Directly to content
  1. Publishing |
  2. Search |
  3. Browse |
  4. Recent items rss |
  5. Open Access |
  6. Jur. Issues |
  7. DeutschClear Cookie - decide language by browser settings

Unraveling Gene Networks of Human Cholesterol Homeostasis and Their Roles in Cardiovascular Disease

Trasta, Anthi

PDF, English
Download (6MB) | Terms of use

Citation of documents: Please do not cite the URL that is displayed in your browser location input, instead use the DOI, URN or the persistent URL below, as we can guarantee their long-time accessibility.


It is well established that elevated plasma cholesterol is a major risk factor for atherosclerosis, the cause of cardiovascular disease (CVD), which is most often manifested as coronary artery disease (CAD) and myocardial infarction (MI). The identification through GWAS of genes associated with lipids and CVD has only to a minor extent explained the genetic architecture of CVD and dyslipidemias. In an effort to identify genes affecting cholesterol regulation, an RNAi-based functional profiling of GWAS-derived loci, which were associated with lipid traits, CAD and/or MI was previously performed in our lab (Blattmann et al. 2013). This study resulted in the identification of 55 genes that had an effect on LDL internalization and/ or cellular cholesterol levels. However, most of the screen hits did not have a strong effect, suggesting that the combinatorial -rather than the individual- function of these genes might regulate cholesterol homeostasis and subsequently CVD. This reasoning is supported by the fact that CVD is a complex disease, which is assumed to arise from the synergistic effect of genes. In the present study, a combinatorial RNAi screen was performed in order to identify interactions between genes identified in the aforementioned study, after juxtaposing them with the results of an Exome Chip of more than 70,000 individuals, genotyped for lipid traits (LDL, HDL, TG, TC) (Peloso et al. 2014). For this purpose, the effect on LDL uptake of all pairwise combinations between 30 candidate genes was tested, and 21 pairs were confirmed as genetic interactors. A gene interaction model network was constructed, based on the results of the screen, connecting known cholesterol regulators, as well as genes without a previously reported lipidregulatory function. Secondary screens were performed to measure the effect of the gene interactions on LDLR mRNA and protein, as well as on SREBF1 and SREBF2 mRNA levels. The results from secondary experiments provided further valuable information for the mechanistic interpretation of the interactions. The one occurring between LDLR, which encodes for the receptor of LDL and HAVCR1, which encodes for a membrane receptor for hepatitis A virus was followed up. Furthermore, hypotheses were generated for the sub-network of LDLR-MLXIPL-HAVCR1 on the mechanism of interaction influencing cholesterol homeostasis. Hypotheses were made also for a few other interesting interactions, which correlated with cellular LDLR mRNA and/or protein levels, as well as with SREBF mRNA levels. For HAVCR1, mutation screening was performed, whereby the overexpression of 18 out of 19 mutations had a significant inhibitory effect on LDL uptake, further supporting a so far undescribed role for HAVCR1 in cholesterol endocytosis. In parallel, in collaboration with Heiko Runz (Merck Research Laboratories), all lead SNPs of the genes tested with co-RNAi were examined for co-occurrence and SNP-SNP interactions in a cohort of more than 4000 individuals (Muntendam et al. 2010). With this analysis, an additive effect was demonstrated for three pairs of SNPs that corresponded to gene interactions identified with the co-RNAi screen (LPL+CELSR2, APOB+HMGCR, LDLR+NCAN). In summary, the study in hand identified combinatorial effects of genes on cholesterol homeostasis, through systematic identification of genetic interactions between GWAS-derived genes. Altogether, this research demonstrates the potential of the scalable strategy employed using quantitative cell-based assays, to uncover the genetic networks underlying common disorders and diseases. Further characterization of these networks would lead to a better understanding of CVD inheritance and provide valuable insight for the generation of novel treatments.

Item Type: Dissertation
Supervisor: Kaksonen, Prof. Dr. Marko
Date of thesis defense: 2 June 2017
Date Deposited: 10 Jul 2017 05:32
Date: 2018
Faculties / Institutes: The Faculty of Bio Sciences > Dean's Office of the Faculty of Bio Sciences
Subjects: 570 Life sciences
Additional Information: Sperrfrist für 12 Monate
About | FAQ | Contact | Imprint |
OA-LogoDINI certificate 2013Logo der Open-Archives-Initiative