δ-Catenin, a Wnt/β-catenin modulator, reveals inducible mutagenesis promoting cancer cell survival adaptation and metabolic reprogramming

Mutations of Wnt/β-catenin signaling pathway has essential roles in development and cancer. Although β-catenin and adenomatous polyposis coli (APC) gene mutations are well established and are known to drive tumorigenesis, discoveries of mutations in other components of the pathway lagged, which hinders the understanding of cancer mechanisms. Here we report that δ-catenin (gene designation: CTNND2), a primarily neural member of the β-catenin superfamily that promotes canonical Wnt/β-catenin/LEF-1-mediated transcription, displays exonic mutations in human prostate cancer and promotes cancer cell survival adaptation and metabolic reprogramming. When overexpressed in cells derived from prostate tumor xenografts, δ-catenin gene invariably gives rise to mutations, leading to sequence disruptions predicting functional alterations. Ectopic δ-catenin gene integrating into host chromosomes is locus nonselective. δ-Catenin mutations promote tumor development in mouse prostate with probasin promoter (ARR2PB)-driven, prostate-specific expression of Myc oncogene, whereas mutant cells empower survival advantage upon overgrowth and glucose deprivation. Reprogramming energy utilization accompanies the downregulation of glucose transporter-1 and poly (ADP-ribose) polymerase cleavage while preserving tumor type 2 pyruvate kinase expression. δ-Catenin mutations increase β-catenin translocation to the nucleus and hypoxia-inducible factor 1α (HIF-1α) expression. Therefore, introducing δ-catenin mutations is an important milestone in prostate cancer metabolic adaptation by modulating β-catenin and HIF-1α signaling under glucose shortage to amplify its tumor-promoting potential.

Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure

The U.S. Tox21 and EPA ToxCast program screen thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors, then used supervised machine learning to predict in vivo hepatotoxic effects. A set of 677 chemicals was represented by 711 in vitro bioactivity descriptors (from ToxCast assays), 4,376 chemical structure descriptors (from QikProp, OpenBabel, PaDEL, and PubChem), and three hepatotoxicity categories (from animal studies). Hepatotoxicants were defined by rat liver histopathology observed after chronic chemical testing and grouped into hypertrophy (161), injury (101) and proliferative lesions (99). Classifiers were built using six machine learning algorithms: linear discriminant analysis (LDA), Naïve Bayes (NB), support vector machines (SVM), classification and regression trees (CART), k-nearest neighbors (KNN), and an ensemble of these classifiers (ENSMB). Classifiers of hepatotoxicity were built using chemical structure descriptors, ToxCast bioactivity descriptors, and hybrid descriptors. Predictive performance was evaluated using 10-fold cross-validation testing and in-loop, filter-based, feature subset selection. Hybrid classifiers had the best balanced accuracy for predicting hypertrophy (0.84 ± 0.08), injury (0.80 ± 0.09), and proliferative lesions (0.80 ± 0.10). Though chemical and bioactivity classifiers had a similar balanced accuracy, the former were more sensitive, and the latter were more specific. CART, ENSMB, and SVM classifiers performed the best, and nuclear receptor activation and mitochondrial functions were frequently found in highly predictive classifiers of hepatotoxicity. ToxCast and ToxRefDB provide the largest and richest publicly available data sets for mining linkages between the in vitro bioactivity of environmental chemicals and their adverse histopathological outcomes. Our findings demonstrate the utility of high-throughput assays for characterizing rodent hepatotoxicants, the benefit of using hybrid representations that integrate bioactivity and chemical structure, and the need for objective evaluation of classification performance.

Nasal endoscopic findings and nasal symptoms in patients with asthma: a clinical study from a rhinological perspective

OBJECTIVE

Allergic rhinitis (AR), non-allergic rhinitis (NAR), chronic rhinosinusitis with nasal polyps (CRSwNP), and chronic rhinosinusitis without nasal polyps (CRSsNP) occur frequently in asthmatic patients. We evaluated nasal symptoms and nasal endoscopic findings in patients with asthma and correlated them to asthma severity.

METHODS

Subjects (n=150) with asthma completed questionnaires designed to provide information related to asthma and nasal disease. Patients were divided into four groups based on asthma severity. Pulmonary function tests, skin-prick tests (SPTs) and nasal endoscopy were performed on every patient. Clinical findings were compared in asthma patients by rhinologists.

RESULTS

The total incidence of AR, NAR, CRSwNP and CRSsNP in these patients with asthma was 76%. By using Fisher's Exact Test, there was no statistical significance between asthma severity and the incidence of AR, NAR, CRSwNP and CRSsNP (P=0.311). There was a significant difference in the total nasal symptoms score among subjects with different grades of asthma (P=0.002). However, there were no significant differences in the total Lund-Kennedy endoscopic score (LKS) (P=0.736). The nasal endoscopic scores were significantly correlated at a high degree with the nasal symptoms score (P=0.000). A significant correlation was found between the nasal endoscopic score and the duration of asthma in the patients with different grades of asthma (P<0.05).

