MOBP and HIP1 in multiple system atrophy: new α‐synuclein partners in glial cytoplasmic inclusions implicated in the disease pathogenesis

Aims: MSA is a fatal neurodegenerative disease. Similar to Parkinson’s disease (PD), MSA is an α‐synucleinopathy, and its pathological hallmark consists of glial cytoplasmic inclusions (GCIs) containing α‐synuclein in oligodendrocytes. We previously identified consistent changes in MOBP and HIP1 DNA methylation status in MSA. We hypothesized that if differential DNA methylation at these loci is mechanistically relevant for MSA, it should have downstream consequences on gene regulation. / Methods: We investigated the relationship between MOBP and HIP1 DNA methylation and mRNA levels in cerebellar white matter from MSA and healthy controls. Additionally, we analysed protein expression using western blotting, immunohistochemistry and proximity ligation assays. / Results: We found decreased MOBP mRNA levels significantly correlated with increased DNA methylation in MSA. For HIP1, we found a distinct relationship between DNA methylation and gene expression levels in MSA compared to healthy controls, suggesting this locus may be subjected to epigenetic remodelling in MSA. Although soluble protein levels for MOBP and HIP1 in cerebellar white matter were not significantly different between MSA cases and controls, we found striking differences between MSA and other neurodegenerative diseases, including PD and Huntington’s disease. We also found that MOBP and HIP1 are mislocalized into the GCIs in MSA, where they appear to interact with α‐synuclein. / Conclusions: This study supports a role for DNA methylation in downregulation of MOBP mRNA in MSA. Most importantly, the identification of MOBP and HIP1 as new constituents of GCIs emphasizes the relevance of these two loci to the pathogenesis of MSA

Common BACE2 Polymorphisms are Associated with Altered Risk for Alzheimer's Disease and CSF Amyloid Biomarkers in APOE ε4 Non-Carriers

It was recently suggested that beta-site amyloid precursor protein (APP)-cleaving enzyme 2 (BACE2) functions as an amyloid beta (Aβ)-degrading enzyme; in addition to its better understood role as an APP secretase. Due to this finding we sought to understand the possible genetic risk contributed by the BACE2 locus to the development of late-onset Alzheimer's disease (AD). In this study, we report that common single nucleotide polymorphism (SNP) variation in BACE2 is associated with altered AD risk in apolipoprotein E gene (APOE) epsilon 4 variant (ε4) non-carriers. In addition, in ε4 non-carriers diagnosed with AD or mild cognitive impairment (MCI), SNPs within the BACE2 locus are associated with cerebrospinal fluid (CSF) levels of Aβ1-42. Further, SNP variants in BACE2 are also associated with BACE2 RNA expression levels suggesting a potential mechanism for the CSF Aβ1-42 findings. Lastly, overexpression of BACE2 in vitro resulted in decreased Aβ1-40 and Aβ1-42 fragments in a cell line model of Aβ production. These findings suggest that genetic variation at the BACE2 locus modifies AD risk for those individuals who don't carry the ε4 variant of APOE. Further, our data indicate that the biological mechanism associated with this altered risk is linked to amyloid generation or clearance possibly through BACE2 expression changes

Immunological network signatures of cancer progression and survival

<p>Abstract</p> <p>Background</p> <p>The immune contribution to cancer progression is complex and difficult to characterize. For example in tumors, immune gene expression is detected from the combination of normal, tumor and immune cells in the tumor microenvironment. Profiling the immune component of tumors may facilitate the characterization of the poorly understood roles immunity plays in cancer progression. However, the current approaches to analyze the immune component of a tumor rely on incomplete identification of immune factors.</p> <p>Methods</p> <p>To facilitate a more comprehensive approach, we created a ranked immunological relevance score for all human genes, developed using a novel strategy that combines text mining and information theory. We used this score to assign an immunological grade to gene expression profiles, and thereby quantify the immunological component of tumors. This immunological relevance score was benchmarked against existing manually curated immune resources as well as high-throughput studies. To further characterize immunological relevance for genes, the relevance score was charted against both the human interactome and cancer information, forming an expanded interactome landscape of tumor immunity. We applied this approach to expression profiles in melanomas, thus identifying and grading their immunological components, followed by identification of their associated protein interactions.</p> <p>Results</p> <p>The power of this strategy was demonstrated by the observation of early activation of the adaptive immune response and the diversity of the immune component during melanoma progression. Furthermore, the genome-wide immunological relevance score classified melanoma patient groups, whose immunological grade correlated with clinical features, such as immune phenotypes and survival.</p> <p>Conclusions</p> <p>The assignment of a ranked immunological relevance score to all human genes extends the content of existing immune gene resources and enriches our understanding of immune involvement in complex biological networks. The application of this approach to tumor immunity represents an automated systems strategy that quantifies the immunological component in complex disease. In so doing, it stratifies patients according to their immune profiles, which may lead to effective computational prognostic and clinical guides.</p

Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3–90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain

Dust dispersion from haul roads in complex terrain: the case of a mineral reclamation site located in Sardinia (Italy)

