Multiple molecular mechanisms form a positive feedback loop driving amyloid β42 peptide-induced neurotoxicity via activation of the TRPM2 channel in hippocampal neurons

Emerging evidence supports an important role for the ROS-sensitive TRPM2 channel in mediating age-related cognitive impairment in Alzheimer’s disease (AD), particularly neurotoxicity resulting from generation of excessive neurotoxic Aβ peptides. Here we examined the elusive mechanisms by which Aβ₄₂ activates the TRPM2 channel to induce neurotoxicity in mouse hippocampal neurons. Aβ₄₂-induced neurotoxicity was ablated by genetic knockout (TRPM2-KO) and attenuated by inhibition of the TRPM2 channel activity or activation through PARP-1. Aβ₄₂-induced neurotoxicity was also inhibited by treatment with TPEN used as a Zn²⁺-specific chelator. Cell imaging revealed that Aβ₄₂-induced lysosomal dysfunction, cytosolic Zn²⁺ increase, mitochondrial Zn²⁺ accumulation, loss of mitochondrial function, and mitochondrial generation of ROS. These effects were suppressed by TRPM2-KO, inhibition of TRPM2 or PARP-1, or treatment with TPEN. Bafilomycin-induced lysosomal dysfunction also resulted in TRPM2-dependent cytosolic Zn²⁺ increase, mitochondrial Zn²⁺ accumulation, and mitochondrial generation of ROS, supporting that lysosomal dysfunction and accompanying Zn²⁺ release trigger mitochondrial Zn²⁺ accumulation and generation of ROS. Aβ₄₂-induced effects on lysosomal and mitochondrial functions besides neurotoxicity were also suppressed by inhibition of PKC and NOX. Furthermore, Aβ₄₂-induced neurotoxicity was prevented by inhibition of MEK/ERK. Therefore, our study reveals multiple molecular mechanisms, including PKC/NOX-mediated generation of ROS, activation of MEK/ERK and PARP-1, lysosomal dysfunction and Zn²⁺ release, mitochondrial Zn²⁺ accumulation, loss of mitochondrial function, and mitochondrial generation of ROS, are critically engaged in forming a positive feedback loop that drives Aβ₄₂-induced activation of the TRPM2 channel and neurotoxicity in hippocampal neurons. These findings shed novel and mechanistic insights into AD pathogenesis

Static Magnetic Field Exposure Reproduces Cellular Effects of the Parkinson's Disease Drug Candidate ZM241385

This study was inspired by coalescing evidence that magnetic therapy may be a viable treatment option for certain diseases. This premise is based on the ability of moderate strength fields (i.e., 0.1 to 1 Tesla) to alter the biophysical properties of lipid bilayers and in turn modulate cellular signaling pathways. In particular, previous results from our laboratory (Wang et al., BMC Genomics, 10, 356 (2009)) established that moderate strength static magnetic field (SMF) exposure altered cellular endpoints associated with neuronal function and differentiation. Building on this background, the current paper investigated SMF by focusing on the adenosine A(2A) receptor (A(2A)R) in the PC12 rat adrenal pheochromocytoma cell line that displays metabolic features of Parkinson's disease (PD).SMF reproduced several responses elicited by ZM241385, a selective A(2A)R antagonist, in PC12 cells including altered calcium flux, increased ATP levels, reduced cAMP levels, reduced nitric oxide production, reduced p44/42 MAPK phosphorylation, inhibited proliferation, and reduced iron uptake. SMF also counteracted several PD-relevant endpoints exacerbated by A(2A)R agonist CGS21680 in a manner similar to ZM241385; these include reduction of increased expression of A(2A)R, reversal of altered calcium efflux, dampening of increased adenosine production, reduction of enhanced proliferation and associated p44/42 MAPK phosphorylation, and inhibition of neurite outgrowth.When measured against multiple endpoints, SMF elicited qualitatively similar responses as ZM241385, a PD drug candidate. Provided that the in vitro results presented in this paper apply in vivo, SMF holds promise as an intriguing non-invasive approach to treat PD and potentially other neurological disorders

Identification of six new susceptibility loci for invasive epithelial ovarian cancer.

Genome-wide association studies (GWAS) have identified 12 epithelial ovarian cancer (EOC) susceptibility alleles. The pattern of association at these loci is consistent in BRCA1 and BRCA2 mutation carriers who are at high risk of EOC. After imputation to 1000 Genomes Project data, we assessed associations of 11 million genetic variants with EOC risk from 15,437 cases unselected for family history and 30,845 controls and from 15,252 BRCA1 mutation carriers and 8,211 BRCA2 mutation carriers (3,096 with ovarian cancer), and we combined the results in a meta-analysis. This new study design yielded increased statistical power, leading to the discovery of six new EOC susceptibility loci. Variants at 1p36 (nearest gene, WNT4), 4q26 (SYNPO2), 9q34.2 (ABO) and 17q11.2 (ATAD5) were associated with EOC risk, and at 1p34.3 (RSPO1) and 6p22.1 (GPX6) variants were specifically associated with the serous EOC subtype, all with P < 5 × 10(-8). Incorporating these variants into risk assessment tools will improve clinical risk predictions for BRCA1 and BRCA2 mutation carriers.COGS project is funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 ] HEALTH ]F2 ]2009 ]223175). The CIMBA data management and data analysis were supported by Cancer Research.UK grants 12292/A11174 and C1287/A10118. The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME ]ON Post ]GWAS Initiative (U19 ]CA148112). This study made use of data generated by the Wellcome Trust Case Control consortium. Funding for the project was provided by the Wellcome Trust under award 076113. The results published here are in part based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancer Institute and National Human Genome Research Institute (dbGap accession number phs000178.v8.p7). The cBio portal is developed and maintained by the Computational Biology Center at Memorial Sloan ] Kettering Cancer Center. SH is supported by an NHMRC Program Grant to GCT. Details of the funding of individual investigators and studies are provided in the Supplementary Note. This study made use of data generated by the Wellcome Trust Case Control consortium, funding for which was provided by the Wellcome Trust under award 076113. The results published here are, in part, based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancerhttp://dx.doi.org/10.1038/ng.3185This is the Author Accepted Manuscript of 'Identification of six new susceptibility loci for invasive epithelial ovarian cancer' which was published in Nature Genetics 47, 164–171 (2015) © Nature Publishing Group - content may only be used for academic research

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs

Co@NH 2

We present a synthetic strategy for the efficient encapsulation of a deriv. of a well-​defined cobaloxime proton redn. catalyst within a photoresponsive metal-​org. framework (NH2- MIL-​125(Ti)​)​. The resulting hybrid system Co@MOF is demonstrated to be a robust heterogeneous composite material. Furthermore, Co@MOF is an efficient and fully recyclable noble metal-​free catalyst system for light-​driven hydrogen evolution from water under visible light illumination

Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk

Funder: Funding details are provided in the Supplementary MaterialAbstractPolygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally-efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestry; 7,669 women of East Asian ancestry; 1,072 women of African ancestry, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestry. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38(95%CI:1.28–1.48,AUC:0.588) per unit standard deviation, in women of European ancestry; 1.14(95%CI:1.08–1.19,AUC:0.538) in women of East Asian ancestry; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestry; hazard ratios of 1.37(95%CI:1.30–1.44,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.51(95%CI:1.36-1.67,AUC:0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.</jats:p