Ota, N. | Search bioRxiv

bioRxiv2018

Coffee polyphenols prevent cognitive dysfunction and suppress amyloid β plaques in APP/PS2 transgenic mouse

Epidemiological studies have found that habitual coffee consumption may reduce the risk of Alzheimers disease. Coffee contains numerous phenolic compounds (coffee polyphenols) such as chlorogenic acids. However, evidence demonstrating the contribution of chlorogenic acids in preventing cognitive dysfunction induced by Alzheimers disease is limited. In this study, we investigated the effect of chlorogenic acids on prevention of cognitive dysfunction in APP/PS2 transgenic mouse model of Alzheimers disease. Five-week-old APP/PS2 mice were administered a diet supplemented with coffee polyphenols daily for 5 months. The memory and cognitive function of mice was determined using the novel object recognition test, the Morris water maze test, and the step-through passive avoidance test. We found that chronic treatment with coffee polyphenols prevented cognitive dysfunction and significantly reduced hippocampal A{beta} deposition. We then determined the effect of 5-caffeoylquinic acid, one of the primary components of coffee polyphenols, on A{beta} formation. 5-Caffeoylquinic acid did not inhibit A{beta} fibrillation, but degraded A{beta} fibrils in a dose-dependent manner. In conclusion, these results demonstrate that coffee polyphenols prevented cognitive deficits and alleviated A{beta} plaque deposition via disaggregation of A{beta} in APP/PS2 mouse.

animal behavior and cognition↗

bioRxiv2018

Supporting global biodiversity assessment through high-resolution macroecological modelling: Methodological underpinnings of the BILBI framework

AimGlobal indicators of change in the state of terrestrial biodiversity are often derived by intersecting observed or projected changes in the distribution of habitat transformation, or of protected areas, with underlying patterns in the distribution of biodiversity. However the two main sources of data used to account for biodiversity patterns in such assessments - i.e. ecoregional boundaries, and vertebrate species ranges - are typically delineated at a much coarser resolution than the spatial grain of key ecological processes shaping both land-use and biological distributions at landscape scale. Species distribution modelling provides one widely used means of refining the resolution of mapped species distributions, but is limited to a subset of species which is biased both taxonomically and geographically, with some regions of the world lacking adequate data to generate reliable models even for better-known biological groups.\n\nInnovationMacroecological modelling of collective properties of biodiversity (e.g. alpha and beta diversity) as a correlative function of environmental predictors offers an alternative, yet highly complementary, approach to refining the spatial resolution with which patterns in the distribution of biodiversity can be mapped across our planet. Here we introduce a new capability - BILBI (the Biogeographic Infrastructure for Large-scaled Biodiversity Indicators) - which has implemented this approach by integrating advances in macroecological modelling, biodiversity informatics, remote sensing and high-performance computing to assess spatial-temporal change in biodiversity at ~1km grid resolution across the entire terrestrial surface of the planet. The initial implementation of this infrastructure focuses on modelling beta-diversity patterns using a novel extension of generalised dissimilarity modelling (GDM) designed to extract maximum value from sparsely and unevenly distributed occurrence records for over 400,000 species of plants, invertebrates and vertebrates.\n\nMain conclusionsModels generated by BILBI greatly refine the mapping of beta-diversity patterns relative to more traditional biodiversity surrogates such as ecoregions. This capability is already proving of considerable value in informing global biodiversity assessment through: 1) generation of indicators of past-to-present change in biodiversity based on observed changes in habitat condition and protected-area coverage; and 2) projection of potential future change in biodiversity as a consequence of alternative scenarios of global change in drivers and policy options.

ecology↗