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      Longitudinal EEG model detects antisense oligonucleotide treatment effect and increased UBE3A in Angelman syndrome

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          Abstract

          Angelman syndrome is a neurodevelopmental disorder caused by deficiency of the maternally inherited UBE3A gene in neurons. Antisense oligonucleotide therapies are under development to reinstate UBE3A protein production. Non-invasive biomarkers to detect target engagement and treatment response are needed to support clinical trials. Delta power measured in the scalp EEG is a reliable biomarker for Angelman syndrome but varies widely across individuals and throughout development, making detection of a treatment effect using single measurements challenging.

          We utilized a longitudinal dataset of 204 EEG recordings from 56 subjects with Angelman syndrome to develop a natural history model of delta (2–4 Hz) power, with predictors of age, elapsed time, and relative delta power at an initial recording. Using this model, we computed the sample and effect sizes needed to detect a treatment effect in a human clinical trial with 80% power. We applied the same model structure to a mouse model of Angelman syndrome ( n = 41) to detect antisense oligonucleotide-mediated treatment effects on absolute delta activity and Ube3a expression. In humans, delta power at a second time point can be reliably predicted using the natural history model. In mice, a treatment effect can be detected after antisense oligonucleotide treatment targeting the Ube3a-antisense transcript through at least 8 weeks post-treatment ( P < 1e-15). Deviations in delta power from the expected natural history correlated with Ube3a expression in the mouse model ( P < 0.001). Deviations in delta power from a human natural history model in Angelman syndrome can detect antisense oligonucleotide-mediated improvement in Ube3a expression in Angelman syndrome mice and may be relevant for human clinical trials.

          Abstract

          Deviations in delta power from a human natural history model in Angelman syndrome can detect antisense oligonucleotide-mediated improvement in Ube3a expression in Angelman syndrome mice and may be relevant for human clinical trials.

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          Most cited references21

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          The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology.

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            Chronux: a platform for analyzing neural signals.

            Chronux is an open-source software package developed for the analysis of neural data. The current version of Chronux includes software for signal processing of neural time-series data including several specialized mini-packages for spike-sorting, local regression, audio segmentation, and other data-analysis tasks typically encountered by a neuroscientist. Chronux is freely available along with user tutorials, sample data, and extensive documentation from http://chronux.org/. Copyright 2010 Elsevier B.V. All rights reserved.
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              Angelman syndrome 2005: updated consensus for diagnostic criteria.

              In 1995, a consensus statement was published for the purpose of summarizing the salient clinical features of Angelman syndrome (AS) to assist the clinician in making a timely and accurate diagnosis. Considering the scientific advances made in the last 10 years, it is necessary now to review the validity of the original consensus criteria. As in the original consensus project, the methodology used for this review was to convene a group of scientists and clinicians, with experience in AS, to develop a concise consensus statement, supported by scientific publications where appropriate. It is hoped that this revised consensus document will facilitate further clinical study of individuals with proven AS, and assist in the evaluation of those who appear to have clinical features of AS but have normal laboratory diagnostic testing.
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                Author and article information

                Journal
                Brain Commun
                Brain Commun
                braincomms
                Brain Communications
                Oxford University Press
                2632-1297
                2022
                26 April 2022
                26 April 2022
                : 4
                : 3
                : fcac106
                Affiliations
                [1 ] Department of Mathematics and Statistics, Boston University , 02215 Boston, MA, USA
                [2 ] Department of Neurology, Massachusetts General Hospital , 02114 Boston, MA, USA
                [3 ] Harvard Medical School , 02115 Boston, MA, USA
                [4 ] Biogen Inc , 02142 Cambridge, MA, USA
                [5 ] School of Medicine, University of California , 92092 San Diego, CA, USA
                [6 ] Department of Pediatrics, University of California , 92093 San Diego, CA, USA
                [7 ] Ionis Pharmaceuticals , 92010 Carlsbad, CA, USA
                Author notes
                Correspondence to: Catherine J Chu, MD 175 Cambridge Street, Suite 340 Boston, MA, USA 02114 E-mail: cjchu@ 123456mgh.harvard.edu
                [*]

                Elizabeth R. Spencer and Wen Shi contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-6467-3309
                https://orcid.org/0000-0001-7670-9313
                Article
                fcac106
                10.1093/braincomms/fcac106
                9123847
                1347d7dd-71bc-4a03-952b-984701c9bb45
                © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 September 2021
                : 01 February 2022
                : 25 April 2022
                Page count
                Pages: 14
                Funding
                Funded by: National Science Foundation, doi 10.13039/100000001;
                Categories
                Original Article
                AcademicSubjects/MED00310
                AcademicSubjects/SCI01870

                angelman syndrome,antisense oligonucleotide,biomarkers,eeg,ube3a

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