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Hypoxia is a universal pathological feature of solid tumors. Hypoxic tumor cells acquire metastatic and lethal phenotypes primarily through the activities of hypoxia-inducible factor 1 alpha (HIF1α). Therefore, HIF1α is considered as a promising therapeutic target. However, HIF inhibitors have not proven to be effective in clinical testing. The underlying mechanism is unclear. We report that oncogenic protein ID1 is upregulated in hypoxia by HIF1α shRNA or pharmacological inhibitors. In turn, ID1 supports tumor growth in hypoxia in vitro and in xenografts in vivo , conferring adaptive survival response and resistance. Mechanistically, ID1 proteins interfere HIF1-mediated gene transcription activation, thus ID1 protein degradation is accelerated by HIF1α-dependent mechanisms in hypoxia. Inhibitions of HIF1α rescues ID1, which compensates the loss of HIF1α by the upregulation of GLS2 and glutamine metabolism, thereby switching the metabolic dependency of HIF1α -inhibited cells from glucose to glutamine., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Geng, Ko, Pittsenbarger, Harvey, Xue, Liu, Wiens, Kachhap, Beer and Qian.)

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., (© 2016 Moyerbrailean et al.; Published by Cold Spring Harbor Laboratory Press.)

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.

In recent years RNA-seq protocols have been developed to investigate a variety of biological problems by measuring the abundance of different RNAs. Many study designs involve performing expensive preliminary studies to screen or optimize experimental conditions. Testing a large number of conditions in parallel may be more cost effective. For example, analyzing tissue/environment-specific gene expression generally implies screening a large number of cellular conditions and samples, without prior knowledge of which conditions are most informative (e.g., some cell types may not respond to certain treatments). To circumvent these challenges, we have established a new two-step high-throughput RNA-seq approach: the first step consists of gene expression screening of a large number of conditions, while the second step focuses on deep sequencing of the most relevant conditions (e.g., largest number of differentially expressed genes). This study design allows for a fast and economical screen in step one, with a more efficient allocation of resources for the deep sequencing of the most biologically relevant libraries in step two. We have applied this approach to study the response to 23 treatments in three lymphoblastoid cell lines demonstrating that it should also be useful for other high-throughput transcriptome profiling applications requiring iterative refinement or screening.

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., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

We present a system for marking the skin during oral surgical operations. This system identifies teeth to be extracted or exposed under general anaesthesia. Removal of the wrong tooth can cause appreciable morbidity and leaves the surgeon and organisation liable for litigation and scrutiny by regulatory bodies. A recent review of claims to the NHS litigation authority between 1995 and 2010 showed that in the field of oral and maxillofacial surgery, dentoalveolar surgery resulted in the largest number of claims for negligence, of which removal of the wrong tooth was one of the most common. In 2010/2011 the National Reporting and Learning System (NRLS) of the National Patient Safety Agency (NPSA) were notified of 20 incidents when the wrong tooth had been extracted, which accounted for 5% of all incidents reported. We have therefore developed a robust marking system for oral surgical procedures in our hospital, which improves on the World Health Organisation (WHO) checklist. We have audited patients' perceptions and the clinical application of our marking system, and have shown that the system is welcomed by patients, and is simple and effective for clinicians to use., (Copyright © 2012 The British Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.)

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., (Copyright © 2013 Wiley Periodicals, Inc.)

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.

To identify potential mechanisms underlying prostate cancer chemotherapy response and resistance, we compared the gene expression profiles in high-risk human prostate cancer specimens before and after neoadjuvant chemotherapy and radical prostatectomy. Among the molecular signatures associated with chemotherapy, transcripts encoding inhibitor of DNA binding 1 (ID1) were significantly upregulated. The patient biochemical relapse status was monitored in a long-term follow-up. Patients with ID1 upregulation were found to be associated with longer relapse-free survival than patients without ID1 increase. This in vivo clinical association was mechanistically investigated. The chemotherapy-induced ID1 upregulation was recapitulated in the prostate cancer cell line LNCaP. Docetaxel dose-dependently induced ID1 transcription, which was mediated by ID1 promoter E-box chromatin modification and c-Myc binding. Stable ID1 overexpression in LNCaP increased cell proliferation, promoted G(1) cell cycle progression, and enhanced docetaxel-induced cytotoxicity. These changes were accompanied by a decrease in cellular mitochondria content, an increase in BCL2 phosphorylation at serine 70, caspase-3 activation, and poly(ADP-ribose) polymerase cleavage. In contrast, ID1 siRNA in the LNCaP and C42B cell lines reduced cell proliferation and decreased docetaxel-induced cytotoxicity by inhibiting cell death. ID1-mediated chemosensitivity enhancement was in part due to ID1 suppression of p21. Overexpression of p21 in LNCaP-ID1-overexpressing cells restored the p21 level and reversed ID1-enhanced chemosensitivity. These molecular data provide a mechanistic rationale for the observed in vivo clinical association between ID1 upregulation and relapse-free survival. Taken together, it shows that ID1 expression has a novel therapeutic role in prostate cancer chemotherapy and prognosis., ((c)2010 AACR.)