Auxin Biology : Quantitative understanding of a dynamic protein network

The plant hormone auxin is an important signaling molecule essential for plant growth and development. The auxin signaling pathway comprises three dedicated family of response proteins namely TIR1 receptor, Aux/IAA repressor and the AUXIN RESPONSE FACTOR (ARF) transcription family. At low auxin concentrations in cell, ARF transcription factors are functionally repressed by a direct interaction with Auxin/indole-3-acetic acid (Aux/IAA) proteins. However, at high cellular auxin concentrations, auxin binds to an intracellular nuclear receptor complex (SCR-TIR1/AFB), a ubiquitin ligase that can only efficiently bind its substrate proteins in the presence of the hormone. The auxin-bound receptor recognizes Aux/IAAs proteins for ubiquitination and degradation, which serves as a transcriptional switch of ARF transcriptional repression thereby allowing auxin regulated gene transcription.

Figure 1: The auxin signalling pathway is the machinery that controls auxin gene expression in the nucleus. At very low auxin levels (a), the auxin response factors (ARFs, in blue) are kept inactive by the Aux/IAA proteins (in red) through heterodimerization. Increased auxin concentration in the nucleus (b), recruits Aux/IAAs forming a complex with SCF (TIR/AFBs) (in green). These co-receptors will be degraded, allowing ARF to modulate auxin-related gene expression. Figure taken from Paque and Weijers BMC Biology (2016).
Figure 1: The auxin signalling pathway is the machinery that controls auxin gene expression in the nucleus. At very low auxin levels (a), the auxin response factors (ARFs, in blue) are kept inactive by the Aux/IAA proteins (in red) through heterodimerization. Increased auxin concentration in the nucleus (b), recruits Aux/IAAs forming a complex with SCF (TIR/AFBs) (in green). These co-receptors will be degraded, allowing ARF to modulate auxin-related gene expression. Figure taken from Paque and Weijers BMC Biology (2016).

While the function of the individual proteins of the auxin network is well understood, a major unanswered question is how the interactions among those proteins generate a dynamic response system with different outputs. Thus far, most studies on auxin response protein have been performed in Arabidopsis thaliana. However, auxin signaling in Arabidopsis comprises 6 TIR1 receptor proteins, 23 ARF proteins and 29 Aux/IAA repressor proteins. For establishing a quantitative view on the auxin response network, a simple but complete auxin system is required. Therefore, we use the liverwort Marchantia polymorpha, an early diverging land plant that has the simplest possible complete auxin signaling system. Marchantia polymorpha has only a single auxin receptor TIR1/AFB (MpTIR1), a single AUX/IAA (MpIAA) inhibitor protein, and three ARF proteins (MpARF1, MpARF2, and MpARF3). Having this limited number of response proteins in Marchantia makes it possibly the best model system for a quantitative study of auxin response protein network. In this project, all relevant quantitative parameters including concentrations of each response protein in Marchantia polymorpha, protein turnover rates and dissociation constants of all protein-protein interactions will be determined (see Figure 2). These quantitative parameters will be integrated into a mathematical model, and we will use this model to identify critical factors within the protein network that affect signal output. Subsequently, we will test model predictions by modifying e.g. protein levels or interaction affinities in Marchantia polymorpha, and we will address effects on response output through quantification of RNA levels of specific target genes.

Figure 2: Schematic representation of the auxin protein network. In this project, we will use Marchantia polymorpha and experimentally determine protein concentrations, protein turnover rates, dissociation constants of protein interactions and binding rates of MpARFs to auxin responsive element (e.g. TGTCTC) to obtain a quantitative view of the auxin response network. These parameters will be used for generating a mathematical model to identify critical factors within this protein network. Finally, the predicted parameters will be validated experimentally through expression levels of target genes.
Figure 2: Schematic representation of the auxin protein network. In this project, we will use Marchantia polymorpha and experimentally determine protein concentrations, protein turnover rates, dissociation constants of protein interactions and binding rates of MpARFs to auxin responsive element (e.g. TGTCTC) to obtain a quantitative view of the auxin response network. These parameters will be used for generating a mathematical model to identify critical factors within this protein network. Finally, the predicted parameters will be validated experimentally through expression levels of target genes.

Several student projects are available that contribute to the overall aim of this research. These projects involve determination of binding constants of ARF-ARF and ARF – DNA interactions, quantification of ARF concentrations in Marchantia polymorpha (oldest land plant) or protein turnover rates. The outcome of this research will be used for developing a mathematical model that identify critical factors within the protein network that affect signal output. For detailed description of student projects, please contact JanWillem.Borst@wur.nl