Publications

Frankia spp. are widespread nitrogen-fixing soil bacteria, which often live in symbiosis with a broad range of hosts. Metal homeostasis plays a crucial role in the success of the symbiosis regarding … the acquisition of essential trace metals and detoxification of potentially toxic elements. We have hypothesised that Frankia releases many organic ligands with a broad spectrum of affinity for essential and toxic metals. We coined the term 'ligandosphere' to describe the entirety of excreted metal complexing agents and ligands derived from the dissolved organic matter. Using metal isotope-coded profiling (MICP); metallophores of physiological important and toxic trace metals were identified by the addition of stable metal isotope pairs such as 54Fe/58Fe, 63Cu/65Cu, 66Zn/68Zn or 95Mo/98Mo. Liquid chromatography coupled to a mass spectrometer revealed strong variations of the metallophore profile in between the 14 test-strains. In total, about 83 organic ligands were identified as binding to one of the tested metals. The predicted sum formula of the major Fe binding ligands and MS/MS experiments suggested that several metallophore candidates have a similar molecular backbone. Growth experiments with a hyper-producer of metallophores revealed a positive relationship between metallophore production and the concentration of Cu in the growth medium. The present study provides the first comprehensive overview of the complexity of Frankia's ligandosphere. It opens a path to a deeper understanding of mechanisms that regulate metal homeostasis in frankiae. Deciphering these mechanisms is important since the fitness of actinorhizal plants and their potential in ecological restoration relies heavily on their symbiosis with frankiae.

Introduction: Stable isotopic labeling experiments are powerful tools to study metabolic pathways, to follow tracers and fluxes in biotic and abiotic transformations and to elucidate molecules involved …d in metal complexing. Objective: To introduce a software tool for the identification of isotopologues from mass spectrometry data. Methods: DeltaMS relies on XCMS peak detection and X13CMS isotopologue grouping and then analyses data for specific isotope ratios and the relative error of these ratios. It provides pipelines for recognition of isotope patterns in three experiment types commonly used in isotopic labeling studies: (1) Search for isotope signatures with a specific mass shift and intensity ratio in one sample set. (2) Analyze two sample sets for a specific mass shift and, optionally, the isotope ratio, whereby one sample set is isotope-labeled, and one is not. (3) Analyze isotope-guided perturbation experiments with a setup described in X13CMS. Results: To illustrate the versatility of DeltaMS, we analyze data sets from case-studies that commonly pose challenges in evaluation of natural isotopes or isotopic signatures in labeling experiment. In these examples, the untargeted detection of sulfur, bromine and artificial metal isotopic patterns is enabled by the automated search for specific isotopes or isotope signatures. Conclusion: DeltaMS provides a platform for the identification of (pre-defined) isotopologues in MS data from single samples or comparative metabolomics data sets.