High-throughput allele-specific expression across 250 environmental conditions.

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 genome-wide association studies.

Which Genetics Variants in DNase-Seq Footprints Are More Likely to Alter Binding?

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.

QuASAR: quantitative allele-specific analysis of reads.

MOTIVATION: Expression quantitative trait loci (eQTL) studies have discovered thousands of genetic variants that regulate gene expression, enabling a better understanding of the functional role of non-coding sequences. However, eQTL studies are costly, requiring large sample sizes and genome-wide genotyping of each sample. In contrast, analysis of allele-specific expression (ASE) is becoming a popular approach to detect the effect of genetic variation on gene expression, even within a single individual. This is typically achieved by counting the number of RNA-seq reads matching each allele at heterozygous sites and testing the null hypothesis of a 1:1 allelic ratio. In principle, when genotype information is not readily available, it could be inferred from the RNA-seq reads directly. However, there are currently no existing methods that jointly infer genotypes and conduct ASE inference, while considering uncertainty in the genotype calls. RESULTS: We present QuASAR, quantitative allele-specific analysis of reads, a novel statistical learning method for jointly detecting heterozygous genotypes and inferring ASE. The proposed ASE inference step takes into consideration the uncertainty in the genotype calls, while including parameters that model base-call errors in sequencing and allelic over-dispersion. We validated our method with experimental data for which high-quality genotypes are available. Results for an additional dataset with multiple replicates at different sequencing depths demonstrate that QuASAR is a powerful tool for ASE analysis when genotypes are not available. AVAILABILITY AND IMPLEMENTATION: http://github.com/piquelab/QuASAR. CONTACT: fluca@wayne.edu or rpique@wayne.edu SUPPLEMENTARY INFORMATION: Supplementary Material is available at Bioinformatics online.

Malate dehydrogenase 2 confers docetaxel resistance via regulations of JNK signaling and oxidative metabolism.

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.

HDAC4 protein regulates HIF1α protein lysine acetylation and cancer cell response to hypoxia.

Hypoxia-inducible factor 1 α (HIF1α) is an essential part of the HIF-1 transcriptional complex that regulates angiogenesis, cellular metabolism, and cancer development. In von Hippel-Lindau (VHL)-null kidney cancer cell lines, we reported previously that HIF1α proteins can be acetylated and inhibited by histone deacetylase (HDAC) inhibitors or specific siRNA against HDAC4. To investigate the mechanism and biological consequence of the inhibition, we have generated stable HDAC4 knockdown via shRNA in VHL-positive normal and cancer cell lines. We report that HDAC4 regulates HIF1α protein acetylation and stability. Specifically, the HIF1α protein acetylation can be increased by HDAC4 shRNA and decreased by HDAC4 overexpression. HDAC4 shRNA inhibits HIF1α protein stability. In contrast, HDAC1 or HDAC3 shRNA has no such inhibitory effect. Mutations of the first five lysine residues (lysine 10, 11, 12, 19, and 21) to arginine within the HIF1α N terminus reduce protein acetylation but render the mutant HIF1α protein resistant to HDAC4 and HDACi-mediated inhibition. Functionally, in VHL-positive cancer cell lines, stable inhibition of HDAC4 decreases both the HIF-1 transcriptional activity and a subset of HIF-1 hypoxia target gene expression. On the cellular level, HDAC4 inhibition reduces the hypoxia-related increase of glycolysis and resistance to docetaxel chemotherapy. Taken together, the novel biological relationship between HDAC4 and HIF1α presented here suggests a potential role for the deacetylase enzyme in regulating HIF-1 cancer cell response to hypoxia and presents a more specific molecular target of inhibition.