Being sessile by nature plants are continuously challenged by biotic and abiotic stress factors. At the cellular level, different stimuli are perceived and translated to the desired response. In order to achieve this, signal transduction cascades have to be interlinked. Complex networks of downstream targets as well as positive and negative regulatory elements are essential for proper signal transduction. This often complicates analysis of signal transduction cascades via genetic approaches as a mutation in one gene results in a pleiotropic phenotype. Pathway components can be placed in the signal transduction cascade based on genetics as well as biochemical interactions between proteins. This results in a signal transduction network which is based on Boolean logics; either the gene is there and is functional (on) or it’s mutated and not functional (off). In such a genetic scheme, intermediate conditions and the effect of concentration on pathway components is not taken into account. Also, the effect of intermediate or transient activation states on signal transduction pathways is rarely included. In principle all proteins in a signal transduction network obey mass action laws suggesting that reaction rate and as a consequence the output of the signal depends on the concentration of the reactants. In addition, signals can be subjected to negative feedback thereby resulting in a signal that attenuates itself to maintain cellular homeostasis, or only responds to a stimulus temporarily and only when required. At the cellular level, the cell has to decipher all stimuli to enable the desired integrated cellular response. In this respect, concentration or amplitudes matter very much as the plant must not respond to background noise. Hence a cellular response will only be induced when a signal is above a certain threshold resulting in “switch like” behaviour of the system. Mathematical modelling can help to visualise and explain the temporal and concentration effects of pathway components on the output of signal transduction cascades. In order to do so, the signal transduction cascade needs to be well described with a clear and measurable response. Obviously, to know how receptor concentration affects the signalling output one has to know its numbers, and this was the starting point of the work described in this thesis. The final challenge was to describe the modulatory effect of SERK co-receptors on BR signalling. For this, SERK mediated BRI1 signalling was incorporated in a mathematical model that describes root growth and hypocotyl elongation based on the BRI1 receptor activity.
In Chapter 1 the brassinosteroid signalling pathway as well as the role of SERK co-receptors on BRI1 mediated signalling is described. BRs are perceived by the plasma membrane localised Brassinosteroid insensitive 1 (BRI1) receptor. For its signalling, BRI1 completely depends on the presence of non-ligand binding co-receptors of the somatic embryogenesis receptor like kinase (SERK) co-receptor family. An added complexity is that SERK co-receptors associate with different main ligand perceiving receptors thereby affecting multiple signalling pathways simultaneously. Therefore, it is important to know how SERK co-receptors modulate the output of the main ligand perceiving receptor and how SERK co-receptors are distributed between the signal transduction cascades. The BRI1 signal transduction pathway is one of the best understood signal transduction cascades in Arabidopsis with clearly described ligands and associated phenotypes. For this reason, the focus of this study was on how SERK co-receptors affect BRI1 mediated signalling quantitatively using a mathematical modelling approach. This requires knowledge on the concentration of the main ligand perceiving receptor, SERK co-receptor and ligand levels. Since the BRI1 and SERK co-receptor concentration was unknown we set out to quantify the number of receptors in a cell. In Chapter 2 a confocal microscopy based method is described that enables quantification of BRI1, SERK1 and SERK3 in planta. The number of BRI1 receptor molecules in root epidermal cells ranges from 22,000 in the meristem to 80,000 in the maturation zone. However, when taking into account differences in cell size, the root meristem cells have the same receptor density which reduces significantly in the maturation zone. The root meristem cells are thought to be most active in BR signalling, suggesting that receptor density rather than total number of BRI1 receptors affects the sensitivity of a cell for BRs.
The next question is, how the physiological response of the cell depends on both ligand stimulation of the receptor and on ligand concentration. To address this, a mathematical modelling approach was employed where the receptor - ligand concentrations were coupled to root growth and hypocotyl elongation as a downstream physiological readout for BR signalling (Chapter 3). Based on the BRI1 receptor activity the model faithfully predicts root growth as observed in bri1 loss-of-function mutants. The model also predicts that a rather low number of receptor molecules are needed to initiate a physiological response. Interestingly, the “switch” between activation and inhibition of root growth depends on the BRI1 occupancy level. This suggests that BRI1 may be a core regulator based on activating different targets based on its occupancy level. Root growth is robust against reduction in the BRI1 receptor level but not to variation in the BR concentration. This indicates that BR signalling is mainly regulated via ligand availability and biochemical activity. Since BRI1 signalling is highly dependent on the presence of SERK co-receptors, it is important to determine how these co-receptors affect the signalling output. Therefore, in Chapter 4, the BRI1 receptor model was extended with two co-receptors, SERK1 and SERK3. The model also takes into account BRI1 signalling independent of SERK1 and SERK3. This may occur due the activity of BRI1 alone, or due to interaction of BRI1 with another co-receptor, for example SERK4. It appears that roots of the serk1serk3 double mutant are almost completely irresponsive for BRs while the hypocotyl is not, suggesting either a difference in co-receptor usage or a higher activity of BRI1 alone in the hypocotyl. The usage of different co-receptors may reflect a mechanism by which the sensitivity of a cell for BRs is regulated. It appears that co-receptors mainly act by increasing the magnitude of the response. In addition, in silico simulations confirm that BRI1 signalling is not impaired when the majority of SERK co-receptors operate in other signalling pathways. The presented model provides a starting point to incorporate the effect of other modulators of the BRI1 signal transduction cascade on a complex physiological response.
Current models for BRI1 mediated signalling postulate that SERK3 is recruited upon ligand binding. However, Fluorescence Recovery After Photo bleaching (FRAP) measurements described in Chapter 5, indicate that BRI1 receptors located in root meristem cells have a relatively low mobility. This suggests that BRI1 and SERK already form complexes in the absence of ligand.
It has been repeatedly reported that SERK co-receptors are involved in various biological processes and signal transduction networks. In Chapter 6, the changes in gene expression in absence of functional SERK1 and SERK3 are studied using transcriptional analysis. Microarrays were performed on RNA isolated from roots of 4-day-old seedlings of serk1, serk3 and serk1serk3 mutants.
Hierarchical cluster analysis indicated that serk3 mutant roots have the same transcriptional pattern when compared to roots of the serk1serk3 double mutant but to a lower magnitude. More than half of the genes differentially regulated in the serk1serk3 double mutant relate to BRI1 mediated signalling. In addition, a number of BR dependent and independent metabolic processes are affected in absence of SERK3 indicating that this co-receptor may have an additional function in metabolic control. Performing microarray analysis on receptor mutants is complicated as effects on gene transcripts may be indirect and due to differential regulation of downstream transcriptional regulators. This complexity is further enhanced in the SERK co-receptor mutants as multiple signalling pathways are affected. This raises the question if it is truly possible to correlate alterations in gene expression due to the absence of functional SERK co-receptors to one particular signal transduction pathway. In Chapter 7, the general discussion, it is described how modelling of BRI1 signalling in this thesis has contributed to new insights into the brassinosteroid signalling. Microscopy has been an important tool to quantify the number of receptors in a cell or the number of cells in a tissue. What is still needed is a clear link between a signalling activity, and, therefore, the physiological response of the cell, to local and intracellular protein-protein interactions and protein concentrations. Further expanding the available microscopic techniques and mathematical models to the cellular level is one of the next challenges. The research described in this thesis is a starting point for such an approach to study signal transduction in Arabidopsis.