TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Enhancing GAT-3 in thalamic astrocytes promotes resilience to brain injury in rodents

Inflammatory processes induced by brain injury are important for recovery; however, when uncontrolled, inflammation can be deleterious, likely explaining why most anti-inflammatory treatments have failed to improve neurological outcomes after brain injury in clinical trials. In the thalamus, chronic activation of glial cells, a proxy of inflammation, has been suggested as an indicator of increased seizure risk and cognitive deficits that develop after cortical injury. Furthermore, lesions in the thalamus, more than other brain regions, have been reported in patients with viral infections associated with neurological deficits, such as SARS-CoV-2. However, the extent to which thalamic inflammation is a driver or by-product of neurological deficits remains unknown. Here, we found that thalamic inflammation in mice was sufficient to phenocopy the cellular and circuit hyperexcitability, enhanced seizure risk, and disruptions in cortical rhythms that develop after cortical injury. In our model, down-regulation of the GABA transporter GAT-3 in thalamic astrocytes mediated this neurological dysfunction. In addition, GAT-3 was decreased in regions of thalamic reactive astrocytes in mouse models of cortical injury. Enhancing GAT-3 in thalamic astrocytes prevented seizure risk, restored cortical states, and was protective against severe chemoconvulsant-induced seizures and mortality in a mouse model of traumatic brain injury, emphasizing the potential of therapeutically targeting this pathway. Together, our results identified a potential therapeutic target for reducing negative outcomes after brain injury

Microglia states and nomenclature: A field at its crossroads

Microglial research has advanced considerably in recent decades yet has been constrained by a rolling series of dichotomies such as "resting versus activated" and "M1 versus M2." This dualistic classification of good or bad microglia is inconsistent with the wide repertoire of microglial states and functions in development, plasticity, aging, and diseases that were elucidated in recent years. New designations continuously arising in an attempt to describe the different microglial states, notably defined using transcriptomics and proteomics, may easily lead to a misleading, although unintentional, coupling of categories and functions. To address these issues, we assembled a group of multidisciplinary experts to discuss our current understanding of microglial states as a dynamic concept and the importance of addressing microglial function. Here, we provide a conceptual framework and recommendations on the use of microglial nomenclature for researchers, reviewers, and editors, which will serve as the foundations for a future white paper