A functional network module for Smith–Magenis syndrome

Authors

  • S Girirajan,

    1. Department of Human and Molecular Genetics
    2. Department of Pediatrics, Medical College of Virginia Campus, Virginia Commonwealth University, Richmond, VA, USA
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    • *

      Current address: Department of Genome Sciences, University of Washington, Seattle, WA, USA

  • HT Truong,

    1. Department of Human and Molecular Genetics
    2. Department of Pediatrics, Medical College of Virginia Campus, Virginia Commonwealth University, Richmond, VA, USA
    3. School of Biomedical Sciences, Faculty of Science, Charles Sturt University, Wagga Wagga, NSW, Australia
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  • CL Blanchard,

    1. School of Biomedical Sciences, Faculty of Science, Charles Sturt University, Wagga Wagga, NSW, Australia
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  • SH Elsea

    Corresponding author
    1. Department of Human and Molecular Genetics
    2. Department of Pediatrics, Medical College of Virginia Campus, Virginia Commonwealth University, Richmond, VA, USA
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Sarah H Elsea, PhD, FACMG, Department of Pediatrics, 1101 East Marshall Street, 12-018 Sanger Hall, PO BOX 980441, Virginia Commonwealth University, Richmond, VA 23298, USA.
Tel.: +1 804 628 0987;
fax: +1 804 628 1609;
e-mail: selsea@vcu.edu

Abstract

Disorders with overlapping diagnostic features are grouped into a network module. Based on phenotypic similarities or differential diagnoses, it is possible to identify functional pathways leading to individual features. We generated a Smith–Magenis syndrome (SMS)-specific network module utilizing patient clinical data, text mining from the Online Mendelian Inheritance in Man database, and in vitro functional analysis. We tested our module by functional studies based on a hypothesis that RAI1 acts through phenotype-specific pathways involving several downstream genes, which are altered due to RAI1 haploinsufficiency. A preliminary genome-wide gene expression study was performed using microarrays on RAI1 haploinsufficient cells created by RNAi-based ∼50% knockdown of RAI1 in HEK293T cells. The top dysregulated genes were involved in growth signaling and insulin sensitivity, neuronal differentiation, lipid biosynthesis and fat mobilization, circadian activity, behavior, renal, cardiovascular and skeletal development, gene expression, and cell-cycle regulation and recombination, reflecting the spectrum of clinical features observed in SMS. Validation using real-time quantitative reverse transcriptase polymerase chain reaction confirmed the gene expression profile of 75% of the selected genes analyzed in both HEK293T RAI1 knockdown cells and SMS lymphoblastoid cell lines. Overall, these data support a method for identifying genes and pathways responsible for individual clinical features in a complex disorder such as SMS.

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