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  • Structural basis of membrane disruption and cellular toxicity by α-synuclein oligomers.

    Posted 2018-01-16 11:00:12 by: Mahammad A. Tafida

    Related Articles Structural basis of membrane disruption and cellular toxicity by α-synuclein oligomers. Science. 2017 12 15;358(6369):1440-1443 Authors: Fusco G, Chen SW, Williamson PTF, Cascella R, Perni M, Jarvis JA, Cecchi C, Vendruscolo M, Chiti F, Cremades N, Ying L, Dobson CM, De Simone A Abstract Oligomeric species populated during the aggregation process of α-synuclein have been linked to neuronal impairment in Parkinson's disease and related neurodegenerative disorders. By using solution and solid-state nuclear magnetic resonance techniques in conjunction with other structural methods, we identified the fundamental characteristics that enable toxic α-synuclein oligomers to perturb biological membranes and disrupt cellular function; these include a highly lipophilic element that promotes strong membrane interactions and a structured region that inserts into lipid bilayers and disrupts their integrity. In support of these conclusions, mutations that target the region that promotes strong membrane interactions by α-synuclein oligomers suppressed their toxicity in neuroblastoma cells and primary cortical neurons. PMID: 29242346 [PubMed - indexed for ...

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  • Guidelines for the use of flow cytometry and cell sorting in immunological studies.

    Posted 2018-01-10 11:00:14 by: Mahammad A. Tafida

    Related Articles Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur J Immunol. 2017 10;47(10):1584-1797 Authors: Cossarizza A, Chang HD, Radbruch A, Akdis M, Andrä I, Annunziato F, Bacher P, Barnaba V, Battistini L, Bauer WM, Baumgart S, Becher B, Beisker W, Berek C, Blanco A, Borsellino G, Boulais PE, Brinkman RR, Büscher M, Busch DH, Bushnell TP, Cao X, Cavani A, Chattopadhyay PK, Cheng Q, Chow S, Clerici M, Cooke A, Cosma A, Cosmi L, Cumano A, Dang VD, Davies D, De Biasi S, Del Zotto G, Della Bella S, Dellabona P, Deniz G, Dessing M, Diefenbach A, Di Santo J, Dieli F, Dolf A, Donnenberg VS, Dörner T, Ehrhardt GRA, Endl E, Engel P, Engelhardt B, Esser C, Everts B, Dreher A, Falk CS, Fehniger TA, Filby A, Fillatreau S, Follo M, Förster I, Foster J, Foulds GA, Frenette PS, Galbraith D, Garbi N, García-Godoy MD, Geginat J, Ghoreschi K, Gibellini L, Goettlinger C, Goodyear CS, Gori A, Grogan J, Gross M, Grützkau A, Grummitt D, Hahn J, Hammer Q, Hauser AE, Haviland DL, Hedley D, Herrera G, Herrmann M, Hiepe F, Holland T, Hombrink P, Houston JP, Hoyer BF, Huang B, Hunter CA, Iannone A, Jäck HM, Jávega B, Jonjic S, Juelke K, Jung S, Kaiser T, Kalina T, Keller B, Khan S, Kienhöfer D, Kroneis T, Kunkel D, Kurts C, Kvistborg P, Lannigan J, Lantz O, Larbi A, LeibundGut-Landmann S, Leipold MD, Levings MK, Litwin V, Liu Y, Lohoff M, Lombardi G, Lopez L, Lovett-Racke A, Lubberts E, Ludewig B, Lugli E, Maecker HT, Martrus G, Matarese G, Maueröder C, McGrath M, McInnes I, Mei HE, Melchers F, Melzer S, Mielenz D, Mills K, Mirrer D, Mjösberg J, Moore J, Moran B, Moretta A, Moretta L, Mosmann TR, Müller S, Müller W, Münz C, Multhoff G, Munoz LE, Murphy KM, Nakayama T, Nasi M, Neudörfl C, Nolan J, Nourshargh S, O'Connor JE, Ouyang W, Oxenius A, Palankar R, Panse I, Peterson P, Peth C, Petriz J, Philips D, Pickl W, Piconese S, Pinti M, Pockley AG, Podolska MJ, Pucillo C, Quataert SA, Radstake ...

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  • Deep learning of the splicing (epi)genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision.

    Posted 2018-01-10 11:00:14 by: Mahammad A. Tafida

    Related Articles Deep learning of the splicing (epi)genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision. Nucleic Acids Res. 2017 Dec 01;45(21):12100-12112 Authors: Xu Y, Wang Y, Luo J, Zhao W, Zhou X Abstract Alternative splicing (AS) is a genetically and epigenetically regulated pre-mRNA processing to increase transcriptome and proteome diversity. Comprehensively decoding these regulatory mechanisms holds promise in getting deeper insights into a variety of biological contexts involving in AS, such as development and diseases. We assembled splicing (epi)genetic code, DeepCode, for human embryonic stem cell (hESC) differentiation by integrating heterogeneous features of genomic sequences, 16 histone modifications with a multi-label deep neural network. With the advantages of epigenetic features, DeepCode significantly improves the performance in predicting the splicing patterns and their changes during hESC differentiation. Meanwhile, DeepCode reveals the superiority of epigenomic features and their dominant roles in decoding AS patterns, highlighting the necessity of including the epigenetic properties when assembling a more comprehensive splicing code. Moreover, DeepCode allows the robust predictions across cell lineages and datasets. Especially, we identified a putative H3K36me3-regulated AS event leading to a nonsense-mediated mRNA decay of BARD1. Reduced BARD1 expression results in the attenuation of ATM/ATR signalling activities and further the hESC differentiation. These results suggest a novel candidate mechanism linking histone modifications to hESC fate decision. In addition, when trained in different contexts, DeepCode can be expanded to a variety of biological and biomedical fields. PMID: 29036709 [PubMed - indexed for ...

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