Somerville, M. | bioRxiv Science

bioRxiv2025

Cytomegalovirus-induced T cell responses accelerate Alzheimer's disease progression in mice

Infections have long been implicated as causative factors in Alzheimers disease (AD). Multiple studies have further suggested a key role for herpesviruses, such as cytomegalovirus (CMV). Using transgenic 3xTg-AD mice, we demonstrate that systemic infection with the {beta}-herpesvirus murine cytomegalovirus (MCMV) accelerates the development of cognitive decline, tauopathy, and synaptic loss in the hippocampus, all of which are key features of AD. Accelerated disease progression after infection was associated with substantial lymphocyte infiltration into the brain dominated by CD8+ T cells specific for MCMV. Moreover, T cell receptor analyses revealed that these responses were clonally diverse, suggesting that multiple viral antigens were targeted in the brain during chronic infection with MCMV. T cell depletion during virus chronicity rescued infection-induced cognitive decline. In addition, antiviral drug treatment reduced lymphocytic infiltrates in the brain and reversed cognitive decline, suggesting potential clinical utility. These data provide a mechanistic link between chronic viral infections and the development of AD.

immunology↗

bioRxiv2019

Extending site-based observations to predict the spatial patterns of vegetation structure and composition

ContextConservation planning and land management are inherently spatial processes that are most effective when implemented over large areas.\n\nObjectivesOur objectives were to (i) use existing plot data to aggregate species inventories to growth forms and derive indicators of vegetation structure and composition and ii) generate spatially-explicit, continuous, landscape scaled models of these discrete vegetation indicators, accompanied by maps of model uncertainty.\n\nMethodUsing a case study from New South Wales, Australia, we aggregated floristic observations from 7234 sites into growth forms. We trained ensembles of artificial neural networks (ANN) to predict the distribution of these indicators over a broad region covering 11.5 million hectares. Importantly, we show spatially explicit models of uncertainty so that end-users have a tangible and transparent means of assessing models.\n\nResultsOur key findings were firstly, widely available site-based floristic records can be used to derive aggregated indicators of the structure and composition of plant growth forms. Secondly, ANNs are a powerful method to predict continuous patterns in complex, non-linear data (Pearsons correlation coefficient 0.83 (total native vegetation cover) to 0.42 (forb cover)). Thirdly, maps of the standardised residual error give insight into model performance and provide an assessment of model uncertainty in specific locations.\n\nConclusionsSpatially explicit, continuous representations of vegetation composition and structural complexity can add considerable value to conventional maps of vegetation extent or community type. This application has the potential to enhance the capacity for conservation planners, landscape managers and policy-makers to make informed decisions across landscape and regional scales.

ecology↗