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Protein sequestration generates a flexible ultrasensitive response in a genetic network
Open accessAbstract
Ultrasensitive responses are crucial for cellular regulation. Protein sequestration, where an active protein is bound in an inactive complex by an inhibitor, can potentially generate ultrasensitivity. Here, in a synthetic genetic circuit in budding yeast, we show that sequestration of a basic leucine zipper transcription factor by a dominant-negative inhibitor converts a graded transcriptional response into a sharply ultrasensitive response, with apparent Hill coefficients up to 12. A simple quantitative model for this genetic network shows that both the threshold and the degree of ultrasensitivity depend upon the abundance of the inhibitor, exactly as we observed experimentally. The abundance of the inhibitor can be altered by simple mutation; thus, ultrasensitive responses mediated by protein sequestration are easily tuneable. Gene duplication of regulatory homodimers and loss-of-function mutations can create dominant negatives that sequester and inactivate the original regulator. The generation of flexible ultrasensitive responses is an unappreciated adaptive advantage that could explain the frequent evolutionary emergence of dominant negatives.
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Goldbeter, A., & Koshland, D. E. (1984) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(17)42619-6 · Cited by 230
A quantitative model of transcription factor–activated gene expression
Kim, H. D., & O'Shea, E. K. (2008) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1500 · Cited by 142
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Stanton, B. C., Nielsen, A. A. K., Tamsir, A., Clancy, K., Peterson, T., & Voigt, C. A. (2014) · Nature Chemical Biology · doi:10.1038/nchembio.1411 · Cited by 379
Interplay between gene expression noise and regulatory network architecture
Chalancon, G., Ravarani, C. N. J., Balaji, S., Martinez-Arias, A., Aravind, L., Jothi, R., & Babu, M. M. (2012) · Trends in Genetics · doi:10.1016/j.tig.2012.01.006 · Cited by 268
Biological underpinnings for lifelong learning machines
Kudithipudi, D., Aguilar-Simon, M., Babb, J., Bazhenov, M., Blackiston, D., Bongard, J., Brna, A. P., Chakravarthi Raja, S., Cheney, N., Clune, J., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00452-0 · Cited by 236
Metadata
- Authors
- Buchler, N. E., & Cross, F. R.
- Venue
- Molecular Systems Biology
- Published
- 2009-01
- Volume
- 5
- Issue
- 1
- Pages
- 272-272
- ISSN
- 1744-4292 (ISSN-L)
- OpenAlex
- W2135326887
- Cited by
- 236
- References
- 2
- FWCI
- 6.72
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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