Browsing by Subject "Genetic predisposition"

Now showing 1 - 3 of 3
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    Non-responsiveness to cardioprotection by ischaemic preconditioning in Ossabaw minipigs with genetic predisposition to, but without the phenotype of the metabolic syndrome
    (Springer, 2022-11-11) Kleinbongard, Petra; Lieder, Helmut Raphael; Skyschally, Andreas; Alloosh, Mouhamad; Gödecke, Axel; Rahmann, Sven; Sturek, Michael; Heusch, Gerd; Anatomy, Cell Biology and Physiology, School of Medicine
    The translation of successful preclinical and clinical proof-of-concept studies on cardioprotection to the benefit of patients with reperfused acute myocardial infarction has been difficult so far. This difficulty has been attributed to confounders which patients with myocardial infarction typically have but experimental animals usually not have. The metabolic syndrome is a typical confounder. We hypothesised that there may also be a genuine non-responsiveness to cardioprotection and used Ossabaw minipigs which have the genetic predisposition to develop a diet-induced metabolic syndrome, but before they had developed the diseased phenotype. Using a prospective study design, a reperfused acute myocardial infarction was induced in 62 lean Ossabaw minipigs by 60 min coronary occlusion and 180 min reperfusion. Ischaemic preconditioning by 3 cycles of 5 min coronary occlusion and 10 min reperfusion was used as cardioprotective intervention. Ossabaw minipigs were stratified for their single nucleotide polymorphism as homozygous for valine (V/V) or isoleucine (I/I)) in the γ-subunit of adenosine monophosphate-activated protein kinase. Endpoints were infarct size and area of no-reflow. Infarct size (V/V: 54 ± 8, I/I: 54 ± 13% of area at risk, respectively) was not reduced by ischaemic preconditioning (V/V: 55 ± 11, I/I: 46 ± 11%) nor was the area of no-reflow (V/V: 57 ± 18, I/I: 49 ± 21 vs. V/V: 57 ± 21, I/I: 47 ± 21% of infarct size). Bioinformatic comparison of the Ossabaw genome to that of Sus scrofa and Göttingen minipigs identified differences in clusters of genes encoding mitochondrial and inflammatory proteins, including the janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway. The phosphorylation of STAT3 at early reperfusion was not increased by ischaemic preconditioning, different from the established STAT3 activation by cardioprotective interventions in other pig strains. Ossabaw pigs have not only the genetic predisposition to develop a metabolic syndrome but also are not amenable to cardioprotection by ischaemic preconditioning.
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    Oxidized Derivatives of Linoleic Acid in Pediatric Metabolic Syndrome: Is Their Pathogenic Role Modulated by the Genetic Background and the Gut Microbiota?
    (Mary Ann Liebert, 2018-11-30) Tricò, Domenico; Di Sessa, Anna; Caprio, Sonia; Chalasani, Naga; Liu, Wanqing; Liang, Tiebing; Graf, Joerg; Herzog, Raimund I.; Johnson, Casey D.; Umano, Giuseppina Rosaria; Feldstein, Ariel E.; Santoro, Nicola; Medicine, School of Medicine
    We tested whether oxidized linoleic acid metabolites (OXLAM) are associated with pediatric metabolic syndrome (MetS) and a proatherogenic lipoprotein profile in 122 obese adolescents. Furthermore, we examined whether genetic and metagenomic factors can modulate plasma OXLAM concentrations by genotyping the fatty acid desaturase 1/2 (FADS) gene and by characterizing the gut microbiota. Subjects with MetS (n = 50) showed higher concentrations of 9- and 13-oxo-octadecadienoic acid (9- and 13-oxo-ODE) than subjects without MetS (n = 72). Both metabolites were associated with an adverse lipoprotein profile that was characterized by elevated very small-dense low-density lipoprotein (p < 0.005) and large very low-density lipoprotein particles (p = 0.01). Plasma 9- and 13-oxo-ODE were higher in subjects carrying the haplotype AA of the FADS gene cluster (p = 0.030 and p = 0.048, respectively). Furthermore, the reduced gut bacterial load was associated with higher 9-oxo-ODE concentrations (p = 0.035). This is the first study showing that high plasma OXLAM concentrations are associated with MetS and suggesting that the leading factors for high plasma concentrations of OXLAM might be the genetic background and the composition of the gut microbiota. In conclusion, high concentrations of 9- and 13-oxo-ODE, which may be the result of a genetic predisposition and a reduced gut bacterial load, are associated with MetS and with a proatherogenic lipoprotein profile in obese adolescents.
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    Variational Autoencoder-based Model Improves Polygenic Prediction in Blood Cell Traits
    (bioRxiv, 2025-01-18) Li, Xiaoqi; Pang, Minxing; Wen, Jia; Zhou, Laura Y.; Raffield, Laura M.; Zhou, Haibo; Yao, Huaxiu; Chen, Can; Sun, Quan; Li, Yun; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Genetic prediction of complex traits, enabled by large-scale genomic studies, has created new measures to understand individual genetic predisposition. Polygenic Risk Scores (PRS) offer a way to aggregate information across the genome, enabling personalized risk prediction for complex traits and diseases. However, conventional PRS calculation methods that rely on linear models are limited in their ability to capture complex patterns and interaction effects in high-dimensional genomic data. In this study, we seek to improve the predictive power of PRS through applying advanced deep learning techniques. We show that the Variational AutoEncoder-based model for PRS construction (VAE-PRS) outperforms currently state-of-the-art methods for biobank-level data in 14 out of 16 blood cell traits, while being computationally efficient. Through comprehensive experiments, we found that the VAE-PRS model offers the ability to capture interaction effects in high-dimensional data and shows robust performance across different pre-screened variant sets. Furthermore, VAE-PRS is easily interpretable via assessing the contribution of each individual marker to the final prediction score through the SHapley Additive exPlanations (SHAP) method, providing potential new insights in identifying trait-associated genetic variants. In summary, VAE-PRS presents a novel measure to genetic risk prediction by harnessing the power of deep learning methods, which could further facilitate the development of personalized medicine and genetic research.