Unknown

Dataset Information

0

Independent phenotypic plasticity axes define distinct obesity sub-types.


ABSTRACT: Studies in genetically 'identical' individuals indicate that as much as 50% of complex trait variation cannot be traced to genetics or to the environment. The mechanisms that generate this 'unexplained' phenotypic variation (UPV) remain largely unknown. Here, we identify neuronatin (NNAT) as a conserved factor that buffers against UPV. We find that Nnat deficiency in isogenic mice triggers the emergence of a bi-stable polyphenism, where littermates emerge into adulthood either 'normal' or 'overgrown'. Mechanistically, this is mediated by an insulin-dependent overgrowth that arises from histone deacetylase (HDAC)-dependent β-cell hyperproliferation. A multi-dimensional analysis of monozygotic twin discordance reveals the existence of two patterns of human UPV, one of which (Type B) phenocopies the NNAT-buffered polyphenism identified in mice. Specifically, Type-B monozygotic co-twins exhibit coordinated increases in fat and lean mass across the body; decreased NNAT expression; increased HDAC-responsive gene signatures; and clinical outcomes linked to insulinemia. Critically, the Type-B UPV signature stratifies both childhood and adult cohorts into four metabolic states, including two phenotypically and molecularly distinct types of obesity.

SUBMITTER: Yang CH 

PROVIDER: S-EPMC9499872 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

altmetric image

Publications


Studies in genetically 'identical' individuals indicate that as much as 50% of complex trait variation cannot be traced to genetics or to the environment. The mechanisms that generate this 'unexplained' phenotypic variation (UPV) remain largely unknown. Here, we identify neuronatin (NNAT) as a conserved factor that buffers against UPV. We find that Nnat deficiency in isogenic mice triggers the emergence of a bi-stable polyphenism, where littermates emerge into adulthood either 'normal' or 'overg  ...[more]

Similar Datasets

2022-07-13 | GSE205741 | GEO
2022-07-13 | GSE205740 | GEO
2022-07-13 | GSE205668 | GEO
| PRJNA847415 | ENA
| PRJNA847049 | ENA
| PRJNA847419 | ENA
| S-EPMC7613064 | biostudies-literature
| S-EPMC4940344 | biostudies-literature
| S-EPMC3115524 | biostudies-other
| S-EPMC7785730 | biostudies-literature