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Publication
Dynamic changes in O-GlcNAcylation regulate osteoclast differentiation and bone loss via nucleoporin 153
(2022) Li, Yi-Nan; Chen, Chih-Wei; Trinh-Minh, Thuong; Zhu, Honglin; Matei, Alexandru-Emil; Györfi, Andrea-Hermina; Kuwert, Frederic; Hubel, Philipp; Ding, Xiao; Manh, Cuong Tran; Xu, Xiaohan; Liebel, Christoph; Fedorchenko, Vladyslav; Liang, Ruifang; Huang, Kaiyue; Pfannstiel, Jens; Huang, Min-Chuan; Lin, Neng-Yu; Ramming, Andreas; Schett, Georg; Distler, Jörg H. W.; Li, Yi-Nan; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Chen, Chih-Wei; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Trinh-Minh, Thuong; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Zhu, Honglin; Department of Rheumatology, Xiangya Hospital, Central South University, Changsha, China; Matei, Alexandru-Emil; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Györfi, Andrea-Hermina; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Kuwert, Frederic; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Hubel, Philipp; Core Facility Hohenheim, University of Hohenheim, Stuttgart, Germany; Ding, Xiao; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Manh, Cuong Tran; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Xu, Xiaohan; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Liebel, Christoph; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Fedorchenko, Vladyslav; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Liang, Ruifang; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Huang, Kaiyue; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Pfannstiel, Jens; Core Facility Hohenheim, University of Hohenheim, Stuttgart, Germany; Huang, Min-Chuan; Graduate Institute of Anatomy and Cell biology, National Taiwan University College of Medicine, Taipei, Taiwan; Lin, Neng-Yu; Graduate Institute of Anatomy and Cell biology, National Taiwan University College of Medicine, Taipei, Taiwan; Ramming, Andreas; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Schett, Georg; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany; Distler, Jörg H. W.; Deutsches Zentrum für Immuntherapie, Friedrich Alexander University Erlangen-Nuremberg and Universitaetsklinikum Erlangen, Erlangen, Germany
Bone mass is maintained by the balance between osteoclast-induced bone resorption and osteoblast-triggered bone formation. In inflammatory arthritis such as rheumatoid arthritis (RA), however, increased osteoclast differentiation and activity skew this balance resulting in progressive bone loss. O-GlcNAcylation is a posttranslational modification with attachment of a single O-linked β-D-N-acetylglucosamine (O-GlcNAc) residue to serine or threonine residues of target proteins. Although O-GlcNAcylation is one of the most common protein modifications, its role in bone homeostasis has not been systematically investigated. We demonstrate that dynamic changes in O-GlcNAcylation are required for osteoclastogenesis. Increased O-GlcNAcylation promotes osteoclast differentiation during the early stages, whereas its downregulation is required for osteoclast maturation. At the molecular level, O-GlcNAcylation affects several pathways including oxidative phosphorylation and cell-cell fusion. TNFα fosters the dynamic regulation of O-GlcNAcylation to promote osteoclastogenesis in inflammatory arthritis. Targeted pharmaceutical or genetic inhibition of O-GlcNAc transferase (OGT) or O-GlcNAcase (OGA) arrests osteoclast differentiation during early stages of differentiation and during later maturation, respectively, and ameliorates bone loss in experimental arthritis. Knockdown of NUP153, an O-GlcNAcylation target, has similar effects as OGT inhibition and inhibits osteoclastogenesis. These findings highlight an important role of O-GlcNAcylation in osteoclastogenesis and may offer the potential to therapeutically interfere with pathologic bone resorption.
