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      Relationships between UBE3A and SNORD116 expression and features of autism in chromosome 15 imprinting disorders

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          Abstract

          Chromosome 15 (C15) imprinting disorders including Prader–Willi (PWS), Angelman (AS) and chromosome 15 duplication (Dup15q) syndromes are severe neurodevelopmental disorders caused by abnormal expression of genes from the 15q11–q13 region, associated with abnormal DNA methylation and/or copy number changes. This study compared changes in mRNA levels of UBE3A and SNORD116 located within the 15q11–q13 region between these disorders and their subtypes and related these to the clinical phenotypes. The study cohort included 58 participants affected with a C15 imprinting disorder (PWS = 27, AS = 21, Dup15q = 10) and 20 typically developing controls. Semi-quantitative analysis of mRNA from peripheral blood mononuclear cells (PBMCs) was performed using reverse transcription droplet digital polymerase chain reaction (PCR) for UBE3A and SNORD116 normalised to a panel of internal control genes determined using the geNorm approach. Participants completed an intellectual/developmental functioning assessment and the Autism Diagnostic Observation Schedule-2nd Edition. The Dup15q group was the only condition with significantly increased UBE3A mRNA levels when compared to the control group ( p < 0.001). Both the AS and Dup15q groups also had significantly elevated SNORD116 mRNA levels compared to controls (AS: p < 0.0001; Dup15q: p = 0.002). Both UBE3A and SNORD116 mRNA levels were positively correlated with all developmental functioning scores in the deletion AS group ( p < 0.001), and autism features ( p < 0.001) in the non-deletion PWS group. The findings suggest presence of novel interactions between expression of UBE3A and SNORD116 in PBMCs and brain specific processes underlying motor and language impairments and autism features in these disorders.

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          The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments.

          Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader's ability to evaluate critically the quality of the results presented or to repeat the experiments. The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement. Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results.
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            Single-cell genomics identifies cell type–specific molecular changes in autism

            Despite the clinical and genetic heterogeneity of autism, bulk gene expression studies show that changes in the neocortex of autism patients converge on common genes and pathways. However, direct assessment of specific cell types in the brain affected by autism has not been feasible until recently. We used single-nucleus RNA sequencing of cortical tissue from patients with autism to identify autism-associated transcriptomic changes in specific cell types. We found that synaptic signaling of upper-layer excitatory neurons and the molecular state of microglia are preferentially affected in autism. Moreover, our results show that dysregulation of specific groups of genes in cortico-cortical projection neurons correlates with clinical severity of autism. These findings suggest that molecular changes in upper-layer cortical circuits are linked to behavioral manifestations of autism.
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              Droplet Digital PCR versus qPCR for gene expression analysis with low abundant targets: from variable nonsense to publication quality data

              Quantitative PCR (qPCR) has become the gold standard technique to measure cDNA and gDNA levels but the resulting data can be highly variable, artifactual and non-reproducible without appropriate verification and validation of both samples and primers. The root cause of poor quality data is typically associated with inadequate dilution of residual protein and chemical contaminants that variably inhibit Taq polymerase and primer annealing. The most susceptible, frustrating and often most interesting samples are those containing low abundant targets with small expression differences of 2-fold or lower. Here, Droplet Digital PCR (ddPCR) and qPCR platforms were directly compared for gene expression analysis using low amounts of purified, synthetic DNA in well characterized samples under identical reaction conditions. We conclude that for sample/target combinations with low levels of nucleic acids (Cq ≥ 29) and/or variable amounts of chemical and protein contaminants, ddPCR technology will produce more precise, reproducible and statistically significant results required for publication quality data. A stepwise methodology is also described to choose between these complimentary technologies to obtain the best results for any experiment.
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                Author and article information

                Contributors
                david.godler@mcri.edu.au
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                29 October 2020
                29 October 2020
                2020
                : 10
                : 362
                Affiliations
                [1 ]GRID grid.416107.5, ISNI 0000 0004 0614 0346, Diagnosis and Development, Murdoch Children’s Research Institute, , Royal Children’s Hospital, ; Melbourne, Victoria Australia
                [2 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Faculty of Medicine, Dentistry and Health Sciences, Department of Paediatrics, , University of Melbourne, ; Parkville, Victoria Australia
                [3 ]GRID grid.1018.8, ISNI 0000 0001 2342 0938, School of Psychology and Public Health, , La Trobe University, ; Melbourne, Victoria Australia
                [4 ]GRID grid.266515.3, ISNI 0000 0001 2106 0692, Department of Psychiatry, Behavioral Sciences and Pediatrics, , University of Kansas Medical Centre, ; Kansas City, Kansas USA
                [5 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, , University of Melbourne, ; Melbourne, Victoria Australia
                [6 ]GRID grid.416107.5, ISNI 0000 0004 0614 0346, Victorian Clinical Genetics Services and Murdoch Children’s Research Institute, , Royal Children’s Hospital, ; Melbourne, Victoria Australia
                [7 ]Genetics of Learning Disability Service, Hunter Genetics, Waratah, New South Wales Australia
                [8 ]GRID grid.484196.6, ISNI 0000 0004 0445 3226, Department of Health, Government of Western Australia, , Genetic Services of Western Australia, ; Perth, Western Australia Australia
                [9 ]GRID grid.416107.5, ISNI 0000 0004 0614 0346, Neurodisability and Rehabilitation, Murdoch Children’s Research Institute, , Royal Children’s Hospital, ; Melbourne, Victoria Australia
                Author information
                http://orcid.org/0000-0001-7455-934X
                http://orcid.org/0000-0001-7191-8511
                http://orcid.org/0000-0002-6405-269X
                Article
                1034
                10.1038/s41398-020-01034-7
                7595031
                33116122
                2da7cadd-efa1-4bd0-b32e-cedf4589a2b6
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 April 2020
                : 20 September 2020
                : 2 October 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000925, Department of Health | National Health and Medical Research Council (NHMRC);
                Award ID: 1049299
                Award ID: 1103389
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100002889, Foundation for Prader-Willi Research (Foundation for Prader-Willi Research, Inc.);
                Award ID: 501393
                Award Recipient :
                Funded by: Medical Research Future Fund MRF1141334 Financial Markets for Children (AU) 2017 – 361
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Clinical Psychology & Psychiatry
                human behaviour,biomarkers,clinical genetics
                Clinical Psychology & Psychiatry
                human behaviour, biomarkers, clinical genetics

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