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Heid, I.

Publications and source records attributed to Heid, I..

2 recordsLinked to original sources

Functional metabolic phenotyping of human pancreatic ductal adenocarcinoma

Pancreatic Ductal Adenocarcinoma (PDAC) lacks targeted treatment options. Although subtypes with transcriptome-based distinct lineage and differentiation features have been identified, deduced clinically actionable targets remain elusive. We here investigate functional metabolic features of the classical and QM (quasi-mesenchymal)/basal-like PDAC subtypes potentially exploitable for non-invasive subtype differentiation and therapeutic intervention. A collection of human PDAC cell lines, primary patient derived cells (PDC), patient derived xenografts (PDX) and patient PDAC samples were transcriptionally stratified into the classical and QM subtype. Functional metabolic analyses including targeted and non-targeted metabolite profiling (matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI)), seahorse metabolic flux assays and metabolic drug targeting were performed. Hyperpolarized 13C-magnetic resonance spectroscopy (HP-MRS) of PDAC xenografts was used for in vivo detection of intra-tumoral [1-13C]pyruvate and [1-13C]lactate metabolism. We identified glycolysis and lipid metabolism/fatty acid oxidation as transcriptionally preserved metabolic pathways in QM and classical PDAC subtype respectively. However, these metabolic cues were not unambiguously functionally linked to one subtype. Striking functional metabolic heterogeneity was observed especially in primary patient derived cells with only individual samples representing high dependence on glycolysis or mitochondrial oxidation. Of note, QM cells actively use the glycolytic product lactate as oxidative mitochondrial fuel. Using HP-MRS, we were able to non-invasively differentiate glycolytic tumor xenografts with high intratumoral [1-13C]pyruvate to [1-13C]lactate conversion in vivo. Although PDAC transcriptomes indicate molecular subtype-associated distinct metabolic pathways, we found substantial functional metabolic heterogeneity independent of the molecular subtype. Non-invasive identification of highly glycolytic tumors by [1-13C]pyruvate/lactate HP-MRS support individualized metabolic targeting approaches.

cancer biology↗

Maximum likelihood method quantifies the overall contribution of gene-environment interaction to complex traits: an application to obesity traits

As genome-wide association studies (GWAS) increased in size, numerous gene-environment interactions (GxE) have been discovered, many of which however explore only one environment at a time and may suffer from statistical artefacts leading to biased interaction estimates. Here we propose a maximum likelihood method to estimate the contribution of GxE to complex traits taking into account all interacting environmental variables at the same time, without the need to measure any. This is possible because GxE induces fluctuations in the conditional trait variance, the extent of which depends on the strength of GxE. The approach can be applied to continuous outcomes and for single SNPs or genetic risk scores (GRS). Extensive simulations demonstrated that our method yields unbiased interaction estimates and excellent confidence interval coverage. We also offer a strategy to distinguish specific GxE from general heteroscedasticity (scale effects). Applying our method to 32 complex traits in the UK Biobank reveals that for body mass index (BMI) the GRSxE explains an additional 1.9% variance on top of the 5.2% GRS contribution. However, this interaction is not specific to the GRS and holds for any variable similarly correlated with BMI. On the contrary, the GRSxE interaction effect for leg impedance [Formula] is significantly (P < 10-56) larger than it would be expected for a similarly correlated variable [Formula]. We showed that our method could robustly detect the global contribution of GxE to complex traits, which turned out to be substantial for certain obesity measures.

epidemiology↗