The coupling between the circadian clock, metabolism and the environment: a systems’ approach

Secondary Supervisor(s):Professor Orkun Soyer(University of Warwick)

University of Registration:University of Warwick

BBSRC Research Themes:Understanding the Rules of Life (Microbiology)

Project Outline

The circadian clock is a gene circuit that generates 24-h cycles of gene expression in many organisms in anticipation of daily cycles of sunlight and temperature. In the cyanobacterium S. elongatus, which has the simplest clock we know of, most of the genome is under clock control. The cyanobacterial clock regulates many processes inside the cell, including metabolism, photosynthesis or the cell cycle. While the clock anticipates environmental change, it is also influenced by a variety of signalling cues. Light and temperature transitions reset the clock state, but the effect of these signals on clock proteins is relayed through more fundamental quantities, such as redox metabolites and ATP/ADP ratios. These cues can be said to characterise the global energy state of the cell, but on the other hand the clock controls expression of most photosynthetic and metabolic proteins. This dynamic and bi-directional coupling between the circadian clock and metabolism likely holds the key to understanding the evolutionary significance of clocks at the most fundamental level (1, 2).

General Aim and Methodology

The aim of this project is to characterise the cross-talk between the circadian clock and metabolism and understand how this cross-talk coordinates cellular growth and division under different environmental scenarios. Using fluorescent reporters, the joint dynamics of the clock and metabolic enzymes will be measured using time-lapse microscopy (3) and microfluidics for thousands of individual cells. Cellular energetics (e.g. ATP/ADP ratios) will be monitored through ratiometric fluorescent reporters. These measurements will provide a comprehensive timeline of physiological changes throughout the 24-h cycle and of how cells allocate metabolic resources to time growth, cell division and DNA replication, which will allow us to create an integrated model of the coupling between the clock and metabolism. We will test this model using a combination of genetic and environmental perturbations in microfluidic devices. These results will allow us to understand why clocks offer a selective advantage, and thus reveal a mechanistic ‘logic’ that can be applied to other systems.

References

A. M. Puszynska, E. K. O’Shea, Switching of metabolic programs in response to light availability is an essential function of the cyanobacterial circadian output pathway. eLife 6, e23210 (2017).

G. Pattanayak, M. J. Rust, The Cyanobacterial Clock and Metabolism. Curr Opin Microbiol 18, 90–95 (2014).

B. M. Martins, A. K. Das, L. Antunes, J. C. Locke, Frequency doubling in the cyanobacterial circadian clock. Molecular Systems Biology12, 896 (2016).

Techniques

• Single-cell time-lapse microscopy and microfluidics.
• Computational work including quantitative image analysis, time-series analysis, writing short routines for automated microscopy acquisition and possibly modelling (depending on the background and interests of the student).
• Microbiology and molecular genetics.

A mathematical model of cell cycle regulation by the cyanobacterial clock: integrating quantitative data, machine learning and stochastic models

Secondary Supervisor(s):Professor Daniel Hebenstreit (University of Warwick)

University of Registration:University of Warwick

BBSRC Research Themes:Understanding the Rules of Life (Microbiology)

Project Outline

Circadian clocks are regulatory networks that generate 24-h rhythms in gene expression and other cellular processes in many organisms in anticipation of daily environmental cycles. The simplest and best characterised clock in nature is that of the cyanobacterium S. elongatus, which consists of only 3 proteins – KaiA, KaiB and KaiC – forming a post-translational loop. The cyanobacterial clock regulates many processes inside the cell, including metabolism, photosynthesis, cell growth and the cell division cycle. There is extensive evidence that the distribution of cell division times has modes at certain clock phases, with consequences for cell size and cell cycle homeostasis (1). While phenomenological models of this clock-cell cycle coupling have been proposed (2,3), a mechanistic understanding remains elusive. Clock modulation of the temporal and spatial dynamics of specific ‘cell division proteins’ is a likely mechanism. Homologous molecular mechanisms have been shown to rate limit cell size, hence cell division, in other bacteria and there is evidence of clock output signals regulating the divisome (4). The cell division cycle and the circadian clock are two ubiquitous intracellular oscillators exhibiting a degree of synchrony – yet not always cycling at the same frequency – across all circadian systems. A mechanistic model of this coupling is lacking. The well characterised cyanobacterial clock and the single-cell data our group has been obtaining offer us an opportunity to build the first model of this kind and reveal rules and principles that could apply to other organisms, including plant crops and humans.

General Aim and Methodology

This project will use a multidisciplinary approach combining integrating single-cell microscopy data with computational analysis (analysis of time-series, stochastic modelling, and machine learning and AI-driven approaches) to shed light on a complex biological system. The aim of this project is to clarify how the circadian clock regulates cell division by creating a data-driven, mechanistic model of clock-cell cycle coupling actions under various environmental conditions and genetic perturbations. We will take advantage of extensive pre-existing and new data generated in the lab, where the spatial and temporal dynamics of fluorescently tagged proteins can be measured at single-cell level. We will use information theory and machine learning/AI-based approaches to identify the key characteristics of cell physiology and protein dynamics that best predict the coupling between the clock and the cell division cycle. We will then use these lessons to build upon and improve on previous models of the circadian clock (5) and cell division (3). Our final objective is to build an integrative mechanistic model that explains the origin of correlations between clock state, protein expression and cell growth-division properties, and generates new predictions and hypotheses that could be tested under environmental and genetic perturbations. These results will allow us to understand how and why these two fundamental oscillators, the circadian clock and the cell cycle, are interlinked and thus reveal a mechanistic ‘logic’ that can be applied to other systems.

References

Techniques

• Quantitative image and time-series analysis.

• Mathematical modelling and dynamical systems.
• Information theory.
• Machine learning and AI