Gene-by-environment (GxE) interactions determine common disease risk factors and biomedically relevant complex traits. However, quantifying how the environment modulates genetic effects on human quantitative phenotypes presents unique challenges. Environmental covariates are complex and difficult to measure and control at the organismal level, as found in GWAS and epidemiological studies. An alternative approach focuses on the cellular environment using in vitro treatments as a proxy for the organismal environment. These cellular environments simplify the organism-level environmental exposures to provide a tractable influence on subcellular phenotypes, such as gene expression. Expression quantitative trait loci (eQTL) mapping studies identified GxE interactions in response to drug treatment and pathogen exposure. However, eQTL mapping approaches are infeasible for large-scale analysis of multiple cellular environments. Recently, allele-specific expression (ASE) analysis emerged as a powerful tool to identify GxE interactions in gene expression patterns by exploiting naturally occurring environmental exposures. Here we characterized genetic effects on the transcriptional response to 50 treatments in five cell types. We discovered 1455 genes with ASE (FDR < 10%) and 215 genes with GxE interactions. We demonstrated a major role for GxE interactions in complex traits. Genes with a transcriptional response to environmental perturbations showed sevenfold higher odds of being found in GWAS. Additionally, 105 genes that indicated GxE interactions (49%) were identified by GWAS as associated with complex traits. Examples include GIPR-caffeine interaction and obesity and include LAMP3-selenium interaction and Parkinson disease. Our results demonstrate that comprehensive catalogs of GxE interactions are indispensable to thoroughly annotate genes and bridge epidemiological and genomewide association studies.

Background: HIF1␣ is a target of anticancer therapy. Results: Lysines within the HIF1␣ N terminus are targets of HDAC4 deacetylation. HDAC4 inhibition causes the increase of HIF1␣ protein acetylation and decrease of protein stability, which lead to the reduction of HIF-1-mediated target gene expressions and activities in cancer cells. Conclusion: HDAC4 provides a novel HIF1␣ regulatory mechanism. Significance: HIF-1 can be targeted by HDAC4 inhibition.

Large experimental efforts are characterizing the regulatory genome, yet we are still missing a systematic definition of functional and silent genetic variants in non-coding regions. Here, we integrated DNaseI footprinting data with sequence-based transcription factor (TF) motif models to predict the impact of a genetic variant on TF binding across 153 tissues and 1,372 TF motifs. Each annotation we derived is specific for a cell-type condition or assay and is locally motif-driven. We found 5.8 million genetic variants in footprints, 66% of which are predicted by our model to affect TF binding. Comprehensive examination using allele-specific hypersensitivity (ASH) reveals that only the latter group consistently shows evidence for ASH (3,217 SNPs at 20% FDR), suggesting that most (97%) genetic variants in footprinted regulatory regions are indeed silent. Combining this information with GWAS data reveals that our annotation helps in computationally fine-mapping 86 SNPs in GWAS hit regions with at least a 2-fold increase in the posterior odds of picking the causal SNP. The rich meta information provided by the tissue-specificity and the identity of the putative TF binding site being affected also helps in identifying the underlying mechanism supporting the association. As an example, the enrichment for LDL level-associated SNPs is 9.1-fold higher among SNPs predicted to affect HNF4 binding sites than in a background model already including tissue-specific annotation.

Background Resistance to chemotherapy represents a significant obstacle in prostate cancer therapeutics. Novel mechanistic understandings in cancer cell chemotherapeutic sensitivity and resistance can optimize treatment and improve patient outcome. Molecular alterations in the metabolic pathways are associated with cancer development; however, the role of these alterations in chemotherapy efficacy is largely unknown. Methods In a bed-side to bench-side reverse translational approach, we used cDNA microarray and qRT-PCR to identify genes that are associated with biochemical relapse after chemotherapy. Further, we tested the function of these genes in cell proliferation, metabolism, and chemosensitivity in prostate cancer cell lines. Results We report that the gene encoding mitochondrial malate dehydrogenase 2 (MDH2) is overexpressed in clinical prostate cancer specimens. Patients with MDH2 overexpression had a significantly shorter period of relapse-free survival (RFS) after undergoing neoadjuvant chemotherapy. To understand the molecular mechanism underlying this clinical observation, we observed that MDH2 expression was elevated in prostate cancer cell lines compared to benign prostate epithelial cells. Stable knockdown of MDH2 via shRNA in prostate cancer cell lines decreased cell proliferation and increased docetaxel sensitivity. Further, MDH2 shRNA enhanced docetaxel-induced activations of JNK signaling and induced metabolic inefficiency. Conclusion Taken together, these data suggest a novel function for MDH2 in prostate cancer development and chemotherapy resistance, in which MDH2 regulates chemotherapy-induced signal transduction and oxidative metabolism.

Supplementary Material is available at Bioinformatics online.