UDP‐Api/UDP‐Xyl synthases affect plant development by controlling the content of UDP‐Api to regulate the RG‐II‐borate complex

Rhamnogalacturonan‐II (RG‐II) is structurally the most complex glycan in higher plants, containing 13 different sugars and 21 distinct glycosidic linkages. Two monomeric RG‐II molecules can form an RG‐II‐borate diester dimer through the two apiosyl (Api) residues of side chain A to regulate cross‐linking of pectin in the cell wall. But the relationship of Api biosynthesis and RG‐II dimer is still unclear. In this study we investigated the two homologous UDP‐D‐apiose/UDP‐D‐xylose synthases (AXSs) in Arabidopsis thaliana that synthesize UDP‐D‐apiose (UDP‐Api). Both AXSs are ubiquitously expressed, while AXS2 has higher overall expression than AXS1 in the tissues analyzed. The homozygous axs double mutant is lethal, while heterozygous axs1/+ axs2 and axs1 axs2/+ mutants display intermediate phenotypes. The axs1/+ axs2 mutant plants are unable to set seed and die. By contrast, the axs1 axs2/+ mutant plants exhibit loss of shoot and root apical dominance. UDP‐Api content in axs1 axs2/+ mutants is decreased by 83%. The cell wall of axs1 axs2/+ mutant plants is thicker and contains less RG‐II‐borate complex than wild‐type Col‐0 plants. Taken together, these results provide direct evidence of the importance of AXSs for UDP‐Api and RG‐II‐borate complex formation in plant growth and development.     

Shifts in plant functional community composition under hydrological stress strongly decelerate litter decomposition

Litter decomposition is a key process of nutrient and carbon cycling in terrestrial ecosystems. The decomposition process will likely be altered under ongoing climate change, both through direct effects on decomposer activity and through indirect effects caused by changes in litter quality. We studied how hydrological change indirectly affects decomposition via plant functional community restructuring caused by changes in plant species’ relative abundances (community‐weighted mean (CWM) traits and functional diversity). We further assessed how those indirect litter quality effects compare to direct effects. We set up a mesocosm experiment, in which sown grassland communities and natural turf pieces were subjected to different hydrological conditions (dryness and waterlogging) for two growing seasons. Species‐level mean traits were obtained from trait databases and combined with species’ relative abundances to assess functional community restructuring. We studied decomposition of mixed litter from these communities in a common “litterbed.” These indirect effects were compared to effects of different hydrological conditions on soil respiration and on decomposition of standard litter (direct effects). Dryness reduced biomass production in sown communities and natural turf pieces, while waterlogging only reduced biomass in sown communities. Hydrological stress caused profound shifts in species’ abundances and consequently in plant functional community composition. Hydrologically stressed communities had higher CMW leaf dry matter content, lower CMW leaf nitrogen content, and lower functional diversity. Lower CWM leaf N content and functional diversity were strongly related to slower decomposition. These indirect effects paralleled direct effects, but were larger and longer‐lasting. Species mean traits from trait databases had therefore considerable predictive power for decomposition. Our results show that stressful soil moisture conditions, that are likely to occur more frequently in the future, quickly shift species’ abundances. The resulting functional community restructuring will decelerate decomposition under hydrological stress.     

Evaluating the effectiveness of retention forestry to enhance biodiversity in production forests of Central Europe using an interdisciplinary,multi‐scale approach

Retention forestry, which retains a portion of the original stand at the time of harvesting to maintain continuity of structural and compositional diversity, has been originally developed to mitigate the impacts of clear‐cutting. Retention of habitat trees and deadwood has since become common practice also in continuous‐cover forests of Central Europe. While the use of retention in these forests is plausible, the evidence base for its application is lacking, trade‐offs have not been quantified, it is not clear what support it receives from forest owners and other stakeholders and how it is best integrated into forest management practices. The Research Training Group ConFoBi (Conservation of Forest Biodiversity in Multiple‐use Landscapes of Central Europe) focusses on the effectiveness of retention forestry, combining ecological studies on forest biodiversity with social and economic studies of biodiversity conservation across multiple spatial scales. The aim of ConFoBi is to assess whether and how structural retention measures are appropriate for the conservation of forest biodiversity in uneven‐aged and selectively harvested continuous‐cover forests of temperate Europe. The study design is based on a pool of 135 plots (1 ha) distributed along gradients of forest connectivity and structure. The main objectives are (a) to investigate the effects of structural elements and landscape context on multiple taxa, including different trophic and functional groups, to evaluate the effectiveness of retention practices for biodiversity conservation; (b) to analyze how forest biodiversity conservation is perceived and practiced, and what costs and benefits it creates; and (c) to identify how biodiversity conservation can be effectively integrated in multi‐functional forest management. ConFoBi will quantify retention levels required across the landscape, as well as the socio‐economic prerequisites for their implementation by forest owners and managers. ConFoBi's research results will provide an evidence base for integrating biodiversity conservation into forest management in temperate forests.     

Evading resistance to gene drives

Gene drives offer the possibility of altering and even suppressing wild populations of countless plant and animal species, and CRISPR technology now provides the technical feasibility of engineering them. However, population-suppression gene drives are prone to select resistance, should it arise. Here, we develop mathematical and computational models to identify conditions under which suppression drives will evade resistance, even if resistance is present initially. Previous models assumed resistance is allelic to the drive. We relax this assumption and show that linkage between the resistance and drive loci is critical to the evolution of resistance and that evolution of resistance requires (negative) linkage disequilibrium between the two loci. When the two loci are unlinked or only partially so, a suppression drive that causes limited inviability can evolve to fixation while causing only a minor increase in resistance frequency. Once fixed, the drive allele no longer selects resistance. Our analyses suggest that among gene drives that cause moderate suppression, toxin-antidote systems are less apt to select for resistance than homing drives. Single drives of moderate effect might cause only moderate population suppression, but multiple drives (perhaps delivered sequentially) would allow arbitrary levels of suppression. The most favorable case for evolution of resistance appears to be with suppression homing drives in which resistance is dominant and fully suppresses transmission distortion; partial suppression by resistance heterozygotes or recessive resistance are less prone to resistance evolution. Given that it is now possible to engineer CRISPR-based gene drives capable of circumventing allelic resistance, this design may allow for the engineering of suppression gene drives that are effectively resistance-proof.     

Neurotransmitter-Triggered Transfer of Exosomes Mediates Oligodendrocyte–Neuron Communication

Reciprocal interactions between neurons and oligodendrocytes are not only crucial for myelination, but also for long-term survival of axons. Degeneration of axons occurs in several human myelin diseases, however the molecular mechanisms of axon-glia communication maintaining axon integrity are poorly understood. Here, we describe the signal-mediated transfer of exosomes from oligodendrocytes to neurons. These endosome-derived vesicles are secreted by oligodendrocytes and carry specific protein and RNA cargo. We show that activity-dependent release of the neurotransmitter glutamate triggers oligodendroglial exosome secretion mediated by Ca²⁺ entry through oligodendroglial NMDA and AMPA receptors. In turn, neurons internalize the released exosomes by endocytosis. Injection of oligodendroglia-derived exosomes into the mouse brain results in functional retrieval of exosome cargo in neurons. Supply of cultured neurons with oligodendroglial exosomes improves neuronal viability under conditions of cell stress. These findings indicate that oligodendroglial exosomes participate in a novel mode of bidirectional neuron-glia communication contributing to neuronal integrity.     

Combination of a Proteomics Approach and Reengineering of Meso Scale Network Models for Prediction of Mode-of-Action for Tyrosine Kinase Inhibitors

In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs.