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Roessner, U.

Publications and source records attributed to Roessner, U..

2 recordsLinked to original sources

Phenotypic plasticity facilitates alterations in life-history strategies under combinations of environmental stresses

Plants developed various reversible and non-reversible acclimation mechanisms to cope with the multifaceted nature of abiotic stress combinations. We hypothesized that in order to endure these stress combinations, plants elicit distinctive acclimation strategies through specific trade-offs between reproduction and defense. To investigate Brachypodium distachyon acclimation strategies to combinations of salinity, drought and heat, we applied a system biology approach, integrating physiological, metabolic and transcriptional analyses. We analyzed the trade-offs among functional and performance traits, and their effects on plant fitness. A combination of drought and heat resulted in escape strategy, while under a combination of salinity and heat, plants exhibited avoidance strategy. On the other hand, under combinations of salinity and drought, with or without heat stress, plant fitness (i.e. germination rate of subsequent generation) was severely impaired. These results indicate that under combined stresses, plants life-history strategies were shaped by the limits of phenotypic and metabolic plasticity and the trade-offs between traits, thereby giving raise to distinct acclimations. Our findings provide a mechanistic understanding of plant acclimations to combinations of abiotic stresses and shed light on the different life-history strategies that can contribute to grass fitness and possibly to their dispersion under changing environments.

plant biology

Best Practice Data Life Cycle Approaches for the Life Sciences

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a life cycle view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain.\n\nHere we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on omics datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

bioinformatics