As the ultimate source of new genetic variation, mutations fuel evolutionary change. Mutation rate is thus a key parameter determining how fast populations adapt to new environments, or even whether they survive a severe environmental challenge, such as antibiotic treatment in the case of bacteria. However, since most mutations are deleterious, organisms have multiple mechanisms to avoid mutagenesis. DNA damage response and mismatch repair systems have been particularly well studied in bacteria. With advances in fluorescent protein labelling and high-resolution microscopy, it has recently become possible to visualise mutagenesis within individual bacterial cells. These experiments have revealed cell-to-cell variation in the timing and intensity of responses to DNA damage when bacteria are exposed to environmental stressors, such as antibiotics or other chemicals . This variability arises even in genetically identical cells in a common environment, due to stochasticity in gene expression and distribution of proteins upon cell division. Therefore, individual cells could have different propensities to survive and mutate under environmental stress.
Despite this growing source of data, the significance of this observed variation for bacterial evolution has hardly yet been explored. The vast majority of population genetic models assume a uniform mutation rate. Initial theoretical work relaxing this assumption suggested that variation among individuals  and responsiveness to the environment  could promote adaptations involving multiple mutations. However, these population-level models were disconnected from the underlying mechanisms, and neglected some important features of bacterial DNA damage responses. For instance, mutation rates should show an intermediate level of correlation from mother to daughter cells due to inheritance of cell contents. Furthermore, mutation rate may be either positively or negatively correlated to cell viability, depending on the particular DNA damage response (“tolerance” vs. “repair”).
This project will explore the evolutionary consequences of cell-to-cell variability in mutagenesis, using data analysis, mathematical modelling, and/or simulations based on empirical observations in bacteria. Potential questions include:
• Under what circumstances can cell-to-cell variation help a bacterial population to survive environmental challenges?
• When is “tolerance” versus “repair” the best response to DNA damage?
• What is the magnitude of mutation rate variation and its correlations among cells?
Depending on student background and interests, these questions could be addressed by:
a) developing a model accounting for general features of DNA damage responses in bacteria, and deriving predictions through mathematical analysis;
b) developing a multi-scale model describing a particular intracellular response pathway and its relationship to cell population dynamics, to be explored through computational simulations;
c) statistically analysing single-cell microscopy data.
Students with diverse backgrounds (e.g. mathematics, physics, statistics, biology), who have strong quantitative skills and interest in bacterial evolution, are encouraged to apply. The student will have the opportunity to deepen their understanding of both bacterial cell biology and evolutionary theory, and to gain skills in mathematical modelling, statistics, computation, and communication in an interdisciplinary setting.
The project will be supervised by Dr. Helen Alexander (Institute of Evolutionary Biology), Prof. Meriem El Karoui (Institute of Cell Biology; Centre for Synthetic and Systems Biology), and Dr. Tibor Antal (School of Mathematics). The student will have the opportunity to interact with lab members studying bacterial evolution, molecular and cell biology; and more broadly with a diverse group of theoretical, experimental, and field biologists, and mathematicians (e.g. through the Mathematical Biology Seminar series). Informal enquiries to Helen Alexander are encouraged.
Supervisor web pages:
Helen Alexander: https://scholar.google.co.uk/citations?user=jRW2Z7QAAAAJ
Meriem El Karoui: http://www.elkarouilab.fr
Tibor Antal: https://www.maths.ed.ac.uk/~antal/index.html
1. Uphoff, S. “Real-time dynamics of mutagenesis reveal the chronology of DNA repair and damage tolerance responses in single cells”, PNAS 115:E6516-E6525 (2018). https://www.pnas.org/content/115/28/E6516
2. Alexander, H. K. et al. “Population heterogeneity in mutation rate increases the frequency of higher-order mutants and reduces long-term mutation load”, Molec Biol Evol 34:419-436 (2018). https://academic.oup.com/mbe/article/34/2/419/2528250
3. Ram, Y. and Hadany, L. “Stress-induced mutagenesis and complex adaptation”, Proc R Soc B 281:20141025 (2014). https://royalsocietypublishing.org/doi/full/10.1098/rspb.2014.1025