Geschmack ist vorhersagbar: Mit FlavorMiner. FlavorMiner heißt das Tool, das IPB-Chemiker und Partner aus Kolumbien jüngst entwickelt haben. Das Programm kann, basierend auf maschinellem Lernen (KI), anhand der…
Seit Februar 2021 bietet Wolfgang Brandt, ehemaliger Leiter der Arbeitsgruppe Computerchemie am IPB, sein Citizen Science-Projekt zur Pilzbestimmung an. Dafür hat er in regelmäßigen Abständen öffentliche Vorträge zur Vielfalt…
Walker, T. W. N.; Alexander, J. M.; Allard, P.-M.; Baines, O.; Baldy, V.; Bardgett, R. D.; Capdevila, P.; Coley, P. D.; David, B.; Defossez, E.; Endara, M.; Ernst, M.; Fernandez, C.; Forrister, D.; Gargallo‐Garriga, A.; Jassey, V. E. J.; Marr, S.; Neumann, S.; Pellissier, L.; Peñuelas, J.; Peters, K.; Rasmann, S.; Roessner, U.; Sardans, J.; Schrodt, F.; Schuman, M. C.; Soule, A.; Uthe, H.; Weckwerth, W.; Wolfender, J.; Dam, N. M.; Salguero‐Gómez, R.;Functional Traits 2.0: The power of the metabolome for ecologyJ. Ecol.1104-20(2022)DOI: 10.1111/1365-2745.13826
1. A major aim of ecology is to upscale attributes of individuals to understand processes at population, community and ecosystem scales. Such attributes are typically described using functional traits, that is, standardised characteristics that impact fitness via effects on survival, growth and/or reproduction. However, commonly used functional traits (e.g. wood density, SLA) are becoming increas-ingly criticised for not being truly mechanistic and for being questionable pre-dictors of ecological processes.2. This Special Feature reviews and studies how the metabolome (i.e. the thousands of unique metabolites that underpin physiology) can enhance trait-based ecology and our understanding of plant and ecosystem functioning.3. In this Editorial, we explore how the metabolome relates to plant functional traits, with reference to life-history trade-offs governing fitness between generations and plasticity shaping fitness within generations. We also identify solutions to challenges of acquiring, interpreting and contextualising metabolome data, and propose a roadmap for integrating the metabolome into ecology. 4. We next summarise the seven studies composing the Special Feature, which use the metabolome to examine mechanisms behind plant community assembly, plant-organismal interactions and effects of plants and soil micro-organisms on ecosystem processes. 5. Synthesis. We demonstrate the potential of the metabolome to improve mechanistic and predictive power in ecology by providing a high-resolution coupling between physiology and fitness. However, applying metabolomics to ecological questions is currently limited by a lack of conceptual, technical and data frameworks, which needs to be overcome to realise the full potential of the metabolome for ecology.
Bücher und Buchkapitel
Weckwerth, W.; Wienkoop, S.; Hoehenwarter, W.; Egelhofer, V.; Sun, X.;From Proteomics to Systems Biology: MAPA, MASS WESTERN, PROMEX, and COVAIN as a User-Oriented PlatformJorrin-Novo, J. V., et al., eds.Methods Mol. Biol.107215-27(2014)ISBN:978-1-62703-631-3DOI: 10.1007/978-1-62703-631-3_2
Genome sequencing and systems biology are revolutionizing life sciences. Proteomics emerged as a fundamental technique of this novel research area as it is the basis for gene function analysis and modeling of dynamic protein networks. Here a complete proteomics platform suited for functional genomics and systems biology is presented. The strategy includes MAPA (mass accuracy precursor alignment; http://www.univie.ac.at/mosys/software.html) as a rapid exploratory analysis step; MASS WESTERN for targeted proteomics; COVAIN (http://www.univie.ac.at/mosys/software.html) for multivariate statistical analysis, data integration, and data mining; and PROMEX (http://www.univie.ac.at/mosys/databases.html) as a database module for proteogenomics and proteotypic peptides for targeted analysis. Moreover, the presented platform can also be utilized to integrate metabolomics and transcriptomics data for the analysis of metabolite–protein–transcript correlations and time course analysis using COVAIN. Examples for the integration of MAPA and MASS WESTERN data, proteogenomic and metabolic modeling approaches for functional genomics, phosphoproteomics by integration of MOAC (metal-oxide affinity chromatography) with MAPA, and the integration of metabolomics, transcriptomics, proteomics, and physiological data using this platform are presented. All software and step-by-step tutorials for data processing and data mining can be downloaded from http://www.univie.ac.at/mosys/software.html.
Bücher und Buchkapitel
Beckers, G. J. M.; Hoehenwarter, W.; Röhrig, H.; Conrath, U.; Weckwerth, W.;Tandem Metal-Oxide Affinity Chromatography for Enhanced Depth of Phosphoproteome AnalysisJorrin-Novo, J. V., et al., eds.Methods Mol. Biol.1072621-632(2014)ISBN:978-1-62703-631-3DOI: 10.1007/978-1-62703-631-3_42
In eukaryotic cells many diverse cellular functions are regulated by reversible protein phosphorylation. In recent years, phosphoproteomics has become a powerful tool to study protein phosphorylation because it allows unbiased localization, and site-specific quantification, of in vivo phosphorylation of hundreds of proteins in a single experiment. A common strategy to identify phosphoproteins and their phosphorylation sites from complex biological samples is the enrichment of phosphopeptides from digested cellular lysates followed by mass spectrometry. However, despite the high sensitivity of modern mass spectrometers the large dynamic range of protein abundance and the transient nature of protein phosphorylation remained major pitfalls in MS-based phosphoproteomics. Tandem metal-oxide affinity chromatography (MOAC) represents a robust and highly selective approach for the identification and site-specific quantification of low abundant phosphoproteins that is based on the successive enrichment of phosphoproteins and -peptides. This strategy combines protein extraction under denaturing conditions, phosphoprotein enrichment using Al(OH)3-based MOAC, tryptic digestion of enriched phosphoproteins followed by TiO2-based MOAC of phosphopeptides. Thus, tandem MOAC effectively targets the phosphate moiety of phosphoproteins and phosphopeptides and, thus, allows probing of the phosphoproteome to unprecedented depth.