CONCLUSIONS

The relationship between rhinitis and asthma is complex. Nasal airways should become part of standard clinical assessment and follow-up in patients with asthma.

An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era

BACKGROUND

Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment?

RESULTS

We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined.

CONCLUSIONS

Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.

Quality control metrics improve repeatability and reproducibility of single-nucleotide variants derived from whole-genome sequencing

Although many quality control (QC) methods have been developed to improve the quality of single-nucleotide variants (SNVs) in SNV-calling, QC methods for use subsequent to single-nucleotide polymorphism-calling have not been reported. We developed five QC metrics to improve the quality of SNVs using the whole-genome-sequencing data of a monozygotic twin pair from the Korean Personal Genome Project. The QC metrics improved both repeatability between the monozygotic twin pair and reproducibility between SNV-calling pipelines. We demonstrated the QC metrics improve reproducibility of SNVs derived from not only whole-genome-sequencing data but also whole-exome-sequencing data. The QC metrics are calculated based on the reference genome used in the alignment without accessing the raw and intermediate data or knowing the SNV-calling details. Therefore, the QC metrics can be easily adopted in downstream association analysis.

Human sex hormone-binding globulin binding affinities of 125 structurally diverse chemicals and comparison with their binding to androgen receptor, estrogen receptor, and α-fetoprotein

One endocrine disruption mechanism is through binding to nuclear receptors such as the androgen receptor (AR) and estrogen receptor (ER) in target cells. The concentration of a chemical in serum is important for its entry into the target cells to bind the receptors, which is regulated by the serum proteins. Human sex hormone-binding globulin (SHBG) is the major transport protein in serum that can bind androgens and estrogens and thus change a chemical's availability to enter the target cells. Sequestration of an androgen or estrogen in the serum can alter the chemical elicited AR- and ER-mediated responses. To better understand the chemical-induced endocrine activity, we developed a competitive binding assay using human pregnancy plasma and measured the binding to the human SHBG for 125 structurally diverse chemicals, most of which were known to bind AR and ER. Eighty seven chemicals were able to bind the human SHBG in the assay, whereas 38 chemicals were nonbinders. Binding data for human SHBG are compared with that for rat α-fetoprotein, ER and AR. Knowing the binding profiles between serum and nuclear receptors will improve assessment of a chemical's potential for endocrine disruption. The SHBG binding data reported here represent the largest data set of structurally diverse chemicals tested for human SHBG binding. Utilization of the SHBG binding data with AR and ER binding data could enable better evaluation of endocrine disrupting potential of chemicals through AR- and ER-mediated responses since sequestration in serum could be considered.

Whole genome sequencing of 35 individuals provides insights into the genetic architecture of Korean population

Background

Due to a significant decline in the costs associated with next-generation sequencing, it has become possible to decipher the genetic architecture of a population by sequencing a large number of individuals to a deep coverage. The Korean Personal Genomes Project (KPGP) recently sequenced 35 Korean genomes at high coverage using the Illumina Hiseq platform and made the deep sequencing data publicly available, providing the scientific community opportunities to decipher the genetic architecture of the Korean population.

Methods

In this study, we used two single nucleotide variant (SNV) calling pipelines: mapping the raw reads obtained from whole genome sequencing of 35 Korean individuals in KPGP using BWA and SOAP2 followed by SNV calling using SAMtools and SOAPsnp, respectively. The consensus SNVs obtained from the two SNV pipelines were used to represent the SNVs of the Korean population. We compared these SNVs to those from 17 other populations provided by the HapMap consortium and the 1000 Genomes Project (1KGP) and identified SNVs that were only present in the Korean population. We studied the mutation spectrum and analyzed the genes of non-synonymous SNVs only detected in the Korean population.

Results

We detected a total of 8,555,726 SNVs in the 35 Korean individuals and identified 1,213,613 SNVs detected in at least one Korean individual (SNV-1) and 12,640 in all of 35 Korean individuals (SNV-35) but not in 17 other populations. In contrast with the SNVs common to other populations in HapMap and 1KGP, the Korean only SNVs had high percentages of non-silent variants, emphasizing the unique roles of these Korean only SNVs in the Korean population. Specifically, we identified 8,361 non-synonymous Korean only SNVs, of which 58 SNVs existed in all 35 Korean individuals. The 5,754 genes of non-synonymous Korean only SNVs were highly enriched in some metabolic pathways. We found adhesion is the top disease term associated with SNV-1 and Nelson syndrome is the only disease term associated with SNV-35. We found that a significant number of Korean only SNVs are in genes that are associated with the drug term of adenosine.

