Preventing Alzheimer's Disease

http://dx.doi.org/10.1126/science.122854

A Coalescent Sampler Successfully Detects Biologically Meaningful Population Structure Overlooked by F‐Statistics

Assessing the geographic structure of populations has relied heavily on Sewell Wright\u27s F‐statistics and their numerous analogues for many decades. However, it is well appreciated that, due to their nonlinear relationship with gene flow, F statistics frequently fail to reject the null model of panmixia in species with relatively high levels of gene flow and large population sizes. Coalescent genealogy samplers instead allow a model‐selection approach to the characterization of population structure, thereby providing the opportunity for stronger inference. Here, we validate the use of coalescent samplers in a high gene flow context using simulations of a stepping‐stone model. In an example case study, we then re‐analyze genetic datasets from 41 marine species sampled from throughout the Hawaiian archipelago using coalescent model selection. Due to the archipelago\u27s linear nature, it is expected that most species will conform to some sort of stepping‐stone model (leading to an expected pattern of isolation by distance), but F‐statistics have only supported this inference in ~10% of these datasets. Our simulation analysis shows that a coalescent sampler can make a correct inference of stepping‐stone gene flow in nearly 100% of cases where gene flow is ≤100 migrants per generation (equivalent to FST = 0.002), while F‐statistics had mixed results. Our re‐analysis of empirical datasets found that nearly 70% of datasets with an unambiguous result fit a stepping‐stone model with varying population sizes and rates of gene flow, although 37% of datasets yielded ambiguous results. Together, our results demonstrate that coalescent samplers hold great promise for detecting weak but meaningful population structure, and defining appropriate management units

Transforming the Energy Landscape of a Coiled-Coil Peptide via Point Mutations

We analyze the effect of point mutations on the energy landscape of a coiled-coil peptide, GCN4-pLI, where the native state is a parallel tetrameric configuration formed from two identical dimers. Experimentally, a single mutation, E20S, supports both antiparallel and parallel structures. Here, we analyze the potential energy landscapes of the dimeric units for the parent sequence and four mutants, namely E20S, E20A, E20P, and E20G. Despite sharing characteristic funnels containing the parallel and antiparallel structures, the point mutations change some parts of the landscape quite dramatically, and we predict new intermediate structures and characterize the associated heat capacities. For the mutants we predict that kinked intermediate structures facilitate the transition between parallel and antiparallel morphologies, in contrast to the parent sequence. Furthermore, we predict a change from a multifunnel energy landscape in the E20S mutant to a landscape dominated by an underlying single funnel in the parent sequence, with accompanying heat capacity signatures. Our results imply that changes in the landscape due to mutations might provide useful tools for functional protein design.K.R. gratefully acknowledges the Engineering and Physical Sciences Research Council for financial support

Evolving coral reef conservation with genetic information

Targeted conservation and management programs are crucial for mitigating anthropogenic threats to declining biodiversity. Although evolutionary processes underpin extant patterns of biodiversity, it is uncommon for resource managers to explicitly consider genetic data in conservation prioritization. Genetic information is inherently relevant to management because it describes genetic diversity, population connectedness, and evolutionary history; thereby typifying their behavioral traits, physiological climate tolerance, evolutionary potential, and dispersal ability. Incorporating genetic information into spatial conservation prioritization starts with reconciling the terminology and techniques used in genetics and conservation science. Genetic data vary widely in analyses and their interpretations can be challenging even for experienced geneticists. Therefore, identifying objectives, decision rules, and implementations in decision support tools specifically for management using genetic data is challenging. Here, we outline a framework for eight genetic system characteristics, their measurement, and how they could be incorporated in spatial conservation prioritization for two contrasting objectives: biodiversity preservation vs maintaining ecological function and sustainable use. We illustrate this framework with an example using data from Tridacna crocea (Lamarck, 1819) (boring giant clam) in the Coral Triangle. We find that many reefs highlighted as conservation priorities with genetic data based on genetic subregions, genetic diversity, genetic distinctness, and connectivity are not prioritized using standard practices. Moreover, different characteristics calculated from the same samples resulted in different spatial conservation priorities. Our results highlight that omitting genetic information from conservation decisions may fail to adequately represent processes regulating biodiversity, but that conservation objectives related to the choice of genetic system characteristics require careful consideration

Prion protein interacts with bace1 and differentially regulates its activity towards wild type and swedish mutant amyloid precursor protein

