Dynamics of the tumor suppressor p53 in single cells
How do living cells sense genotoxic stress and activate an appropriate response?
The tumor suppressor p53 is a central hub in the signaling network mediating the mammalian DNA damage response. It converts incoming signals into alternate cell fate decisions by changing the expression of hundreds of target genes. Previously, we used a combination of quantitative fluorescent time-lapse microscopy, computational data analysis and mathematical modeling to show that the p53 protein accumulates with oscillation-like dynamics upon induction of DNA double strand breaks. These dynamics are generated by an excitable network comprising positive and negative feedback, resulting in pulses of uniform amplitude and duration. The strength of the insult is mainly encoded in the number of pulses, similar to a digital signaling system. However, depending on the type of the stimulus and the activity of interacting pathways, the p53 system can generate other dynamics as well.
How is the cellular response to different forms of genotoxic stress determined by the dynamic activation of the p53 network?
To systematically measure network dynamics, fluorescent reporter cell lines for regulators and target genes of p53 were generated using CRISPR / Cas9 mediated genomic engineering. Using these cell lines, signaling dynamics and cellular outcomes upon treatment with chemotherapeutic drugs or other forms of genotoxic stress will be analyzed by time-lapse microscopy, automated image processing and computational data analysis. Perturbation of interacting pathways will be applied using small-molecule inhibitors or pathway agonists. From these data, hypotheses of the underlying molecular mechanisms will be generated and tested by applying appropriate molecular perturbations.
• One IREP student(s) can work on this project.
Pre-requisites or requirements for the project
For students in life sciences: Experience in basic molecular and cell biology techniques is required, experience in fluorescent microscopy would be helpful and some knowledge in image and data analysis using Matlab would be a plus.
For students in physics / mathematics / informatics: The analysis part of the project could also be performed by a student from the quantitative sciences with a strong interest in biological problems. Experience in Matlab programming would be required, knowledge of image analysis a plus.
Literature and preparation
- Batchelor, E., Loewer, A., & Lahav, G. (2009). The ups and downs of p53: understanding protein dynamics in single cells. Nature Reviews Cancer, 9(5), 371–377.
- Loewer, A., & Lahav, G. (2011). We are all individuals: causes and consequences of non-genetic heterogeneity in mammalian cells. Current Opinion in Genetics & Development, 21(6), 753–758.
- Purvis, J. E., & Lahav, G. (2013). Encoding and Decoding Cellular Information through Signaling Dynamics. Cell, 152(5), 945–956.
- Spiller, D. G., Wood, C. D., Rand, D. A., & White, M. R. H. (2010). Measurement of single-cell dynamics. Nature, 465(7299), 736–745
until the end of December 2020