Phenothiazine-mediated rescue of cognition in tau transgenic mice requires neuroprotection and reduced soluble tau burden

Abstract Background It has traditionally been thought that the pathological accumulation of tau in Alzheimer's disease and other tauopathies facilitates neurodegeneration, which in turn leads to cognitive impairment. However, recent evidence suggests that tau tangles are not the entity responsible for memory loss, rather it is an intermediate tau species that disrupts neuronal function. Thus, efforts to discover therapeutics for tauopathies emphasize soluble tau reductions as well as neuroprotection. Results Here, we found that neuroprotection alone caused by methylene blue (MB), the parent compound of the anti-tau phenothiaziazine drug, Rember™, was insufficient to rescue cognition in a mouse model of the human tauopathy, progressive supranuclear palsy (PSP) and fronto-temporal dementia with parkinsonism linked to chromosome 17 (FTDP17): Only when levels of soluble tau protein were concomitantly reduced by a very high concentration of MB, was cognitive improvement observed. Thus, neurodegeneration can be decoupled from tau accumulation, but phenotypic improvement is only possible when soluble tau levels are also reduced. Conclusions Neuroprotection alone is not sufficient to rescue tau-induced memory loss in a transgenic mouse model. Development of neuroprotective agents is an area of intense investigation in the tauopathy drug discovery field. This may ultimately be an unsuccessful approach if soluble toxic tau intermediates are not also reduced. Thus, MB and related compounds, despite their pleiotropic nature, may be the proverbial "magic bullet" because they not only are neuroprotective, but are also able to facilitate soluble tau clearance. Moreover, this shows that neuroprotection is possible without reducing tau levels. This indicates that there is a definitive molecular link between tau and cell death cascades that can be disrupted.http://deepblue.lib.umich.edu/bitstream/2027.42/78314/1/1750-1326-5-45.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78314/2/1750-1326-5-45.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78314/3/1750-1326-5-45-S1.PDFPeer Reviewe

Stellar Coronal and Wind Models: Impact on Exoplanets

Surface magnetism is believed to be the main driver of coronal heating and stellar wind acceleration. Coronae are believed to be formed by plasma confined in closed magnetic coronal loops of the stars, with winds mainly originating in open magnetic field line regions. In this Chapter, we review some basic properties of stellar coronae and winds and present some existing models. In the last part of this Chapter, we discuss the effects of coronal winds on exoplanets.Comment: Chapter published in the "Handbook of Exoplanets", Editors in Chief: Juan Antonio Belmonte and Hans Deeg, Section Editor: Nuccio Lanza. Springer Reference Work

Mechanical Properties of Soot Particles: The Impact of Crosslinked Polycyclic Aromatic Hydrocarbons

In this paper, we estimate the degree of crosslinking within soot particles making use of reactive molecular dynamics simulations of mechanical properties of crosslinked polycyclic aromatic hydrocarbons (PAH). Representative systems of PAH (pyrene, coronene, ovalene and circumpyrene) with a density similar to soot and with varying degrees of crosslinking were built. Uniaxial tensile test simulations were carried out on the systems and the yield stress of each sample was calculated. The hardness was estimated from the yield stress using an empirical conversion constant and the obtained values were compared with nanoindentation experiments of soot particles. The results show that mature ethylene and diesel soot particles are expected to present a degree of crosslinking between 2.1–3.0 and 3.0–3.5, respectively, to have a value comparable to the hardness found experimentally. Finally, an MD simulation of nanoindentation of a particle of crosslinked coronene molecules provided an alternative means to compute the empirical constant used to convert the yield stress in hardness. These results reveal the importance of crosslinking reactions during soot maturation that give rise to a structure in which the majority of aromatics are aliphatically linked in a 3D network

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Evolution of the soot particle size distribution along the centreline of an n-heptane/toluene co-flow diffusion flame

A newly developed experimental set-up for studying liquid hydrocarbon combustion in the well-established Yale burner was used to investigate the correlation between fuel composition and its sooting propensity. Soot particle size distributions (PSDs) and flame temperatures along the centreline of an n-heptane/toluene co-flow diffusion flame are reported. The results are compared to soot temperature and volume fraction profiles obtained using colour ratio pyrometry. The addition of toluene (0, 5, 10, and 15 mol%) to heptane moved soot inception to lower heights above the burner (HAB). The earlier inception extended the soot growth zone in the toluene-laden flames, leading to larger soot primary and agglomerate particles. Toluene addition had little influence on the maximum soot number density, indicating that the observed increase in soot volume fraction can mainly be attributed to the increase in particle size. The reported PSDs inside a vapour-fed diffusion flame are the first of their kind and provide a comprehensive dataset for future studies of combustion chemistry and soot particle models

