MassIVE MSV000097721

Partial Public

Developing probabilistic graphical models and analysis software to integrate multi-omics data

Description

We are requesting the polar metabolomics analysis from a high-resolution time series experiment to develop probabilistic graphical models of bacterial-fungal-plant interactions. The experiment exposed resistant (line H7996) and susceptible (line FL8000) tomatoes (Solanum lycopersicum) to the select agent soil pathogen Ralstonia solanacearum (RS5) which is responsible for Bacterial Wilt. R. solanacearum infects plants through root wounds and colonizes vascular tissues. The pathogen rapidly multiplies and clogs the xylem vessels causing the infected plant to quickly wilt and die. Exopolysaccharides from R. solanacearum activate salicylic acid (SA) pathways in the resistant plants, but not in susceptible plants (Mansfield et al., 2012; Milling et al., 2011). A recent study suggested the microbiome plays a role in host susceptibility (Kwak et al., 2018). The system is being used as a model for developing new techniques in modeling bacterial-fungal-plant interactions and developing statistical methods to engineer microbial consortia from these graphical models. The work (proposal:httpas://doi.org/10.46936/10.25585/60000455) conducted by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231. [doi:10.25345/C5J38KW5N] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: bacterial-fungal-plant interactions ; polar metabolomics ; DatasetType:Metabolomics

Contact

Principal Investigators:
(in alphabetical order)
Adam Rivers, USDA, United States
Submitting User: bpbowen
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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

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