Proteome reorganization and amino acid metabolism during germination and seedling establishment in Lupinus albus.

During germination plants rely entirely on their seed storage compounds to provide energy and precursors for the synthesis of macromolecular structures until the seedling has emerged from the soil and photosynthesis can be established. Lupin seeds use proteins as their major storage compounds, accounting for up to 40% of the seed dry weight. Lupins are therefore a valuable complement to soy as a source of plant protein for human and animal nutrition. The aim of this study was to elucidate how storage protein metabolism is coordinated with other metabolic processes to meet the requirements of the growing seedling. In a quantitative approach, we analyzed seedling growth, as well as alterations in biomass composition, the proteome, and metabolite profiles during germination and seedling establishment in Lupinus albus. The reallocation of nitrogen resources from seed storage proteins to functional seed proteins was mapped based on a manually curated functional protein annotation database. Although classified as a protein crop, Lupinus albus does not use amino acids as a primary substrate for energy metabolism during germination. However, fatty acid and amino acid metabolism may be integrated at the level of malate synthase to combine stored carbon from lipids and proteins into gluconeogenesis.

Application of untargeted liquid chromatography-mass spectrometry to routine analysis of food using three-dimensional bucketing and machine learning.

For the detection of food adulteration, sensitive and reproducible analytical methods are required. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is a highly sensitive method that can be used to obtain analytical fingerprints consisting of a variety of different components. Since the comparability of measurements carried out with different devices and at different times is not given, specific adulterants are usually detected in targeted analyses instead of analyzing the entire fingerprint. However, this comprehensive analysis is desirable in order to stay ahead in the race against food fraudsters, who are constantly adapting their adulterations to the latest state of the art in analytics. We have developed and optimized an approach that enables the separate processing of untargeted LC­HRMS data obtained from different devices and at different times. We demonstrate this by the successful determination of the geographical origin of honey samples using a random forest model. We then show that this approach can be applied to develop a continuously learning classification model and our final model, based on data from 835 samples, achieves a classification accuracy of 94% for 126 test samples from 6 different countries.

Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth.

For the untargeted analysis of the metabolome of biological samples with liquid chromatography-mass spectrometry (LC-MS), high-dimensional data sets containing many different metabolites are obtained. Since the utilization of these complex data is challenging, different machine learning approaches have been developed. Those methods are usually applied as black box classification tools, and detailed information about class differences that result from the complex interplay of the metabolites are not obtained. Here, we demonstrate that this information is accessible by the application of random forest (RF) approaches and especially by surrogate minimal depth (SMD) that is applied to metabolomics data for the first time. We show this by the selection of important features and the evaluation of their mutual impact on the multi-level classification of white asparagus regarding provenance and biological identity. SMD enables the identification of multiple features from the same metabolites and reveals meaningful biological relations, proving its high potential for the comprehensive utilization of high-dimensional metabolomics data.