Experimental and numerical study of the evolution of soot primary particles in a diffusion flame

© 2018 The Combustion Institute. The evolution of primary soot particles is studied experimentally and numerically along the centreline of a co-flow laminar diffusion flame. Soot samples from a flame fueled with C2H4are taken thermophoretically at different heights above the burner (HAB), their size and nano-structure are analysed through TEM. The experimental results suggest that after inception, the nascent soot particles coagulate and coalesce to form larger primary particles (~5 to 15 nm). As these primary particles travel along the centreline, they grow mainly due coagulation and condensation and a layer of amorphous hydrocarbons (revealed by HRTEM) forms on their surface. This amorphous layer appears to promote the aggregation of primary particles to form fractal structures. Fast carbonisation of the amorphous layer leads to a graphitic-like shell around the particles. Further graphitization compacts the primary particles, resulting in a decrease of their size. Towards the flame tip the primary particles decrease in size due to rapid oxidation. A detailed population balance model is used to investigate the mechanisms that are important for prediction of primary particle size distributions. Suggestions are made regarding future model development efforts. Simulation results indicate that the primary particle size distributions are very sensitive to the parameterization of the coalescence and particle rounding processes. In contrast, the average primary particle size is less sensitive to these parameters. This demonstrates that achieving good predictions for the average primary particle size does not necessarily mean that the distribution has been accurately predicted

Pathogen-induced hatching and population-specific life-history response to water-borne cues in brown trout (Salmo trutta)

Hatching is an important niche shift, and embryos in a wide range of taxa can either accelerate or delay this life-history switch in order to avoid stage-specific risks. Such behavior can occur in response to stress itself and to chemical cues that allow anticipation of stress. We studied the genetic organization of this phenotypic plasticity and tested whether there are differences among populations and across environments in order to learn more about the evolutionary potential of stress-induced hatching. As a study species, we chose the brown trout (Salmo trutta; Salmonidae). Gametes were collected from five natural populations (within one river network) and used for full-factorial in vitro fertilizations. The resulting embryos were either directly infected with Pseudomonas fluorescens or were exposed to waterborne cues from P. fluorescens-infected conspecifics. We found that direct inoculation with P. fluorescens increased embryonic mortality and induced hatching in all host populations. Exposure to waterborne cues revealed population-specific responses. We found significant additive genetic variation for hatching time, and genetic variation in trait plasticity. In conclusion, hatching is induced in response to infection and can be affected by waterborne cues of infection, but populations and families differ in their reaction to the latter

KoVariome: Korean National Standard Reference Variome database of whole genomes with comprehensive SNV, indel, CNV, and SV analyses

High-coverage whole-genome sequencing data of a single ethnicity can provide a useful catalogue of population-specific genetic variations, and provides a critical resource that can be used to more accurately identify pathogenic genetic variants. We report a comprehensive analysis of the Korean population, and present the Korean National Standard Reference Variome (KoVariome). As a part of the Korean Personal Genome Project (KPGP), we constructed the KoVariome database using 5.5 terabases of whole genome sequence data from 50 healthy Korean individuals in order to characterize the benign ethnicity-relevant genetic variation present in the Korean population. In total, KoVariome includes 12.7M single-nucleotide variants (SNVs), 1.7M short insertions and deletions (indels), 4K structural variations (SVs), and 3.6K copy number variations (CNVs). Among them, 2.4M (19%) SNVs and 0.4M (24%) indels were identified as novel. We also discovered selective enrichment of 3.8M SNVs and 0.5M indels in Korean individuals, which were used to filter out 1,271 coding-SNVs not originally removed from the 1,000 Genomes Project when prioritizing disease-causing variants. KoVariome health records were used to identify novel disease-causing variants in the Korean population, demonstrating the value of high-quality ethnic variation databases for the accurate interpretation of individual genomes and the precise characterization of genetic variation

Essential versus accessory aspects of cell death: recommendations of the NCCD 2015

