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Title: A multi-level mechanical study of hypocotyl growth in the dark
Author: Chen, Yuanjie
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2019
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Dark-grown hypocotyls of Arabidopsis thaliana exhibit a striking wave of cell elongation which moves acropetally from the base to the tip of the organ over time. The combined effect of this wave of cell elongation is a pattern of organ elongation. It was aimed to understand the coordination of cellular growth and growth mechanisms through multi-level studies including modelling approaches, transcriptome analysis and hormone analysis. The first aim of this thesis was to describe the hypocotyl growth at a cellular level, including cell length and diameter measures, over a 72-hour period after germination. It was observed that the 'wave of growth' is exhibited only in the cell length dimension, not in diameter. The precise position and magnitude of the elongation wave was quantified as a foundation for subsequent aims. Microtubules were quantified and co-ordinated transverse alignments at inner walls was found to be associated with cell growth rate. In the second aim of this thesis a dynamic, intrinsic, model was built based on the physical factors controlling cell elongation, using a bottom-up approach. Using a chain of cell units, each modelled with a modified Lockhart model, hypocotyl elongation in silico mirrored experimental data. The model successfully simulated behaviours of cell size, turgor pressure and yield stress over time and it responded to simulated parameter changes (representing physical factors) reasonably well. The model performance was compared to experimental manipulations. A concept of 'bond energy distribution' was introduced to the model in relation to yield stress, and it suggested that a change in the cell wall structure, or bond distribution, can be an efficient way of controlling cell growth in comparison to change in physical factors. The model also implied that a quantitative 'chemical signal' may exist and can be involved in the initiation of cell growth and thus the acropetal wave. For the next aim, an empirical model of hypocotyl cell growth was built based on data of cell sizes up to 72 hours post germination. Using data fitting and parameter extrapolation, the model predicted the progression of cell sizes until 196 hours post germination, when the hypocotyl elongation was terminating. The prediction fitted well with the measured hypocotyl growth at organ level. The Probit function had the best performance among the three sigmoidal functions selected for fitting and parameter extrapolation, which is a model describing the transition from cellular to organ level growth. The fourth aim of this thesis was to investigate which genes, related to physical growth parameters, were related to cell growth. An RNAseq experiment was conducted and analysed which yielded differentially expressed genes from slow and fast-elongating regions of the hypocotyl (non-wave vs 4 wave) at three time points (24/36/48HPG). Several groups of genes that were involved in cell wall modification and hormones were studied in detail and those with differential expression within the wave were identified. The final aim of this thesis involved investigating a hormonal signal for the acropetal wave and cell growth initiation. The intrinsic cell model in Aim 2 indicated the need for a growth signal to start cell elongation. Hormones are strong candidates for growth signals and the transcriptome analysis in Aim 4 indicated that gibberellin may be a good candidate. GA levels were examined in the hypocotyl using the nlsGPS1 GA-biosensor. It was found that the level of sensor emission ratio qualitatively correlated well with the cellular growth rates, indicating the existence of global control on cell growth through chemical signals.
Supervisor: Ahnert, Sebastian ; Braybrook, Siobhan ; Jones, Alexendar Sponsor: Cambridge Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: Modelling ; Hypocotyl ; Arabidopsis ; Mechanical Growth ; RNA-seq ; Growth models