Folliculin-interacting protein FNIP2 impacts on overweight and obesity through a polymorphism in a conserved 3′ untranslated region

Background: Overweight and obesity are defined by an anomalous or excessive fat accumulation that may compromise health. To find single-nucleotide polymorphisms (SNPs) influencing metabolic phenotypes associated with the obesity state, we analyze multiple anthropometric and clinical parameters in a cohort of 790 healthy volunteers and study potential associations with 48 manually curated SNPs, in metabolic genes functionally associated with the mechanistic target of rapamycin (mTOR) pathway. Results: We identify and validate rs2291007 within a conserved region in the 3'UTR of folliculin-interacting protein FNIP2 that correlates with multiple leanness parameters. The T-to-C variant represents the major allele in Europeans and disrupts an ancestral target sequence of the miRNA miR-181b-5p, thus resulting in increased FNIP2 mRNA levels in cancer cell lines and in peripheral blood from carriers of the C allele. Because the miRNA binding site is conserved across vertebrates, we engineered the T-to-C substitution in the endogenous Fnip2 allele in mice. Primary cells derived from Fnip2 C/C mice show increased mRNA stability, and more importantly, Fnip2 C/C mice replicate the decreased adiposity and increased leanness observed in human volunteers. Finally, expression levels of FNIP2 in both human samples and mice negatively associate with leanness parameters, and moreover, are the most important contributor in a multifactorial model of body mass index prediction. Conclusions: We propose that rs2291007 influences human leanness through an evolutionarily conserved modulation of FNIP2 mRNA levels.

Automated summary

This study identified a SNP, rs2291007, in the conserved region of the 3'UTR of the FNIP2 gene that is associated with multiple leanness parameters. The SNP affects FNIP2 mRNA levels and leads to decreased adiposity and increased leanness in both human volunteers and mice. FNIP2 expression is an important contributor to body mass index prediction.