Cells exposed to extreme physicochemical or mechanical stimuli die in an uncontrollable manner, as a result of their immediate structural breakdown. Such an unavoidable variant of cellular demise is generally referred to as ‘accidental cell death’ (ACD). In most settings, however, cell death is initiated by a genetically encoded apparatus, correlating with the fact that its course can be altered by pharmacologic or genetic interventions. ‘Regulated cell death’ (RCD) can occur as part of physiologic programs or can be activated once adaptive responses to perturbations of the extracellular or intracellular microenvironment fail. The biochemical phenomena that accompany RCD may be harnessed to classify it into a few subtypes, which often (but not always) exhibit stereotyped morphologic features. Nonetheless, efficiently inhibiting the processes that are commonly thought to cause RCD, such as the activation of executioner caspases in the course of apoptosis, does not exert true cytoprotective effects in the mammalian system, but simply alters the kinetics of cellular demise as it shifts its morphologic and biochemical correlates. Conversely, bona fide cytoprotection can be achieved by inhibiting the transduction of lethal signals in the early phases of the process, when adaptive responses are still operational. Thus, the mechanisms that truly execute RCD may be less understood, less inhibitable and perhaps more homogeneous than previously thought. Here, the Nomenclature Committee on Cell Death formulates a set of recommendations to help scientists and researchers to discriminate between essential and accessory aspects of cell death

Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures.

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10(-5)). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk.ABCFS: The Australian Breast Cancer Family Registry (ABCFR; 1992-1995) was supported by the Australian NHMRC, the New South Wales Cancer Council, and the Victorian Health Promotion Foundation (Australia), and by grant UM1CA164920 from the USA National Cancer Institute. The Genetic Epidemiology Laboratory at the University of Melbourne has also received generous support from Mr B. Hovey and Dr and Mrs R.W. Brown to whom we are most grateful. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Breast Cancer Susceptibility Variants and Mammographic Density 5 Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. BBCC: This study was funded in part by the ELAN-Program of the University Hospital Erlangen; Katharina Heusinger was funded by the ELAN program of the University Hospital Erlangen. BBCC was supported in part by the ELAN program of the Medical Faculty, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg. EPIC-Norfolk: This study was funded by research programme grant funding from Cancer Research UK and the Medical Research Council with additional support from the Stroke Association, British Heart Foundation, Department of Health, Research into Ageing and Academy of Medical Sciences. MCBCS: This study was supported by Public Health Service Grants P50 CA 116201, R01 CA 128931, R01 CA 128931-S01, R01 CA 122340, CCSG P30 CA15083, from the National Cancer Institute, National Institutes of Health, and Department of Health and Human Services. MCCS: Melissa C. Southey is a National Health and Medical Research Council Senior Research Fellow and a Victorian Breast Cancer Research Consortium Group Leader. The study was supported by the Cancer Council of Victoria and by the Victorian Breast Cancer Research Consortium. MEC: National Cancer Institute: R37CA054281, R01CA063464, R01CA085265, R25CA090956, R01CA132839. MMHS: This work was supported by grants from the National Cancer Institute, National Institutes of Health, and Department of Health and Human Services. (R01 CA128931, R01 CA 128931-S01, R01 CA97396, P50 CA116201, and Cancer Center Support Grant P30 CA15083). Breast Cancer Susceptibility Variants and Mammographic Density 6 NBCS: This study has been supported with grants from Norwegian Research Council (#183621/S10 and #175240/S10), The Norwegian Cancer Society (PK80108002, PK60287003), and The Radium Hospital Foundation as well as S-02036 from South Eastern Norway Regional Health Authority. NHS: This study was supported by Public Health Service Grants CA131332, CA087969, CA089393, CA049449, CA98233, CA128931, CA 116201, CA 122340 from the National Cancer Institute, National Institutes of Health, Department of Health and Human Services. OOA study was supported by CA122822 and X01 HG005954 from the NIH; Breast Cancer Research Fund; Elizabeth C. Crosby Research Award, Gladys E. Davis Endowed Fund, and the Office of the Vice President for Research at the University of Michigan. Genotyping services for the OOA study were provided by the Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096. OFBCR: This work was supported by grant UM1 CA164920 from the USA National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. SASBAC: The SASBAC study was supported by Märit and Hans Rausing’s Initiative against Breast Cancer, National Institutes of Health, Susan Komen Foundation and Agency for Science, Technology and Research of Singapore (A*STAR). Breast Cancer Susceptibility Variants and Mammographic Density 7 SIBS: SIBS was supported by program grant C1287/A10118 and project grants from Cancer Research UK (grant numbers C1287/8459). COGS grant: Collaborative Oncological Gene-environment Study (COGS) that enabled the genotyping for this study. Funding for the BCAC component is provided by grants from the EU FP7 programme (COGS) and from Cancer Research UK. Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692), the National Institutes of Health (CA128978) and Post- Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAMEON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund.This is the author accepted manuscript. The final version is available via American Association for Cancer Research at http://cancerres.aacrjournals.org/content/early/2015/04/10/0008-5472.CAN-14-2012.abstract