In recent years there has been significant research effort to investigate the use of plume dispersion models to assess the environmental impact of fugitive dust emissions from surface mining operations. In particular, the results of these studies have identified challenges to the use of traditional Gaussian plume dispersion models to satisfactorily reproduce fugitive dust dispersion and deposition experienced from low elevation release heights within complex topography. This paper presents a discussion of the results of a preliminary series of modelling studies that have employed the UK-ADMS (Atmospheric Dispersion Modelling System) model to investigate the dust dispersion and deposition from to a former mining site currently undergoing remediation. The remediation site is located within a valley in south western Sardinia that may be considered an aerodynamically complex terrain. A series of field measurement surveys were conducted along the length of an unpaved surface haul truck roadway to measure the PM2.5 and PM10 dust fractions within the emitted plumes. To investigate the potential effects that that the surrounding topography may have on the atmospheric dispersion and deposition experienced a series of UK-ADMS dispersion models were solved for a range of meteorological stability conditions typical of the area under investigation. A preliminary analysis of the results of these simulations concludes that there was a strong influence of the surrounding terrain on the dispersion and deposition phenomena predicted

Geographic Population Structure (GPS) of worldwide human populations infers biogeographical origin down to home village

1944T Geographic Population Structure (GPS) of worldwide human populations infers biogeographical origin down to home village. E. Elhaik1, T. Tatarinova2,3, D. Chebotarev3, I.S. Piras4, C.M. Calò4, A.D. Montis5, M. Atzori5, M. Marini5, S. Tofanelli6, P. Francalacci7, L. Pagani8, C. Tyler- Smith8, Y. Xue8, G. Cucca4, T.G Schurr9, J.B. Gaieski9, C. Melendez9, M.G Vilar9, R. Gomez10, R. Fujita11, F.R. Santos12, D. Comas13, O. Balanovsky14,15, P. Zalloua16, H. Soodyall17, R. Pitchappan18, A. GaneshPrasad18, M. Hammer19, L. Matisoo-Smith20, S.R. Wells21. 1) Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205; 2) Glamorgan Computational Biology Research Group, University of Glamorgan, Wales, CF371HR, United Kingdom; 3) Laboratory of Applied Pharmacokinetics and Genomics, Children's Hospital Los Angeles, University of Southern California, 4650 Sunset Blvd, Los Angeles, CA 90027; 4) Department of Sciences of Life and Environment , University of Cagliari, Monserrato, SS 554, 09042, Italy; 5) Research Laboratories, bcs Biotech S.r.l., Viale Monastir 112, 09122 Cagliari, Italy; 6) Department of Biology, University of Pisa, Via Ghini 13, 56126 Pisa, Italy; 7) Department of Science of Nature and Territory, University of Sassari, Località Piandanna, Sassari, Italy; 8) The Wellcome Trust Sanger Institute, CB10 1SA, Hinxton, UK; 9) University of Pennsylvania, Philadelphia, PA; 10) CINVESTAV, Mexico City, Mexico; 11) University of San Martin de Porres, Lima, Peru; 12) Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil; 13) Institut de Biologia Evolutiva (CSIC-UPF), Universitat Pompeu Fabra, Barcelona, Spain; 14) Vavilov Institute for General Genetics, Moscow, Russia; 15) Research Centre for Medical Genetics, Moscow, Russia; 16) The Lebanese American University, Chouran, Beirut, Lebanon; 17) University of the Witwatersrand, Johannesburg, South Africa; 18) Chettinad Academy of Research and Education, Chennai, India; 19) University of Arizona, Tucson, AZ; 20) University of Otago, Dunedin , New Zealand; 21) National Geographic Society, Washington DC, USA. The search for a method that utilizes biological information to predict human's place of origin has occupied scientists for millennia. Modern biogeography methods are accurate to 700 km in Europe but are highly inaccurate elsewhere, particularly in Southeast Asia and Oceania. The accuracy of these methods is bound by the choice of genotyping arrays, the size and quality of the reference dataset, and principal component (PC)-based algorithms. To overcome the first two obstacles, we designed GenoChip, a dedicated genotyping array for genetic anthropology with an unprecedented number of ~12,000 Y-chromosomal and ~3,300 mtDNA SNPs and over 130,000 autosomal and X-chromosomal SNPs carefully chosen to study ancestry without any known health, medical, or phenotypic relevance. We also 615 individuals from 54 worldwide populations collected as part of the Genographic Project and the 1000 Genomes Project. To overcome the last impediment, we developed an admixture-based Geographic Population Structure (GPS) method that infers the biogeography of worldwide individuals down to their village of origin. GPS's accuracy was demonstrated on three data sets: worldwide populations, Southeast Asians and Oceanians, and Sardinians (Italy) using 40,000-130,000 GenoChip markers. GPS correctly placed 80%; of worldwide individuals within their country of origin with an accuracy of 87%; for Asians and Oceanians. Applied to over 200 Sardinians villagers of both sexes, GPS placed a quarter of them within their villages and most of the remaining within 50 km of their villages, allowing us to identify the demographic processes that shaped the Sardinian society. These findings are significantly more accurate than PCA-based approaches. We further demonstrate two GPS applications in tracing the poorly understood biogeographical origin of the Druze and North American (CEU) populations. Our findings demonstrate the potential of the GenoChip array for genetic anthropology. Moreover, the accuracy and power of GPSunderscore the promise of admixture-based methods to biogeography and has important ramifications for genetic ancestry testing, forensic and medical sciences, and genetic privacy