Dataset Information

How grass keeps growing: an integrated analysis of hormonal crosstalk in the maize leaf growth zone.

ABSTRACT: We studied the maize leaf to understand how long-distance signals, auxin and cytokinin, control leaf growth dynamics. We constructed a mathematical model describing the transport of these hormones along the leaf growth zone and their interaction with the local gibberellin (GA) metabolism in the control of cell division. Assuming gradually declining auxin and cytokinin supply at the leaf base, the model generated spatiotemporal hormone distribution and growth patterns that matched experimental data. At the cellular level, the model predicted a basal leaf growth as a result of cell division driven by auxin and cytokinin. Superimposed on this, GA synthesis regulated growth through the control of the size of the region of active cell division. The predicted hormone and cell length distributions closely matched experimental data. To correctly predict the leaf growth profiles and final organ size of lines with reduced or elevated GA production, the model required a signal proportional to the size of the emerged part of the leaf that inhibited the basal leaf growth driven by auxin and cytokinin. Excision and shading of the emerged part of the growing leaf allowed us to demonstrate that this signal exists and depends on the perception of light intensity.

SUBMITTER: De Vos D

PROVIDER: S-EPMC7116270 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

How grass keeps growing: an integrated analysis of hormonal crosstalk in the maize leaf growth zone.

De Vos Dirk D Nelissen Hilde H AbdElgawad Hamada H Prinsen Els E Broeckhove Jan J Inzé Dirk D Beemster Gerrit T S GTS

The New phytologist 20191224 6

We studied the maize leaf to understand how long-distance signals, auxin and cytokinin, control leaf growth dynamics. We constructed a mathematical model describing the transport of these hormones along the leaf growth zone and their interaction with the local gibberellin (GA) metabolism in the control of cell division. Assuming gradually declining auxin and cytokinin supply at the leaf base, the model generated spatiotemporal hormone distribution and growth patterns that matched experimental da ...[more]

PMID: 31705666

Similar Datasets

Project description:In grasses, leaf growth is often monitored to gain insights in growth processes, biomass accumulation, regrowth after cutting, etc. To study the growth dynamics of the grass leaf, its length is measured at regular time intervals to derive the leaf elongation rate (LER) profile over time. From the LER profile, parameters such as maximal LER and leaf elongation duration (LED), which are essential for detecting inter-genotype growth differences and/or quantifying plant growth responses to changing environmental conditions, can be determined. As growth is influenced by the circadian clock and, especially in grasses, changes in environmental conditions such as temperature and evaporative demand, the LER profiles show considerable experimental variation and thus often do not follow a smooth curve. Hence it is difficult to quantify the duration and timing of growth. For these reasons, the measured data points should be fitted using a suitable mathematical function, such as the beta sigmoid function for leaf elongation. In the context of high-throughput phenotyping, we implemented the fitting of leaf growth measurements into a user-friendly Microsoft Excel-based macro, a tool called LEAF-E. LEAF-E allows to perform non-linear regression modeling of leaf length measurements suitable for robust and automated extraction of leaf growth parameters such as LER and LED from large datasets. LEAF-E is particularly useful to quantify the timing of leaf growth, which forms an important added value for detecting differences in leaf growth development. We illustrate the broad application range of LEAF-E using published and unpublished data sets of maize, Miscanthus spp. and Brachypodium distachyon, generated in independent experiments and for different purposes. In addition, we show that LEAF-E could also be used to fit datasets of other growth-related processes that follow the sigmoidal profile, such as cell length measurements along the leaf axis. Given its user-friendliness, ability to quantify duration and timing of leaf growth and broad application range, LEAF-E is a tool that could be routinely used to study growth processes following the sigmoidal profile.

Dataset Information

How grass keeps growing: an integrated analysis of hormonal crosstalk in the maize leaf growth zone.

Publications

How grass keeps growing: an integrated analysis of hormonal crosstalk in the maize leaf growth zone.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets