Lipid membranes accelerate amyloid formation in the mouse model of AA amyloidosis

Introduction: AA amyloidosis develops as a result of prolonged inflammation and is characterized by deposits of N-terminal proteolytic fragments of the acute phase reactant serum amyloid A (SAA). Macrophages are usually found adjacent to amyloid, suggesting their involvement in the formation and/or degradation of the amyloid fibrils. Furthermore, accumulating evidence suggests that lipid membranes accelerate the fibrillation of different amyloid proteins. Methods: Using an experimental mouse model of AA amyloidosis, we compared the amyloidogenic effect of liposomes and/or amyloid-enhancing factor (AEF). Inflammation was induced by subcutaneous injection of silver nitrate followed by intravenous injection of liposomes and/or AEF to accelerate amyloid formation. Results: We showed that liposomes accelerate amyloid formation in inflamed mice, but the amyloidogenic effect of liposomes was weaker compared with AEF. Regardless of the induction method, amyloid deposits were mainly found in the marginal zones of the spleen and coincided with the depletion of marginal zone macrophages, while red pulp macrophages and metallophilic marginal zone macrophages proved insensitive to amyloid deposition. Conclusions: We conclude that increased intracellular lipid content facilitates AA amyloid fibril formation and show that the mouse model of AA amyloidosis is a suitable system for further mechanistic studies

Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method

Canopy nitrogen (N) influences carbon (C) uptake by vegetation through its important role in photosynthetic enzymes. Global Vegetation Models (GVMs) predict C assimilation, but are limited by a lack spatial canopy N input. Mapping canopy N has been done in various ecosystems using remote sensing (RS) products, but has rarely considered environmental variables as additional predictors. Our research objective was to estimate spatial patterns of canopy N in European forests and to investigate the degree to which including environmental variables among the predictors would improve the models compared to using remotely sensed products alone. The environmental variables included were climate, soil properties, altitude, N deposition and land cover, while the remote sensing products were vegetation indices and NIR reflectance from MODIS and MERIS sensors, the MOD13Q1 and MTCI products, respectively. The results showed that canopy N could be estimated both within and among forest types using the random forests technique and calibration data from ICP Forests with good accuracy (r2 = 0.62, RRMSE = 0.18). The predicted spatial pattern shows higher canopy N in mid-western Europe and relatively lower values in both southern and northern Europe. For all subgroups tested (All plots, Evergreen Needleleaf Forest (ENF) plots and Deciduous Broadleaf Forest (DBF) plots), including environmental variables improved the predictions. Including environmental variables was especially important for the DBF plots, as the prediction model based on remotely sensed data products predicted canopy N with the lowest accuracy

Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method

Canopy nitrogen (N) influences carbon (C) uptake by vegetation through its important role in photosynthetic enzymes. Global Vegetation Models (GVMs) predict C assimilation, but are limited by a lack spatial canopy N input. Mapping canopy N has been done in various ecosystems using remote sensing (RS) products, but has rarely considered environmental variables as additional predictors. Our research objective was to estimate spatial patterns of canopy N in European forests and to investigate the degree to which including environmental variables among the predictors would improve the models compared to using remotely sensed products alone. The environmental variables included were climate, soil properties, altitude, N deposition and land cover, while the remote sensing products were vegetation indices and NIR reflectance from MODIS and MERIS sensors, the MOD13Q1 and MTCI products, respectively. The results showed that canopy N could be estimated both within and among forest types using the random forests technique and calibration data from ICP Forests with good accuracy (r2 = 0.62, RRMSE = 0.18). The predicted spatial pattern shows higher canopy N in mid-western Europe and relatively lower values in both southern and northern Europe. For all subgroups tested (All plots, Evergreen Needleleaf Forest (ENF) plots and Deciduous Broadleaf Forest (DBF) plots), including environmental variables improved the predictions. Including environmental variables was especially important for the DBF plots, as the prediction model based on remotely sensed data products predicted canopy N with the lowest accuracy

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present