SRSF2 Mutations Contribute to Myelodysplasia by Mutant-Specific Effects on Exon Recognition.

Mutations affecting spliceosomal proteins are the most common mutations in patients with myelodysplastic syndromes (MDS), but their role in MDS pathogenesis has not been delineated. Here we report that mutations affecting the splicing factor SRSF2 directly impair hematopoietic differentiation in vivo, which is not due to SRSF2 loss of function. By contrast, SRSF2 mutations alter SRSF2's normal sequence-specific RNA binding activity, thereby altering the recognition of specific exonic splicing enhancer motifs to drive recurrent mis-splicing of key hematopoietic regulators. This includes SRSF2 mutation-dependent splicing of EZH2, which triggers nonsense-mediated decay, which, in turn, results in impaired hematopoietic differentiation. These data provide a mechanistic link between a mutant spliceosomal protein, alterations in the splicing of key regulators, and impaired hematopoiesis.E.K. is supported by the Worldwide Cancer Research Fund. A.R. was supported by the NIH/NHLBI (U01 HL099993), NIH/NIDDK (K08 DK082783), the J.P. McCarthy Foundation, and the Storb Foundation. S.H. and O.A.-W. are supported by grants from the Edward P. Evans Foundation. S.H. was supported by Yale Comprehensive Cancer Center institutional funds. R.K.B. was supported by the Hartwell Innovation Fund, Damon Runyon Cancer Research Foundation (DFS 04-12), Ellison Medical Foundation (AG-NS-1030-13), NIH/NIDDK (R56 DK103854), NIH/NCI recruitment support (P30 CA015704), and Fred Hutchinson Cancer Research Center institutional funds. J.O.I. was supported by an NIH/NCI training grant (T32 CA009657) and NIH/NIDDK pilot study (P30 DK056465). C.L. is supported by a career development award grant from the Leukemia and Lymphoma Society and an ATIP-Avenir grant from the French government. O.A.-W. is supported by an NIH K08 clinical investigator award (1K08CA160647-01), a Department of Defense Postdoctoral Fellow Award in Bone Marrow Failure Research (W81XWH-12-1-0041), the Josie Robertson Investigator Program, and a Damon Runyon Clinical Investigator Award with support from the Evans Foundation. F.H.-T.A. acknowledges support from the NCCR RNA and Disease funded by the Swiss National Science Foundation and the SNF Sinergia CRSII3_127454. Y.L. and Y.M. were supported by NIH/NIGMS grant R01 GM102869 and Senior Research Fellowship Grant 101908/Z/13/Z (to Y.M.) from the Wellcome Trust. J.D. acknowledges assistance from Dr. Nezih Cereb, HistoGenetics (Ossining, NY).This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.ccell.2015.04.00

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.ISSN:1434-6044ISSN:1434-605