Dataset Information

Susceptible interaction Ectocarpus/Eurychasma

ABSTRACT: The pathosystem between the model brown alga Ectocarpus siliculsosus and the oomycete pathogen Eurychasma dicksonii was investigated with the aim to identify proteins involved in host repsonse. Comparative 2D electrophoresis was used to identify protein spots differentially expressed between uninfected control and infected algal tissue.

INSTRUMENT(S): Bruker Daltonics HCT Series

ORGANISM(S): Ectocarpus Siliculosus (brown Alga)

SUBMITTER: Martina Strittmatter

LAB HEAD: Dr. Claire Gachon

PROVIDER: PXD001458 | Pride | 2018-10-19

REPOSITORIES: Pride

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Publications

Infection of the brown alga Ectocarpus siliculosus by the oomycete Eurychasma dicksonii induces oxidative stress and halogen metabolism.

Strittmatter Martina M Grenville-Briggs Laura J LJ Breithut Lisa L Van West Pieter P Gachon Claire M M CM Küpper Frithjof C FC

Plant, cell & environment 20150423 2

Pathogens are increasingly being recognized as key evolutionary and ecological drivers in marine ecosystems. Defence mechanisms of seaweeds, however, have mostly been investigated by mimicking infection using elicitors. We have established an experimental pathosystem between the genome brown model seaweed Ectocarpus siliculosus and the oomycete Eurychasma dicksonii as a powerful new tool to investigate algal responses to infection. Using proteomics, we identified 21 algal proteins differentially ...[more]

PMID: 25764246

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Project description:This model is from the article: Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism. Roger L Chang, Lila Ghamsari,, Ani Manichaikul, Erik F Y Hom, Santhanam Balaji, Weiqi Fu, Yun Shen, Tong Hao, Bernhard Ø Palsson, Kourosh Salehi-Ashtiani & Jason A Papin Molecular Systems Biology 7:518; 2011 doi:10.1038/msb.2011.52 Abstract: Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. 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. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. 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
	spot11.dat	Other
	spot11.mgf	Mgf
	spot11.yep	Other
	spot112.dat	Other
	spot112.mgf	Mgf