Epistatic interaction of apolipoprotein E and lipolysis-stimulated lipoprotein receptor genetic variants is associated with Alzheimer's disease

VEGF-A-related genetic variants protect against Alzheimer's disease

The Apolipoprotein E (APOE) genotype has been shown to be the strongest genetic risk factor for Alzheimer’s disease (AD). Moreover, both the lipolysis-stimulated lipoprotein receptor (LSR) and the vascular endothelial growth factor A (VEGF-A) are involved in the development of AD. The aim of the study was to develop a prediction model for AD including single nucleotide polymorphisms (SNP) of APOE, LSR and VEGF-A-related variants.

The population consisted of 323 individuals (143 AD cases and 180 controls). Genotyping was performed for: the APOE common polymorphism (rs429358 and rs7412), two LSR variants (rs34259399 and rs916147) and 10 VEGF-A-related SNPs (rs6921438, rs7043199, rs6993770, rs2375981, rs34528081, rs4782371, rs2639990, rs10761741, rs114694170, rs1740073), previously identified as genetic determinants of VEGF-A levels in GWAS studies. The prediction model included direct and epistatic interaction effects, age and sex and was developed using the elastic net machine learning methodology.

An optimal model including the direct effect of the APOE e4 allele, age and eight epistatic interactions between APOE and LSR, APOE and VEGF-A-related variants was developed with an accuracy of 72%. Two epistatic interactions (rs7043199*rs6993770 and rs2375981*rs34528081) were the strongest protective factors against AD together with the absence of ε4 APOE allele. Based on pathway analysis, the involved variants and related genes are implicated in neurological diseases.

In conclusion, this study demonstrated links between APOE, LSR and VEGF-A-related variants and the development of AD and proposed a model of nine genetic variants which appears to strongly influence the risk for AD.

LSR Promotes Cell Proliferation and Invasion in Lung Cancer

The lipolysis-stimulated lipoprotein receptor (LSR) displays an important regulatory role in cancer. However, the association between LSR and lung cancer is still elusive. Here, the candidate oncogene LSR on Ch.9q was obtained and assessed by bioinformatics analysis of The Cancer Genome Atlas (TCGA) dataset of lung cancer. We conducted clinical pathology and survival analysis based on the lung cancer database. We assessed the biological effects of LSR in lung cancer cells on cell proliferation. Our data indicated that LSR was upregulated in lung cancer cells. Meanwhile, LSR was identified in this study to be a poor prognostic factor, and its high expression exhibited relations with grades, stages, and nodal metastasis status. Using in vitro analysis, our data revealed that LSR could promote lung cancer progression by regulating cell proliferation, migration, and invasion. In our study, our data demonstrated that LSR was a tumor promoter for lung cancer and was a potential biomarker and target for lung cancer prognosis and treatment.

The Combined Analysis of Pleiotropy and Epistasis (CAPE)

Epistasis, or gene-gene interaction, contributes substantially to trait variation in organisms ranging from yeast to humans, and modeling epistasis directly is critical to understanding the genotype-phenotype map. However, inference of genetic interactions is challenging compared to inference of individual allele effects due to low statistical power. Furthermore, genetic interactions can appear inconsistent across different quantitative traits, presenting a challenge for the interpretation of detected interactions. Here we present a method called the Combined Analysis of Pleiotropy and Epistasis (CAPE) that combines information across multiple quantitative traits to infer directed epistatic interactions. By combining information across multiple traits, CAPE not only increases power to detect genetic interactions but also interprets these interactions across traits to identify a single interaction that is consistent across all observed data. This method generates informative, interpretable interaction networks that explain how variants interact with each other to influence groups of related traits. This method could potentially be used to link genetic variants to gene expression, physiological endophenotypes, and higher-level disease traits.

Leptin Downregulates Angulin-1 in Active Crohn's Disease via STAT3

Crohn’s disease (CD) has an altered intestinal barrier function, yet the underlying mechanisms remain to be disclosed. The tricellular tight junction protein tricellulin is involved in the maintenance of the paracellular macromolecule barrier and features an unchanged expression level in CD but a shifted localization. As angulins are known to regulate the localization of tricellulin, we hypothesized the involvement of angulins in CD. Using human biopsies, we found angulin-1 was downregulated in active CD compared with both controls and CD in remission. In T84 and Caco-2 monolayers, leptin, a cytokine secreted by fat tissue and affected in CD, decreased angulin-1 expression. This effect was completely blocked by STAT3 inhibitors, Stattic and WP1066, but only partially by JAK2 inhibitor AG490. The effect of leptin was also seen at a functional level as we observed in Caco-2 cells an increased permeability for FITC-dextran 4 kDa indicating an impaired barrier against macromolecule uptake. In conclusion, we were able to show that in active CD angulin-1 expression is downregulated, which leads to increased macromolecule permeability and is inducible by leptin via STAT3. This suggests that angulin-1 and leptin secretion are potential targets for intervention in CD to restore the impaired intestinal barrier.