Computational modeling and quantitative physiology reveal central parameters for brassinosteroid-regulated early cell physiological processes linked to elongation growth of theArabidopsisroot

Abstract

Brassinosteroids (BR) are one of the key regulators of plant growth and development and have been the object of intense study. Whereas the individual components of the pathway have been well characterized experimentally, we employed computational modeling in combination with quantitative experiments to study the dynamics and regulation of the plasma membrane-localized fast BR response pathway in the epidermal cell layer along the Arabidopsis thaliana root axis that initiates early processes leading to cell elongation growth. The model, consisting of ordinary differential equations, comprises the BR induced hyperpolarization of the plasma membrane, the acidification of the apoplast and subsequent swelling of the cell wall. Utilizing this model and verified by experimental approaches, we demonstrate that the competence of the root epidermal cells for the physiological responses predominantly depends on the amount and activity of H+-ATPases in the plasma membrane. The model further predicted that an influx of cations is required to balance the shift of charges caused by the acidification of the apoplast. A potassium transporter was identified and characterized, which may fulfill this charge compensation. Lastly, we further specified in silico the role of the negative regulator BIR3 in the fine tuning of the cell physiological output.

Tripartite hormonal regulation of plasma membrane H+-ATPase activity

“…The H + fluxes during the hydrotropic response were decreased especially in bri1-5 root elongation zone. Another study supported these observations by showing that triple or quadruple mutants in BRs receptor or co-receptors, including bri1, bak1, bri1brl1brl3, brl1brl3bak1 and bri1brl1brl3bak1, displayed reduced root growth and root curvature angles in the hydrotropism assay, while the BRL3 overexpression transgenic line demonstrated an increased root hydrotropic bending compared to wild type roots [59].…”

“…Thus, low BR concentrations in the meristem and high in the root elongation zone contribute to the optimal root growth [57]. Interestingly, during root development the AHA2 transcripts are also increased in the transition and elongation zone, resulting in AHA2 protein accumulation and acidic apoplastic pH in the epidermal cells in this part of the root [59]. As BRI1 interacts directly with AHA2 and AHA7 [53][54][55][56], it was proposed that AHA2-containing-BRI1-BAK1 nanocluster at least in part regulates arabidopsis root growth along the root tip axis [59].…”

Brassinosteroid gene regulatory networks at cellular resolution

“…The epidermis is widely described as the major site for BR-promoted gene expression in the root (Chaiwanon and Wang, 2015; Fridman et al, 2014, 2021; Großeholz et al, 2021; Hacham et al, 2011; Nolan et al, 2020; Vragović et al, 2015; Wei and Li, 2016). Previous studies showed that epidermal expression of BRI1 was sufficient to rescue morphological phenotypes including meristem size and root length of loss-of-function BR mutants such as bri1-T (Fridman et al, 2014; Hacham et al, 2011; Kang et al, 2017).…”

Root biology never sleeps

“…Mathematical modelling complements phenotyping advances by overcoming the challenges of experimental approaches and benefits from the emerging field of functional phenomics (York, 2019). Highlights from the diversity of modelling approaches presented at ISRR/Rooting2021, in both spatial and temporal scales, include: a micro‐hydrological model that describes a new symplastic water pumping mechanism (Couvreur et al ., 2021); the dynamics and regulation of a fast brassinosteroid response pathway in Arabidopsis root tips (Großeholz et al ., 2021); functional–structural plant (FSP) models to identify optimal root phenotypes for nutrient capture in contrasting environments (Rangarajan, 2021); and field‐scale simulations of plant populations and communities (Postma et al ., 2017; Schnepf et al ., 2018; Faverjon et al ., 2019). Future mathematical models will draw on larger, more complex, datasets incorporating novel imaging technology, high‐throughput phenotyping and availability of relevant environmental data.…”