Publication
Linking transcriptional dynamics of CH4-cycling grassland soil microbiomes to seasonal gas fluxes
(2022) Täumer, Jana; Marhan, Sven; Groß, Verena; Jensen, Corinna; Kuss, Andreas W.; Kolb, Steffen; Urich, Tim; Täumer, Jana; Institute of Microbiology, Center for Functional Genomics of Microbes, University of Greifswald, Greifswald, Germany; Marhan, Sven; Institute of Soil Science and Land Evaluation, Soil Biology Department, University of Hohenheim, Stuttgart, Germany; Groß, Verena; Institute of Microbiology, Center for Functional Genomics of Microbes, University of Greifswald, Greifswald, Germany; Jensen, Corinna; Human Molecular Genetics Group, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany; Kuss, Andreas W.; Human Molecular Genetics Group, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany; Kolb, Steffen; Thaer Institute, Faculty of Life Sciences, Humboldt University of Berlin, Berlin, Germany; Urich, Tim; Institute of Microbiology, Center for Functional Genomics of Microbes, University of Greifswald, Greifswald, Germany
AbstractSoil CH4 fluxes are driven by CH4-producing and -consuming microorganisms that determine whether soils are sources or sinks of this potent greenhouse gas. To date, a comprehensive understanding of underlying microbiome dynamics has rarely been obtained in situ. Using quantitative metatranscriptomics, we aimed to link CH4-cycling microbiomes to net surface CH4 fluxes throughout a year in two grassland soils. CH4 fluxes were highly dynamic: both soils were net CH4 sources in autumn and winter and sinks in spring and summer, respectively. Correspondingly, methanogen mRNA abundances per gram soil correlated well with CH4 fluxes. Methanotroph to methanogen mRNA ratios were higher in spring and summer, when the soils acted as net CH4 sinks. CH4 uptake was associated with an increased proportion of USCα and γ pmoA and pmoA2 transcripts. We assume that methanogen transcript abundance may be useful to approximate changes in net surface CH4 emissions from grassland soils. High methanotroph to methanogen ratios would indicate CH4 sink properties. Our study links for the first time the seasonal transcriptional dynamics of CH4-cycling soil microbiomes to gas fluxes in situ. It suggests mRNA transcript abundances as promising indicators of dynamic ecosystem-level processes.
Publication
Proposal and extensive test of a calibration protocol for crop phenology models
(2023) Wallach, Daniel; Palosuo, Taru; Thorburn, Peter; Mielenz, Henrike; Buis, Samuel; Hochman, Zvi; Gourdain, Emmanuelle; Andrianasolo, Fety; Dumont, Benjamin; Ferrise, Roberto; Gaiser, Thomas; Garcia, Cecile; Gayler, Sebastian; Harrison, Matthew; Hiremath, Santosh; Horan, Heidi; Hoogenboom, Gerrit; Jansson, Per-Erik; Jing, Qi; Justes, Eric; Kersebaum, Kurt-Christian; Launay, Marie; Lewan, Elisabet; Liu, Ke; Mequanint, Fasil; Moriondo, Marco; Nendel, Claas; Padovan, Gloria; Qian, Budong; Schütze, Niels; Seserman, Diana-Maria; Shelia, Vakhtang; Souissi, Amir; Specka, Xenia; Srivastava, Amit Kumar; Trombi, Giacomo; Weber, Tobias K. D.; Weihermüller, Lutz; Wöhling, Thomas; Seidel, Sabine J.; Wallach, Daniel; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany; Palosuo, Taru; Natural Resources Institute Finland (Luke), Helsinki, Finland; Thorburn, Peter; CSIRO Agriculture and Food, Brisbane, Australia; Mielenz, Henrike; Institute for Crop and Soil Science, Julius Kühn Institute (JKI) – Federal Research Centre for Cultivated Plants, Braunschweig, Germany; Buis, Samuel; INRAE, UMR 1114 EMMAH, Avignon, France; Hochman, Zvi; CSIRO Agriculture and Food, Brisbane, Australia; Gourdain, Emmanuelle; ARVALIS - Institut du végétal Paris, Paris, France; Andrianasolo, Fety; ARVALIS - Institut du végétal Paris, Paris, France; Dumont, Benjamin; Plant Sciences & TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium; Ferrise, Roberto; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy; Gaiser, Thomas; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany; Garcia, Cecile; ARVALIS - Institut du végétal Paris, Paris, France; Gayler, Sebastian; Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany; Harrison, Matthew; Tasmanian Institute of Agriculture, University of Tasmania, Launceston, Tasmania, Australia; Hiremath, Santosh; Aalto University School of Science, Espoo, Finland; Horan, Heidi; CSIRO Agriculture and Food, Brisbane, Australia; Hoogenboom, Gerrit; Global Food Systems Institute, University of Florida, Gainesville, USA; Jansson, Per-Erik; Royal Institute of Technology (KTH), Stockholm, Sweden; Jing, Qi; Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada; Justes, Eric; PERSYST Department, CIRAD, Montpellier, France; Kersebaum, Kurt-Christian; Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany; Launay, Marie; INRAE, US 1116 AgroClim, Avignon, France; Lewan, Elisabet; Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden; Liu, Ke; Tasmanian Institute of Agriculture, University of Tasmania, Launceston, Tasmania, Australia; Mequanint, Fasil; Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Stuttgart, Germany; Moriondo, Marco; CNR-IBE, Firenze, Italy; Nendel, Claas; Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany; Padovan, Gloria; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy; Qian, Budong; Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada; Schütze, Niels; Institute of Hydrology and Meteorology, Chair of Hydrology, Technische Universität Dresden, Dresden, Germany; Seserman, Diana-Maria; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Shelia, Vakhtang; Global Food Systems Institute, University of Florida, Gainesville, USA; Souissi, Amir; Swift Current Research and Development Centre, Agriculture and Agri-Food Canada, Swift Current, Canada; Specka, Xenia; Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Srivastava, Amit Kumar; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany; Trombi, Giacomo; Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy; Weber, Tobias K. D.; Faculty of Organic Agriculture, Soil Science Section, University of Kassel, Witzenhausen, Germany; Weihermüller, Lutz; Institute of Bio- and Geosciences - IBG-3, Agrosphere, Forschungszentrum Jülich GmbH, Jülich, Germany; Wöhling, Thomas; Lincoln Agritech Ltd., Hamilton, New Zealand; Seidel, Sabine J.; Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.
