Identification of a RAI1-associated disease network through integration of exome sequencing, transcriptomics, and 3D genomics

Abstract

BackgroundSmith-Magenis syndrome (SMS) is a developmental disability/multiple congenital anomaly disorder resulting from haploinsufficiency of RAI1. It is characterized by distinctive facial features, brachydactyly, sleep disturbances, and stereotypic behaviors.MethodsWe investigated a cohort of 15 individuals with a clinical suspicion of SMS who showed neither deletion in the SMS critical region nor damaging variants in RAI1 using whole exome sequencing. A combination of network analysis (co-expression and biomedical text mining), transcriptomics, and circularized chromatin conformation capture (4C-seq) was applied to verify whether modified genes are part of the same disease network as known SMS-causing genes.ResultsPotentially deleterious variants were identified in nine of these individuals using whole-exome sequencing. Eight of these changes affect KMT2D, ZEB2, MAP2K2, GLDC, CASK, MECP2, KDM5C, and POGZ, known to be associated with Kabuki syndrome 1, Mowat-Wilson syndrome, cardiofaciocutaneous syndrome, glycine encephalopathy, mental retardation and microcephaly with pontine and cerebellar hypoplasia, X-linked mental retardation 13, X-linked mental retardation Claes-Jensen type, and White-Sutton syndrome, respectively. The ninth individual carries a de novo variant in JAKMIP1, a regulator of neuronal translation that was recently found deleted in a patient with autism spectrum disorder. Analyses of co-expression and biomedical text mining suggest that these pathologies and SMS are part of the same disease network. Further support for this hypothesis was obtained from transcriptome profiling that showed that the expression levels of both Zeb2 and Map2k2 are perturbed in Rai1 –/– mice. As an orthogonal approach to potentially contributory disease gene variants, we used chromatin conformation capture to reveal chromatin contacts between RAI1 and the loci flanking ZEB2 and GLDC, as well as between RAI1 and human orthologs of the genes that show perturbed expression in our Rai1 –/– mouse model.ConclusionsThese holistic studies of RAI1 and its interactions allow insights into SMS and other disorders associated with intellectual disability and behavioral abnormalities. Our findings support a pan-genomic approach to the molecular diagnosis of a distinctive disorder.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0359-z) contains supplementary material, which is available to authorized users.

Exome analysis of Smith–Magenis-like syndrome cohort identifies de novo likely pathogenic variants

“…For patients with molecular findings where photographs were available, images can be found in Supplemental Figure 1. This cohort of SMS-like individuals expands the published cohort of these individuals studied by exome analysis by 40% and identifies several genes that integrate into the proposed RAI1 -associated disease network (Loviglio et al 2016). …”

“…We explored if the genes harboring de novo variants were functionally associated with the network of genes recently identified in a separate SMS-like cohort (Loviglio et al 2016). This previous study identified variants in KMT2D (mapped to MLL2 by STRING), ZEB2, MAP2K2, GLDC, CASK, MECP2, KDM5C , and POGZ .…”

“…We utilized the STRING database website (www.string-db.org) (Szklarczyk et al 2016) to integrate our findings with the recently published RAI -associated disease network (Loviglio et al 2016). The eight genes found in that publication were entered with the six genes identified in this paper.…”

“…De novo mutations were also shown to play important roles in specific nonsyndromic neurocognitive phenotypes including schizophrenia and autism (Iossifov et al 2014; McCarthy et al 2014; Ronemus et al 2014; Slager et al 2003; Xu et al 2012). Recent studies described exome sequencing in patients with SMS-like phenotypes and identified pathogenic variants (Chen et al 2016; Loviglio et al 2016). One specific example of the success of this approach is the identification and subsequent characterization of the White–Sutton syndrome [MIM#616364] (White et al 2016), which was initially described in a patient with an SMS-like phenotype.…”

“…One specific example of the success of this approach is the identification and subsequent characterization of the White–Sutton syndrome [MIM#616364] (White et al 2016), which was initially described in a patient with an SMS-like phenotype. More recently, a cohort of 15 patients with SMS-like features was analyzed to identify potentially deleterious variants in 9 individuals and suggested a common functional network of dysregulation underlying the overlapping phenotypes (Loviglio et al 2016). …”