Conclusion

We identified the SNVs that were found in the Korean population but not seen in other populations, and explored the corresponding genes and pathways as well as the associated disease terms and drug terms. The results expand our knowledge of the genetic architecture of the Korean population, which will benefit the implementation of personalized medicine for the Korean population.

Competitive molecular docking approach for predicting estrogen receptor subtype α agonists and antagonists

Background

Endocrine disrupting chemicals (EDCs) are exogenous compounds that interfere with the endocrine system of vertebrates, often through direct or indirect interactions with nuclear receptor proteins. Estrogen receptors (ERs) are particularly important protein targets and many EDCs are ER binders, capable of altering normal homeostatic transcription and signaling pathways. An estrogenic xenobiotic can bind ER as either an agonist or antagonist to increase or inhibit transcription, respectively. The receptor conformations in the complexes of ER bound with agonists and antagonists are different and dependent on interactions with co-regulator proteins that vary across tissue type. Assessment of chemical endocrine disruption potential depends not only on binding affinity to ERs, but also on changes that may alter the receptor conformation and its ability to subsequently bind DNA response elements and initiate transcription. Using both agonist and antagonist conformations of the ERα, we developed an in silico approach that can be used to differentiate agonist versus antagonist status of potential binders.

Methods

The approach combined separate molecular docking models for ER agonist and antagonist conformations. The ability of this approach to differentiate agonists and antagonists was first evaluated using true agonists and antagonists extracted from the crystal structures available in the protein data bank (PDB), and then further validated using a larger set of ligands from the literature. The usefulness of the approach was demonstrated with enrichment analysis in data sets with a large number of decoy ligands.

Results

The performance of individual agonist and antagonist docking models was found comparable to similar models in the literature. When combined in a competitive docking approach, they provided the ability to discriminate agonists from antagonists with good accuracy, as well as the ability to efficiently select true agonists and antagonists from decoys during enrichment analysis.

Conclusion

This approach enables evaluation of potential ER biological function changes caused by chemicals bound to the receptor which, in turn, allows the assessment of a chemical's endocrine disrupting potential. The approach can be used not only by regulatory authorities to perform risk assessments on potential EDCs but also by the industry in drug discovery projects to screen for potential agonists and antagonists.

Toward predictive models for drug-induced liver injury in humans: are we there yet?

Drug-induced liver injury (DILI) is a frequent cause for the termination of drug development programs and a leading reason of drug withdrawal from the marketplace. Unfortunately, the current preclinical testing strategies, including the regulatory-required animal toxicity studies or simple in vitro tests, are insufficiently powered to predict DILI in patients reliably. Notably, the limited predictive power of such testing strategies is mostly attributed to the complex nature of DILI, a poor understanding of its mechanism, a scarcity of human hepatotoxicity data and inadequate bioinformatics capabilities. With the advent of high-content screening assays, toxicogenomics and bioinformatics, multiple end points can be studied simultaneously to improve prediction of clinically relevant DILIs. This review focuses on the current state of efforts in developing predictive models from diverse data sources for potential use in detecting human hepatotoxicity, and also aims to provide perspectives on how to further improve DILI prediction.

Improved methods for selective cryolipolysis results in subcutaneous fat layer reduction in a porcine model

BACKGROUND/AIMS

Cryolipolysis is a noninvasive method for the selective reduction of localized fat tissues. It has demonstrated efficacy in both clinical and preclinical trials; however, despite its popularity, its mechanisms of action and evaluation methods are not yet fully defined. The purpose of this study was to improved methods for cryolipolysis using a porcine model.

METHODS

The abdomens of female PWG micro-pigs were treated with a cooling device (CRYOLIPO II(™)), and we examined the treatment effects using photography, three-dimensional photography, ultrasound, gross, and microscopic pathology, and serum lipid level analyses in order to determine the mechanism of action, efficacy, and safety of CRYOLIPO II(™).

RESULTS

CRYOLIPO II(™) successfully reduced abdominal fat in our porcine model. Gross and microscopic histological results confirmed the noninvasive cold-induced selective subcutaneous fat destruction, and showed increases in pre-adipocyte differentiation and in the activation of lipid catabolism. In particular, we found that CRYOLIPO II(™) may increase PPARδ (delta) levels in adipose tissue at 30-60 days post-treatment.

CONCLUSION

Fat reduction by cryolipolysis was successfully achieved in our porcine model. Thus, our findings indicate that CRYOLIPO II(™) may be a promising fat reduction device for body contouring and fat reduction in humans, and that cryolipolysis exerts its effects, at least partly, by targeting the PPARδ signaling pathway. These results show that both investigative and diagnostic potentials capacity.

