medRxivGenetic & Genomic MedicinePreprint
Biallelic variants in SREK1 downregulating SNORD115 and SNORD116 cause a novel Prader-Willi-like syndrome
Sadia SaeediD, Anna‐Maria SiegertiD, Y. C. Loraine TungiD, Roohia KhanamiD, Qasim M. JanjuaiD, Jaida ManzooriD, Mehdi DerhourhiiD, Bénédicte Toussaint, Brian LamiD, Sherine Awad Mahmoud, Emmanuel Vaillant, Emmanuel Buse Falay, Souhila Amanzougarene, Hina Ayesha, Waqas Imran KhaniD, Nosheen Ramazan, Vladimı́r SaudekiD, Stephen O’RahillyiD, Anthony Peter GoldstoneiD, Muhammad Arslan, Amélie BonnefondiD, Philippe FrogueliD, Giles S.H. YeoiD
Abstract
Up to 10% of patients with severe early-onset obesity carry pathogenic variants in known obesity-related genes, mostly affecting the leptin-melanocortin pathway. Studying children with severe obesity from consanguineous populations provides a unique opportunity to uncover novel molecular mechanisms. Using whole-exome sequencing, followed by a rigorous analytical and filtration strategy, we identified three different homozygous missense variants in SREK1 (encoding Splicing Regulatory glutamic acid and lysine rich protein) in children with severe obesity, from three unrelated consanguineous pedigrees. The wild type SREK1 gene of human induced pluripotent stem cell (iPSC)-derived hypothalamic neurons was individually replaced by each of the three variants and the impact of these changes on global gene expression was studied. Neurons expressing the two variants in the SREK1 RNA recognition domain p.P95L and p.T194M, but not the C-terminally located p.E601K, had markedly reduced expression of the small nucleolar RNA clusters SNORD115 and SNORD116, deficiency of which has been implicated in Prader-Willi syndrome (PWS). In addition to hyperphagic obesity the carriers of these two variants had other features of PWS, such as neonatal hypotonia. In conclusion, homozygous variants in SREK1 result in a subtype of severe early onset obesity sharing features with PWS.
- organism: human
- assay: whole-exome sequencing
- research focus: differential expression
- approach: statistical model
- Citations
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