Dataset Information

Multi-dimensional leaf phenotypes reflect root system genotype in grafted grapevine over the growing season

ABSTRACT:

BACKGROUND: Modern biological approaches generate volumes of multi-dimensional data, offering unprecedented opportunities to address biological questions previously beyond reach owing to small or subtle effects. A fundamental question in plant biology is the extent to which below-ground activity in the root system influences above-ground phenotypes expressed in the shoot system. Grafting, an ancient horticultural practice that fuses the root system of one individual (the rootstock) with the shoot system of a second, genetically distinct individual (the scion), is a powerful experimental system to understand below-ground effects on above-ground phenotypes. Previous studies on grafted grapevines have detected rootstock influence on scion phenotypes including physiology and berry chemistry. However, the extent of the rootstock's influence on leaves, the photosynthetic engines of the vine, and how those effects change over the course of a growing season, are still largely unknown.

RESULTS: Here, we investigate associations between rootstock genotype and shoot system phenotypes using 5 multi-dimensional leaf phenotyping modalities measured in a common grafted scion: ionomics, metabolomics, transcriptomics, morphometrics, and physiology. Rootstock influence is ubiquitous but subtle across modalities, with the strongest signature of rootstock observed in the leaf ionome. Moreover, we find that the extent of rootstock influence on scion phenotypes and patterns of phenomic covariation are highly dynamic across the season.

CONCLUSIONS: These findings substantially expand previously identified patterns to demonstrate that rootstock influence on scion phenotypes is complex and dynamic and underscore that broad understanding necessitates volumes of multi-dimensional data previously unmet.

INSTRUMENT(S): Liquid Chromatography MS - positive - reverse phase

SUBMITTER: Zachary Harris

PROVIDER: MTBLS2831 | MetaboLights | 2022-01-07

REPOSITORIES: MetaboLights

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Multi-dimensional leaf phenotypes reflect root system genotype in grafted grapevine over the growing season.

Harris Zachary N ZN Awale Mani M Bhakta Niyati N Chitwood Daniel H DH Fennell Anne A Frawley Emma E Klein Laura L LL Kovacs Laszlo G LG Kwasniewski Misha M Londo Jason P JP Ma Qin Q Migicovsky Zoë Z Swift Joel F JF Miller Allison J AJ

GigaScience 20211201 12

<h4>Background</h4>Modern biological approaches generate volumes of multi-dimensional data, offering unprecedented opportunities to address biological questions previously beyond reach owing to small or subtle effects. A fundamental question in plant biology is the extent to which below-ground activity in the root system influences above-ground phenotypes expressed in the shoot system. Grafting, an ancient horticultural practice that fuses the root system of one individual (the rootstock) with t ...[more]

PMID: 34966928

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Project description:This model is from the article: The auxin signalling network translates dynamic input into robust patterning at the shoot apex. Vernoux T, Brunoud G, Farcot E, Morin V, Van den Daele H, Legrand J, Oliva M, Das P, Larrieu A, Wells D, Guédon Y, Armitage L, Picard F, Guyomarc'h S, Cellier C, Parry G, Koumproglou R, Doonan JH, Estelle M, Godin C, Kepinski S, Bennett M, De Veylder L, Traas J. Mol Syst Biol. 2011 Jul 5;7:508. 21734647 , Abstract: The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex. Note: Figure 4 of the supplementary material of the reference article has been reproduced here. In this model, the fluctuations of auxin level is represented using sinux function. Time evolution of the variables AUX/IAA (I) and mRNA (R) are plotted, under the influence of fluctuations of auxin level. pi_A is varied between 0 and 2 by steps of 0.1. This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. For more information see the terms of use . To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

			Action	DRS
	MTBLS2831	Other
	FILES	Other
	a_MTBLS2831_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	files-all.json	Other
	i_Investigation.txt	Txt