Browsing by Person "Stefan, Thorsten"

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Gut microbiota patterns predicting long-term weight loss success in individuals with obesity undergoing nonsurgical therapy
(2022) Bischoff, Stephan C.; Nguyen, Nguyen K.; Seethaler, Benjamin; Beisner, Julia; Kügler, Philipp; Stefan, Thorsten
The long-term success of nonsurgical weight reduction programs is variable; thus, predictors of outcome are of major interest. We hypothesized that the intestinal microbiota known to be linked with diet and obesity contain such predictive elements. Methods: Metagenome analysis by shotgun sequencing of stool DNA was performed in a cohort of 15 adults with obesity (mean body mass index 43.1 kg/m2) who underwent a one-year multidisciplinary weight loss program and another year of follow-up. Eight individuals were persistently successful (mean relative weight loss 18.2%), and seven individuals were not successful (0.2%). The relationship between relative abundancies of bacterial genera/species and changes in relative weight loss or body mass index was studied using three different statistical modeling methods. Results: When combining the predictor variables selected by the applied statistical modeling, we identified seven bacterial genera and eight bacterial species as candidates for predicting success of weight loss. By classification of relative weight-loss predictions for each patient using 2–5 term models, 13 or 14 out of 15 individuals were predicted correctly. Conclusions: Our data strongly suggest that gut microbiota patterns allow individual prediction of long-term weight loss success. Prediction accuracy seems to be high but needs confirmation by larger prospective trials.
A high‐confidence Physcomitrium patens plasmodesmata proteome by iterative scoring and validation reveals diversification of cell wall proteins during evolution
(2023) Gombos, Sven; Miras, Manuel; Howe, Vicky; Xi, Lin; Pottier, Mathieu; Kazemein Jasemi, Neda S.; Schladt, Moritz; Ejike, J. Obinna; Neumann, Ulla; Hänsch, Sebastian; Kuttig, Franziska; Zhang, Zhaoxia; Dickmanns, Marcel; Xu, Peng; Stefan, Thorsten; Baumeister, Wolfgang; Frommer, Wolf B.; Simon, Rüdiger; Schulze, Waltraud X.
Plasmodesmata (PD) facilitate movement of molecules between plant cells. Regulation of this movement is still not understood. Plasmodesmata are hard to study, being deeply embedded within cell walls and incorporating several membrane types. Thus, structure and protein composition of PD remain enigmatic. Previous studies of PD protein composition identified protein lists with few validations, making functional conclusions difficult. We developed a PD scoring approach in iteration with large‐scale systematic localization, defining a high‐confidence PD proteome of Physcomitrium patens (HC300). HC300, together with bona fide PD proteins from literature, were placed in Pddb. About 65% of proteins in HC300 were not previously PD‐localized. Callose‐degrading glycolyl hydrolase family 17 (GHL17) is an abundant protein family with representatives across evolutionary scale. Among GHL17s, we exclusively found members of one phylogenetic clade with PD localization and orthologs occur only in species with developed PD. Phylogenetic comparison was expanded to xyloglucan endotransglucosylases/hydrolases and Exordium‐like proteins, which also diversified into PD‐localized and non‐PD‐localized members on distinct phylogenetic clades. Our high‐confidence PD proteome HC300 provides insights into diversification of large protein families. Iterative and systematic large‐scale localization across plant species strengthens the reliability of HC300 as basis for exploring structure, function, and evolution of this important organelle.
Regulatory modules of metabolites and protein phosphorylation in arabidopsis genotypes with altered sucrose allocation
(2022) Stefan, Thorsten; Wu, Xu Na; Zhang, Youjun; Fernie, Alisdair; Schulze, Waltraud X.
Multi-omics data sets are increasingly being used for the interpretation of cellular processes in response to environmental cues. Especially, the posttranslational modification of proteins by phosphorylation is an important regulatory process affecting protein activity and/or localization, which, in turn, can have effects on metabolic processes and metabolite levels. Despite this importance, relationships between protein phosphorylation status and metabolite abundance remain largely underexplored. Here, we used a phosphoproteomics–metabolomics data set collected at the end of day and night in shoots and roots of Arabidopsis to propose regulatory relationships between protein phosphorylation and accumulation or allocation of metabolites. For this purpose, we introduced a novel, robust co-expression measure suited to the structure of our data sets, and we used this measure to construct metabolite-phosphopeptide networks. These networks were compared between wild type and plants with perturbations in key processes of sugar metabolism, namely, sucrose export (sweet11/12 mutant) and starch synthesis (pgm mutant). The phosphopeptide–metabolite network turned out to be highly sensitive to perturbations in sugar metabolism. Specifically, KING1, the regulatory subunit of SnRK1, was identified as a primary candidate connecting protein phosphorylation status with metabolism. We additionally identified strong changes in the fatty acid network of the sweet11/12 mutant, potentially resulting from a combination of fatty acid signaling and metabolic overflow reactions in response to high internal sucrose concentrations. Our results further suggest novel protein-metabolite relationships as candidates for future targeted research.