Preventing Alzheimer's Disease

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

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

Haplotype-based association analysis of general cognitive ability in Generation Scotland, the English Longitudinal Study of Ageing, and UK Biobank

Background: Cognitive ability is a heritable trait with a polygenic architecture, for which several associated variants have been identified using genotype-based and candidate gene approaches. Haplotype-based analyses are a complementary technique that take phased genotype data into account, and potentially provide greater statistical power to detect lower frequency variants. Methods: In the present analysis, three cohort studies (ntotal = 48,002) were utilised: Generation Scotland: Scottish Family Health Study (GS:SFHS), the English Longitudinal Study of Ageing (ELSA), and the UK Biobank. A genome-wide haplotype-based meta-analysis of cognitive ability was performed, as well as a targeted meta-analysis of several gene coding regions. Results: None of the assessed haplotypes provided evidence of a statistically significant association with cognitive ability in either the individual cohorts or the meta-analysis. Within the meta-analysis, the haplotype with the lowest observed P-value overlapped with the D-amino acid oxidase activator (DAOA) gene coding region. This coding region has previously been associated with bipolar disorder, schizophrenia and Alzheimer’s disease, which have all been shown to impact upon cognitive ability. Another potentially interesting region highlighted within the current genome-wide association analysis (GS:SFHS: P = 4.09 x 10-7), was the butyrylcholinesterase (BCHE) gene coding region. The protein encoded by BCHE has been shown to influence the progression of Alzheimer’s disease and its role in cognitive ability merits further investigation. Conclusions: Although no evidence was found for any haplotypes with a statistically significant association with cognitive ability, our results did provide further evidence that the genetic variants contributing to the variance of cognitive ability are likely to be of small effect

Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation

Discovery of biphenylacetamide-derived inhibitors of BACE1 using de novo structure-based molecular design

β-Secretase (BACE1), the enzyme responsible for the first and rate-limiting step in the production of amyloid-β peptides, is an attractive target for the treatment of Alzheimer’s disease. In this study, we report the application of the de novo fragment-based molecular design program SPROUT to the discovery of a series of nonpeptide BACE1 inhibitors based upon a biphenylacetamide scaffold. The binding affinity of molecules based upon this designed molecular scaffold was increased from an initial BACE1 IC50 of 323 μM to 27 μM following the synthesis of a library of optimized ligands whose structures were refined using the recently developed SPROUT-HitOpt software. Although a number of inhibitors were found to exhibit cellular toxicity, one compound in the series was found to have useful BACE1 inhibitory activity in a cellular assay with minimal cellular toxicity. This work demonstrates the power of an in silico fragment-based molecular design approach in the discovery of novel BACE1 inhibitors

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

Detection of Aβ plaque-associated astrogliosis in Alzheimer’s disease brain by spectroscopic imaging and immunohistochemistry

Recent work using micro-Fourier transform infrared (μFTIR) imaging has revealed that a lipid-rich layer surrounds many plaques in post-mortem Alzheimer’s brain. However, the origin of this lipid layer is not known, nor is its role in the pathogenesis of Alzheimer’s disease (AD). Here, we studied the biochemistry of plaques in situ using a model of AD. We combined FTIR, Raman and immunofluorescence images, showing that astrocyte processes co-localise with the lipid-ring surrounding many plaques. We used μFTIR imaging to rapidly measure chemical signatures of plaques over large fields of view, and selected plaques for higher resolution analysis with Raman. Raman maps showed similar lipid-rings and dense protein cores as in FTIR images, but also revealed cell bodies. We confirmed the presence of plaques using amylo-glo staining, and measured astrocytes using immunohistochemistry, revealing astrocyte colocalisation with lipid-rings. This work is important because it correlates biochemically changes surrounding the plaque with the biological process of astrogliosis

Drivers of Cape Verde archipelagic endemism in keyhole limpets

Oceanic archipelagos are the ideal setting for investigating processes that shape species assemblages. Focusing on keyhole limpets, genera Fissurella and Diodora from Cape Verde Islands, we used an integrative approach combining molecular phylogenetics with ocean transport simulations to infer species distribution patterns and analyse connectivity. Dispersal simulations, using pelagic larval duration and ocean currents as proxies, showed a reduced level of connectivity despite short distances between some of the islands. It is suggested that dispersal and persistence driven by patterns of oceanic circulation favouring self-recruitment played a primary role in explaining contemporary species distributions. Mitochondrial and nuclear data revealed the existence of eight Cape Verde endemic lineages, seven within Fissurella, distributed across the archipelago, and one within Diodora restricted to Boavista. The estimated origins for endemic Fissurella and Diodora were 10.2 and 6.7 MY, respectively. Between 9.5 and 4.5 MY, an intense period of volcanism in Boavista might have affected Diodora, preventing its diversification. Having originated earlier, Fissurella might have had more opportunities to disperse to other islands and speciate before those events. Bayesian analyses showed increased diversification rates in Fissurella possibly promoted by low sea levels during Plio-Pleistocene, which further explain differences in species richness between both genera.FCT - Portuguese Science Foundation [SFRH/BPD/109685/2015, SFRH/BPD/111003/2015]; Norte Portugal Regional Operational Program (NORTE), under the PORTUGAL Partnership Agreement, through the European Regional Development Fund (ERDF) [MARINFO - NORTE-01-0145-FEDER-000031]info:eu-repo/semantics/publishedVersio