Molecular Resolution of Diabesity in Children

• Applicants: Professorin Dr. Antje Körner; Professorin Dr. Eleftheria Zeggini
• Subject Area: Endocrinology, Diabetology, Metabolism ; General Genetics and Functional Genome Biology ; Pediatric and Adolescent Medicine
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 458892987

Childhood obesity develops early in life and is already accompanied by emerging comorbidities. These clinical observations are paralleled biologically by excess accumulation and dysfunction of adipose tissue. This adipose tissue dysfunction contributes to development of obesity and ensuing (pre)diabetes ("diabesity") already in children. Disentangling the molecular networks underlying these associations has the promise not only to identify the molecular and biological mechanisms that are causal in driving diabesity, but further on to develop etiology based strategies for prediction and intervention of obesity and ensuing comorbidities.To achieve this, we need a better understanding of the molecular drivers of disease pathogenesis and progression. With our childhood obesity cohorts, we have a rare opportunity to directly access and study one of the key relevant tissues: adipose tissue in different stages of emerging disease. The overarching aim of our project is to catalyse a step-change in our understanding of the molecular mechanisms underpinning obesity and ensuing diabetes in children. We will deploy an integrated approach to identify molecular signatures from adipose tissue of children and understand the biological processes driving metabolic health and disease, in order to accelerate the discovery of potential targets for intervention. For this, we will apply multi-omics for the first time to relevant tissue derived from children at this scale and depth, in order to identify effector genes and their mechanism of action and assess the link of molecular traits with well-characterized clinical and biological phenotypes. By integrating multiple layers of molecular profile assays (gene expression, methylome, genetic variation) and phenotypes, and coupling those to big data analytics including machine learning approaches, we are able to distinguish between cause and consequence of disease-relevant processes. Identified candidate genes will immediately be further assessed for their functional mechanistic relevance for adipose tissue (dys)function in in vitro and in vivo models as well as clinical relevance for predicting obesity and diabetes in our large childhood cohorts.Hence, this integrated approach linking multiple complementary molecular signatures in adipose tissue with biological and clinical phenotypes of adipose tissue dysfunction and diabesity in children, will help to identify molecular mechanisms in adipose tissue and understand the biological processes driving metabolic health and disease with potential implications for developing tools for precision risk prediction and intervention.

• DFG Programme: Research Grants