Summary Stress granules (SGs) are evolutionary conserved aggregates of proteins and untranslated mRNAs formed in response to stress. Despite their importance for stress adaptation, no complete proteome composition has been reported for plant SGs. In this study, we addressed the existing gap. Importantly, we also provide evidence for metabolite sequestration within the SGs. To isolate SGs we used Arabidopsis seedlings expressing green fluorescent protein (GFP) fusion of the SGs marker protein, Rbp47b, and an experimental protocol combining differential centrifugation with affinity purification (AP). SGs isolates were analysed using mass spectrometry‐based proteomics and metabolomics. A quarter of the identified proteins constituted known or predicted SG components. Intriguingly, the remaining proteins were enriched in key enzymes and regulators, such as cyclin‐dependent kinase A (CDKA), that mediate plant responses to stress. In addition to proteins, nucleotides, amino acids and phospholipids also accumulated in SGs. Taken together, our results indicated the presence of a preexisting SG protein interaction network; an evolutionary conservation of the proteins involved in SG assembly and dynamics; an important role for SGs in moderation of stress responses by selective storage of proteins and metabolites.

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Background The impact of Malassezia yeasts on skin mycobiome and health has received considerable attention recently. Pityriasis versicolor (PV), a common dermatosis caused by Malassezia genus worldwide, is a manifestation of dysbiosis. PV can be associated with hyper- and/or hypopigmented skin lesions. This disease entity is characterized by high percentage of relapses, which demands a proper antifungal therapy that is based on unambiguous species identification and drug susceptibility testing. Case presentation Comprehensive analysis of PV case in man presenting simultaneously hyper- and hypopigmented skin lesions was performed. Conventional and molecular diagnostic procedures revealed Malassezia furfur and Malassezia sympodialis, respectively as etiological agents of skin lesions observed. Susceptibility tests showed significantly lowered sensitivity of M. furfur cells to fluconazole. Based on susceptibility profiles local antifungal therapy with drugs characterized by entirely different mechanism of action was included. Conclusions Our study indicates that cases of PV represented by two types of skin lesions in one patient may be associated with distinct Malassezia species. Moreover, as observed in this case, each of the isolated etiological agents of PV may differ significantly in susceptibility to antifungals. This can significantly complicate the treatment of dermatosis, which by definition is associated with a significant percentage of relapses. In the presented case localized topical treatment was sufficient and successful while allowing maintaining the physiological mycobiome.

Quinoa (Chenopodium quinoa Willd.) is an herbaceous annual crop of the amaranth family (Amaranthaceae). It is increasingly cultivated for its nutritious grains, which are rich in protein and essential amino acids, lipids, and minerals. Quinoa exhibits a high tolerance towards various abiotic stresses including drought and salinity, which supports its agricultural cultivation under climate change conditions. The use of quinoa grains is compromised by anti-nutritional saponins, a terpenoid class of secondary metabolites deposited in the seed coat; their removal before consumption requires extensive washing, an economically and environmentally unfavorable process; or their accumulation can be reduced through breeding. In this study, we analyzed the seed metabolomes, including amino acids, fatty acids, and saponins, from 471 quinoa cultivars, including two related species, by liquid chromatography – mass spectrometry. Additionally, we determined a large number of agronomic traits including biomass, flowering time, and seed yield. The results revealed considerable diversity between genotypes and provide a knowledge base for future breeding or genome editing of quinoa.

Metabolite-protein interactions affect and shape diverse cellular processes. Yet, despite advances, approaches for identifying metabolite-protein interactions at a genome-wide scale are lacking. Here we present an approach termed SLIMP that predicts metabolite-protein interactions using supervised machine learning on features engineered from metabolic and proteomic profiles from a co-fractionation mass spectrometry-based technique. By applying SLIMP with gold standards, assembled from public databases, along with metabolic and proteomic data sets from multiple conditions and growth stages we predicted over 9,000 and 20,000 metabolite-protein interactions for Saccharomyces cerevisiae and Arabidopsis thaliana, respectively. Extensive comparative analyses corroborated the quality of the predictions from SLIMP with respect to widely-used performance measures (e.g. F1-score exceeding 0.8). SLIMP predicted novel targets of 2’, 3’ cyclic nucleotides and dipeptides, which we analysed comparatively between the two organisms. Finally, predicted interactions for the dipeptide Tyr-Asp in Arabidopsis and the dipeptide Ser-Leu in yeast were independently validated, opening the possibility for future applications of supervised machine learning approaches in this area of systems biology.