Hong, M.-Y. | bioRxiv Science

bioRxiv2020

Mutant glucocorticoid receptor binding elements on Interleukin-6 promoter regulate dexamethasone effects

Glucocorticoid has been widely used as an important modulator for clinical infectious and inflammatory disease. Glucocorticoid receptor (GR) is a transcription factor belonging to the family of nuclear receptors, regulated anti-inflammatory process and the release of pro-inflammatory cytokines. Five putative GR and other transcription factor binding sites on interleukin (IL)-6 promoter were identified and dexamethasone could reduce LPS-induced IL-6 release. Among them, the mutant transcriptional factors NF-{kappa}B, AP-1, and Sp1-2 site decreased the basal and effects of lipopolysaccharide (LPS)-induced IL-6 promoter activities in different responses. GR2/3 seemed to be an important role in both basal and inducible promoter activities in LPS-induced inflammation. We concluded that the selective GR2/3 modulators may have agonistic and antagonistic combined effects and activate important signaling pathway during LPS-stimulated inflammatory process.

immunology↗

bioRxiv2018

GenEpi: Gene-based Epistasis Discovery Using Machine Learning

Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimers disease (AD). In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power. Availability: GenEpi is an open-source python package and available free of charge only for non-commercial users. The package can be downloaded from https://github.com/Chester75321/GenEpi, and has also been published on The Python Package Index.

bioinformatics↗