Project description:Protein–metabolite interactions play an important role in the cell’s metabolism and many methods have been developed to screen them in vitro. However, few methods can be applied at a large scale and not alter biological state. Here we describe a proteometabolomic approach, using chromatography to generate cell fractions which are then analyzed with mass spectrometry for both protein and metabolite identification. Integrating the proteomic and metabolomic analyses makes it possible to identify protein-bound metabolites. Applying the concept to the thermophilic fungus Chaetomium thermophilum, we predict many likely protein-metabolite interactions, most of them novel. As a proof of principle, we experimentally validate a predicted interaction between the ribosome and isopentenyl adenine.
Project description:Loliolide, a metabolite of carotenoid metabolic pathways in plants, was identified as an inducer of resistance to herbivores such as the two-spotted spider mite, Tetranychus urticae, and the common cutworm, Spodoptera litura. To identify host factors involved in loliolide-induced herbivore resistance, microarray analysis of tomato plants treated with loliolide was performed. We identified several cell wall-associated defense genes as loliolide-responsive genes.
Project description:Untargeted metabolomics of Botrytis-Bacillus interaction at 6,24 and 48h. Ethyl-acetate extraction of supernatant fraction and methanol extraction for cell fraction.
Project description:The project aimed to create dynamic maps of protein-protein-metabolite complexes of Arabidopsis thaliana seedlings using PROMIS (PROtein–Metabolite Interactions using Size separation). The approach involves using size exclusion chromatography (SEC) to separate complexes, followed by LC-MS-based proteomics and metabolomics analysis of the obtained fractions. Co-elution is used to reconstruct the protein-metabolite interactions (PMIs) networks. PROMIS strongly progresses understanding protein-small molecule interactions due to its non-targeted manner, cell-wide scale, and generic nature, making it suitable across biological systems. Combining PROMIS with mashing learning approach SLIMP “supervised learning of metabolite-protein interactions from multiple co-fractionation mass spectrometry datasets” allows computing a global map of metabolite-protein interactions in vivo.
Project description:Our analysis indicates that at least 37% of the transcriptome mobilized by KNAT1 is potentially dependent on this interaction, and includes genes involved in secondary cell wall modifications and phenylpropanoid biosynthesis.
Project description:The project aims to create dynamic maps of protein-protein-metabolite complexes in S. cerevisiae across growth phases using PROMIS (PROtein–Metabolite Interactions using Size separation). It is a biochemical, untargeted, proteome- and metabolome-wide method to study protein-protein and protein–metabolite complexes close to in vivo conditions. Approach involves using size exclusion chromatography (SEC) to separate complexes, followed by LC-MS-based proteomics and metabolomics analysis. This dataset was used for mashie learning approach: SLIMP, supervised learning of metabolite-protein interactions from multiple co-fractionation mass spectrometry datasets, to compute a global map of metabolite-protein interactions.