Escherichia coli aging from a single cell
E.coli are prokaryotes that show aging and rejuvenation. Evidences show that damage allocation among daughter cells can explain the aging and rejuvenation pattern in an E.coli lineage. However, population aging originated from cellular aging. And in E.coli, there is not enough study on the molecular dynamics that are necessary for a single cell to create different daughter cells. This is the focus of this thesis. Namely, single cell experiments and single cell biophysic models are built to explain cellular aging in E.coli. And these studies are aimed to provide informative connections between cellular aging and population aging. The age difference between cell poles is established physically by the division pattern of E.coli. With one pole being synthesed anew from most recent division, two poles of E.coli is inevitably different by the time they each exist. And proteins that harbor at the poles will have different age and wear and tear. On the other hand, given the division pattern, natural selection is optimizing the physical parameters and dynamics of proteins inside cell, so their intracellular distribution will result in the optimal intercellular distribution among the daughters and become beneficial to population fitness. Two categories of proteins are of greatest interest. First is damaged protein, usually in the form of protein aggregate, with its consequence being studied but dynamics obsure. Second is fresh functional protein and repair protein. Up to the starting of this thesis, no study has contributed to this topic. The chapter 1 will investigate the fresh protein distribution in line with cellular aging. Chapter 2 is a model tackling the damage dynamics inside single cell. Chapter 3 is discussing more conceptually and fundamentally, the evolutionary origin of cell division. In chapter 1, a constitutively expressing green fluorescent protein (GFP) gene is inserted into E.coli genome under a stable promoter, and its intracellular and intercellular distribution is observed and discussed. We also invented a protocol of deconvoluting fluorescent image of E.coli colony. GFP here is representing a group of fresh proteins that are free of damage. The observation of GFP enriches in new pole and new daughters are in consistent with the higher growth rate of new pole and new lineage. The representation of GFP is also appreciated by its relative small molecular weight and fast diffusion. This makes its localization most likely be a passive visualization of free intracellular space instead of an active and protein-specific arrangement. The fact of lack of physiological function of GFP in E.coli also supports the enrichment being passive, which indicates a baseline enrichment level of functional but invisible proteins that contribute to real fitness. In more detailed study, we find the free cellular space revealed by GFP can be predicted qualitatively by the age of the pole. The fact that younger pole has more free space leads to the speculation of accumulation of aged or damaged protein on the other end of cell. And therefore, fresh proteins are spatially excluded by damaged protein, from which polarity of cell is established and physiological distinction between daughters are maximized. Cellular polarity will subsequently be magnified into standing fitness variation of population, and we found the age can explain 37% of total variance of GFP distribution. This polarity is created by self-organisation based on size of molecules, and cannot be realized without damage aggregation. Given the possible energy cost of sorting and rearranging each specific damage or fresh protein of all sizes, the aggregation of damage allows damage and fresh protein to be organised physically by their size with no energy involved. The damage aggregation- disaggregation dynamics will be explored in Chapter 2. In chapter 2, classical view of damage distribution causinig cellular aging is examined by a biophysical and individual based finite population model. Damage is hypothesed to have been actively transported to old pole, or have been excluded from nucleoid because of aggregation. The first hypothesis is rejected because the resulting exponential-shape distribution is conflicting the observed damage enrichment of both poles. However, nucleoid exclusion hypothesis have not been tested and parameterized in an elongating E.coli cell. The key parameters in this picture, and are certainly parameters under natural selection, are the size of damage and the elongation regime of E.coli. Damage particles originated from wear and tear of fresh and functional protein. When protein is damaged, the size will most likely keep similar. In this way, the high diffusion rate of protein will cause damage to have uniform distribution inside cell, and no polarity can be built without extra mechanism or energy. The evolution of protein aggregation solves this problem by allowing damaged protein to bind and form larger aggregate. The larger size will be physically excluded by nucleoid and eventually reside in the cell poles. And when cell divides, new pole is created damage free by the division of nucleoid, and will take time for new aggregate to colonize. And damage polarity is established automatically. Therefore, it is important that the size of aggregate is larger than the mesh size of nucleoid and larger than the space between nucleoid and cell wall. Once resides into the pole, the aggregate will keep growing by merging with more and more free flowing new damages, and will eventually cause old lineage die from damage overload, if not saved by disaggregation or elongation. Disaggregation will efficiently control the size of damage aggregate by releasing repaired protein out from aggregate, and once aggregation –disaggregation equilibrium is reached, the size of aggregate will keep constant. In this way, disaggregation saves the old lineage by compromising the polarity of cell. And a moderate level of disaggregation is necessary. In our model, the free damage size after parameterization is close to the size of a ribosome. Therefore free damage is small enough to travel through nucleoid between poles. And it takes aggregation of 3 free damages to be excluded from nucleoid and establish damage polarity. The disaggregation probability from model is 0.1. Since damage polarity is established passively, it is not robust under changing level of external damage, which occurs randomly across a cell. It explains why both experiment and model produce symmetry when external damage increases. Elongation, in general, is an automatic dilution of existing damage, and will further promote elongation by alleviating detrimental effect of damage. And fast elongation will in turn lead to less reception of new external damage. There is a prominent positive feedback between fast/slow elongation and less/more amount of damage. Subcellular elongation pattern has been measured by transient cell wall labeling and monitoring local dilution. Our observation of new pole elongates significantly fast than old pole means elongation happened locally and the elongation –dilution feedback will also apply subcellularly. This results in more polarized damage distribution of a cell once initial damage/fresh protein polarity is established. In our model, each cell is represented by multiple one dimension compartments. And each compartment has its unique elongation probability, due to local damage, to reproduce local elongation. After parameterization, the model can reproduce the elongation rate relation between mother and old and new daughters, including stochastic attractor behavior at the equilibrium point where mother’s doubling time equals one of her daughter’s. This model is developed on the assumption of exponential cell growth from self-replicating compartments (growth units). In chapter 3, an in-detailed analysis is performed to quantitatively describe their behavior and consequences. When measured in better time resolution, E.coli elongation performs accerlerating multi-linear phases of growth. Each linear phase can be realized as constant biomass production of a certain growth units, with the shift to a faster phase being the event of a new unit start functioning. To keep up with cell size, the growth unit number needs to double as the cell length doubles. Considerinig most cells perform two linear phases during doubling, the growth unit should start from number of 2, advance to 3, and divide right at the time it reaches 4. The fact of observing the third unit functioning also indicates the mixing of products from 2 existing units (cooperation). This mechanism gives cell that contains more growth units fitness advantage for their elongation span accomadates more rounds of cooperation. And a cell with short length has lower fitness. However, our elongation rate data at long cell length indicates slowing down of accerleration across phase shift, which can be explained by inefficient cooperation at new cell pole (c.f. chapter 1) caused by long distance between poles. Therefore, growth units not only result in multi-linear cell elongation, but their cooperation produces an optimal cell size and explains cell division. Chapter 3 now has complete conceptual projection, and there are still controls and tests need to be performed.