Publication
Demokratiezufriedenheit und Institutionenvertrauen in Baden-Württemberg
(2023) Vetter, Angelika; Brettschneider, Frank; Vetter, Angelika; Institut für Sozialwissenschaften, Universität Stuttgart, Stuttgart, Deutschland; Brettschneider, Frank; Institut für Kommunikationswissenschaft, Universität Hohenheim, Stuttgart, Deutschland
In Baden-Württemberg sind vor allem jene Menschen mit dem Funktionieren der Demokratie zufrieden, die die Wirtschaftslage positiv einschätzen, die das Gefühl haben, dass sich Politik responsiv verhält, und die einer Regierungspartei zuneigen. Sie vertrauen auch politischen Institutionen eher. Ferner stärkt dialogische Bürgerbeteiligung sowohl die Demokratiezufriedenheit als auch das Institutionenvertrauen von Menschen. Allerdings nicht immer. Vor allem die Zufriedenheit mit dem Beteiligungsverfahren ist wichtig. Erst danach spielt die Zufriedenheit mit dem Ergebnis der Beteiligung eine Rolle. Auch bei Menschen, die nicht einer der Regierungsparteien zuneigen, stärkt dialogische Beteiligung die Demokratiezufriedenheit und das Vertrauen. Allerdings: Bewerten Teilnehmende an Bürgerbeteiligung sowohl das Verfahren als auch das Ergebnis negativ, dann sind ihre Demokratiezufriedenheit und ihr Institutionenvertrauen sogar geringer als bei jenen, die nicht an Bürgerbeteiligung teilgenommen haben. Diesen Analysen liegen repräsentative Umfragen aus den Jahren 2021 und 2022 in Baden-Württemberg zugrunde.
Publication
Diagnosing similarities in probabilistic multi-model ensembles: An application to soil–plant-growth-modeling
(2022) Schäfer Rodrigues Silva, Aline; Weber, Tobias K. D.; Gayler, Sebastian; Guthke, Anneli; Höge, Marvin; Nowak, Wolfgang; Streck, Thilo; Schäfer Rodrigues Silva, Aline; Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems/Cluster of Excellence “Data-Integrated Simulation Science”, University of Stuttgart, Stuttgart, Germany; Weber, Tobias K. D.; Department of Biogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany; Gayler, Sebastian; Department of Biogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany; Guthke, Anneli; Junior Research Group for Statistical Model-Data Integration, Cluster of Excellence “Data-Integrated Simulation Science”, University of Stuttgart, Stuttgart, Germany; Höge, Marvin; Department of Systems Analysis, Integrated Assessment and Modelling, Eawag-Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland; Nowak, Wolfgang; Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems/Cluster of Excellence “Data-Integrated Simulation Science”, University of Stuttgart, Stuttgart, Germany; Streck, Thilo; Department of Biogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany
There has been an increasing interest in using multi-model ensembles over the past decade. While it has been shown that ensembles often outperform individual models, there is still a lack of methods that guide the choice of the ensemble members. Previous studies found that model similarity is crucial for this choice. Therefore, we introduce a method that quantifies similarities between models based on so-called energy statistics. This method can also be used to assess the goodness-of-fit to noisy or deterministic measurements. To guide the interpretation of the results, we combine different visualization techniques, which reveal different insights and thereby support the model development. We demonstrate the proposed workflow on a case study of soil–plant-growth modeling, comparing three models from the Expert-N library. Results show that model similarity and goodness-of-fit vary depending on the quantity of interest. This confirms previous studies that found that “there is no single best model” and hence, combining several models into an ensemble can yield more robust results.