010.06.011 · Cited by 7,929
Cell Signaling by Receptor Tyrosine Kinases
Schlessinger, J. (2000) · Cell · doi:10.1016/s0092-8674(00)00114-8 · Cited by 3,774
Suppression of Aging in Mice by the Hormone Klotho
Kurosu, H., Yamamoto, M., Clark, J. D., Pastor, J. V., Nandi, A., Gurnani, P., McGuinness, O. P., Chikuda, H., Yamaguchi, M., Kawaguchi, H., et al. (2005) · Science · doi:10.1126/science.1112766 · Cited by 1,935
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Metadata
- Authors
- Song, X., Di Giovanni, V., He, N., Wang, K., Ingram, A., Rosenblum, N. D., & Pei, Y.
- Venue
- Human Molecular Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-04-03
- Volume
- 18
- Issue
- 13
- Pages
- 2328-2343
- ISSN
- 0964-6906 (ISSN-L)
- 1460-2083
- OpenAlex
- W2124577658
- Cited by
- 256
- References
- 55
- FWCI
- 6.77
- Type
- journal article
- Language
- en
- Open access
- closed
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- Song, X., Di Giovanni, V., He, N., Wang, K., Ingram, A., Rosenblum, N. D., & Pei, Y. (2009). Systems biology of autosomal dominant polycystic kidney disease (ADPKD): computational identification of gene expression pathways and integrated regulatory networks. Human Molecular Genetics. https://doi.org/10.1093/hmg/ddp165
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