Aberrant expression of agouti signaling protein (ASIP) as a cause of monogenic severe childhood obesity.
Elena KempfKathrin LandgrafRobert SteinMartha HanschkowAnja HilbertRami Abou JamraPaula BoczkiGunda HerberthAndreas KühnapfelYu-Hua TsengClaudia StäubertTorsten SchönebergPeter KühnenNigel William RaynerEleftheria ZegginiWieland KiessMatthias BlüherAntje KörnerPublished in: Nature metabolism (2022)
Here we report a heterozygous tandem duplication at the ASIP (agouti signaling protein) gene locus causing ubiquitous, ectopic ASIP expression in a female patient with extreme childhood obesity. The mutation places ASIP under control of the ubiquitously active itchy E3 ubiquitin protein ligase promoter, driving the generation of ASIP in patient-derived native and induced pluripotent stem cells for all germ layers and hypothalamic-like neurons. The patient's phenotype of early-onset obesity, overgrowth, red hair and hyperinsulinemia is concordant with that of mutant mice ubiquitously expressing the homolog nonagouti. ASIP represses melanocyte-stimulating hormone-mediated activation as a melanocortin receptor antagonist, which might affect eating behavior, energy expenditure, adipocyte differentiation and pigmentation, as observed in the index patient. As the type of mutation escapes standard genetic screening algorithms, we rescreened the Leipzig Childhood Obesity cohort of 1,745 patients and identified four additional patients with the identical mutation, ectopic ASIP expression and a similar phenotype. Taken together, our data indicate that ubiquitous ectopic ASIP expression is likely a monogenic cause of human obesity.
Keyphrases
- early onset
- poor prognosis
- binding protein
- induced pluripotent stem cells
- insulin resistance
- weight loss
- metabolic syndrome
- case report
- type diabetes
- high fat diet induced
- late onset
- adipose tissue
- gene expression
- ejection fraction
- spinal cord
- machine learning
- dna methylation
- long non coding rna
- copy number
- skeletal muscle
- small molecule
- artificial intelligence
- spinal cord injury
- prognostic factors
- deep learning
- body mass index