ABSTRACT
Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.
ABSTRACT
Human genetic variation in PPARGC1B has been associated with adiposity, but the genetic variants that affect PPARGC1B expression have not been experimentally determined. Here, guided by previous observational data, we used clustered regularly interspaced short palindromic repeats/CRISPR associated protein 9 (CRISPR/Cas9) to scarlessly edit the alleles of the candidate causal genetic variant rs10071329 in a human brown adipocyte cell line. Switching the rs10071329 genotype from A/A to G/G enhanced PPARGC1B expression throughout the adipogenic differentiation, identifying rs10071329 as a cis-expression quantitative trait loci (eQTL). The higher PPARGC1B expression in G/G cells coincided with greater accumulation of triglycerides and higher expression of mitochondria-encoded genes, but without significant effects on adipogenic marker expression. Furthermore, G/G cells had improved basal- and norepinephrine-stimulated mitochondrial respiration, possibly relating to enhanced mitochondrial gene expression. The G/G cells also exhibited increased norepinephrine-stimulated glycerol release, indicating improved lipolysis. Altogether, our results showed that rs10071329 is a cis-eQTL, with the G/G genotype conferring enhanced PPARGC1B expression, with consequent improved mitochondrial function and response to norepinephrine in brown adipocytes. This genetic variant, and as yet undetermined eQTLs, at PPARGC1B could prove useful in genotype-based precision medicine for obesity treatment.