In Alzheimer disease amyloid-β (Aβ) peptides derived from the amyloid precursor protein (APP) accumulate in the brain. Cleavage of APP by the β-secretase BACE1 is the rate-limiting step in the production of Aβ. We have reported previously that the cellular prion protein (PrP(C)) inhibited the action of BACE1 toward human wild type APP (APP(WT)) in cellular models and that the levels of endogenous murine Aβ were significantly increased in PrP(C)-null mouse brain. Here we investigated the molecular and cellular mechanisms underlying this observation. PrP(C) interacted directly with the prodomain of the immature Golgi-localized form of BACE1. This interaction decreased BACE1 at the cell surface and in endosomes where it preferentially cleaves APP(WT) but increased it in the Golgi where it preferentially cleaves APP with the Swedish mutation (APP(Swe)). In transgenic mice expressing human APP with the Swedish and Indiana familial mutations (APP(Swe,Ind)), PrP(C) deletion had no influence on APP proteolytic processing, Aβ plaque deposition, or levels of soluble Aβ or Aβ oligomers. In cells, although PrP(C) inhibited the action of BACE1 on APP(WT), it did not inhibit BACE1 activity toward APP(Swe). The differential subcellular location of the BACE1 cleavage of APP(Swe) relative to APP(WT) provides an explanation for the failure of PrP(C) deletion to affect Aβ accumulation in APP(Swe,Ind) mice. Thus, although PrP(C) exerts no control on cleavage of APP(Swe) by BACE1, it has a profound influence on the cleavage of APP(WT), suggesting that PrP(C) may be a key protective player against sporadic Alzheimer disease

Peroxisome Proliferator-Activated Receptor gamma enhances the activity of a insulin degrading enzyme-like metalloprotease for amyloid-beta clearance.

Peroxisome proliferator-activated receptor gamma (PPARgamma) activation results in an increased rate of amyloid-beta (Abeta) clearance from the media of diverse cells in culture, including primary neurons and glial cells. Here, we further investigate the mechanism for Abeta clearance and found that PPARgamma activation modulates a cell surface metalloprotease that can be inhibited by metalloprotease inhibitors, like EDTA and phenanthroline, and also by the peptide hormones insulin and glucagon. The metalloprotease profile of the Abeta-degrading mechanism is surprisingly similar to insulin-degrading enzyme (IDE). This mechanism is maintained in hippocampal and glia primary cultures from IDE loss-of-function mice. We conclude that PPARgamma activates an IDE-like Abeta degrading activity. Our work suggests a drugable pathway that can clear Abeta peptide from the brain

Knowledge sharing about deep-sea ecosystems to inform conservation and research decisions

The Marianas Trench Marine National Monument (MNM) currently extends policy-based protection to deep-sea ecosystems contained within it, but managers require better understanding of the current knowledge and knowledge gaps about these ecosystems to guide decision-making. To address this need, we present a case study of the Marianas Trench MNM using in-depth interviews to determine scientists’ (1) current understanding of anthropogenic drivers of change and system vulnerability in deep-sea ecosystems; and (2) perceptions of the least understood deep-sea ecosystems and processes in the Marianas Trench MNM, and which of these, if any, should be research priorities to fill knowledge gaps about these systems and the impacts from anthropogenic drivers of change. Interview respondents shared similar views on the current knowledge of deep-sea ecosystems and potential anthropogenic drivers of change in the Marianas Trench MNM. Respondents also identified trench and deep pelagic (bathyal, abyssal, and hadal zones) ecosystems as the least understood, and highlighted climate change, litter and waste, mining and fishing, and interactions between these drivers of change as critical knowledge gaps. To fill key knowledge gaps and inform conservation decision-making, respondents identified the need for monitoring networks and time-series data. Our approach demonstrates how in-depth interviews can be used to elicit knowledge to inform decision-making in data-limited situations

Ocean currents help explain population genetic structure

Management and conservation can be greatly informed by considering explicitly how environmental factors influence population genetic structure. Using simulated larval dispersal estimates based on ocean current observations, we demonstrate how explicit consideration of frequency of exchange of larvae among sites via ocean advection can fundamentally change the interpretation of empirical population genetic structuring as compared with conventional spatial genetic analyses. Both frequency of larval exchange and empirical genetic difference were uncorrelated with Euclidean distance between sites. When transformed into relative oceanographic distances and integrated into a genetic isolation-by-distance framework, however, the frequency of larval exchange explained nearly 50 per cent of the variance in empirical genetic differences among sites over scales of tens of kilometres. Explanatory power was strongest when we considered effects of multiple generations of larval dispersal via intermediary locations on the long-term probability of exchange between sites. Our results uncover meaningful spatial patterning to population genetic structuring that corresponds with ocean circulation. This study advances our ability to interpret population structure from complex genetic data characteristic of high gene flow species, validates recent advances in oceanographic approaches for assessing larval dispersal and represents a novel approach to characterize population connectivity at small spatial scales germane to conservation and fisheries management