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

Dynamic layer rearrangement during growth of layered oxide films by molecular beam epitaxy

The A(n+1)B(n)O(3n+1) Ruddlesden-Popper homologous series offers a wide variety of functionalities including dielectric, ferroelectric, magnetic and catalytic properties. Unfortunately, the synthesis of such layered oxides has been a major challenge owing to the occurrence of growth defects that result in poor materials behaviour in the higher-order members. To understand the fundamental physics of layered oxide growth, we have developed an oxide molecular beam epitaxy system with in situ synchrotron X-ray scattering capability. We present results demonstrating that layered oxide films can dynamically rearrange during growth, leading to structures that are highly unexpected on the basis of the intended layer sequencing. Theoretical calculations indicate that rearrangement can occur in many layered oxide systems and suggest a general approach that may be essential for the construction of metastable Ruddlesden-Popper phases. We demonstrate the utility of the new-found growth strategy by performing the first atomically controlled synthesis of single-crystalline La3Ni2O7.

Versatility or promiscuity: the estrogen receptors, control of ligand selectivity and an update on subtype selective ligands

The estrogen receptors (ERs) are a group of versatile receptors. They regulate an enormity of processes starting in early life and continuing through sexual reproduction, development, and end of life. This review provides a background and structural perspective for the ERs as part of the nuclear receptor superfamily and discusses the ER versatility and promiscuity. The wide repertoire of ER actions is mediated mostly through ligand-activated transcription factors and many DNA response elements in most tissues and organs. Their versatility, however, comes with the drawback of promiscuous interactions with structurally diverse exogenous chemicals with potential for a wide range of adverse health outcomes. Even when interacting with endogenous hormones, ER actions can have adverse effects in disease progression. Finally, how nature controls ER specificity and how the subtle differences in receptor subtypes are exploited in pharmaceutical design to achieve binding specificity and subtype selectivity for desired biological response are discussed. The intent of this review is to complement the large body of literature with emphasis on most recent developments in selective ER ligands.

Transcriptomic profiling of rat liver samples in a comprehensive study design by RNA-Seq

RNA-Seq provides the capability to characterize the entire transcriptome in multiple levels including gene expression, allele specific expression, alternative splicing, fusion gene detection, and etc. The US FDA-led SEQC (i.e., MAQC-III) project conducted a comprehensive study focused on the transcriptome profiling of rat liver samples treated with 27 chemicals to evaluate the utility of RNA-Seq in safety assessment and toxicity mechanism elucidation. The chemicals represented multiple chemogenomic modes of action (MOA) and exhibited varying degrees of transcriptional response. The paired-end 100 bp sequencing data were generated using Illumina HiScanSQ and/or HiSeq 2000. In addition to the core study, six animals (i.e., three aflatoxin B1 treated rats and three vehicle control rats) were sequenced three times, with two separate library preparations on two sequencing machines. This large toxicogenomics dataset can serve as a resource to characterize various aspects of transcriptomic changes (e.g., alternative splicing) that are byproduct of chemical perturbation.

Quantum dot-based nanoprobes for in vivo targeted imaging

Fluorescent semiconductor quantum dots (QDs) have attracted tremendous attention over the last decade. The superior optical properties of QDs over conventional organic dyes make them attractive labels for a wide variety of biomedical applications, whereas their potential toxicity and instability in biological environment have puzzled scientific researchers. Much research effort has been devoted to surface modification and functionalization of QDs to make them versatile probes for biomedical applications, and significant progress has been made over the last several years. This review article aims to describe the current state-of-the-art of the synthesis, modification, bioconjugation, and applications of QDs for in vivo targeted imaging. In addition, QD-based multifunctional nanoprobes are also summarized.

Multimodality imaging of CXCR4 in cancer: current status towards clinical translation

CXCR4 has gained tremendous attention over the last decade, since it was found to be up-regulated in a wide variety of cancer types, in addition to its role in human immunodeficiency virus infection. Molecular imaging of CXCR4 with small molecules, peptides, and antibodies has been a vibrant research area over the last several years. In this review article, we will summarize the current status of imaging CXCR4 with fluorescence, bioluminescence, positron emission tomography, and single-photon emission computed tomography techniques. Since each molecular imaging modality has its own strengths and weaknesses, dualmodality probes that can be detected by more than one imaging techniques have also been investigated. Noninvasive visualization of CXCR4 expression has potential clinical applications in multiple facets of patient management. While big strides have been made over the last several years in the development of CXCR4- targeted imaging probes, clinical translation and investigation of these agents in cancer patients are eagerly awaited. Since CXCR4 is also involved in many other diseases beyond cancer, these clinically translatable probes can also play multiple roles in other pathological disorders such as myocardial infarction and several immunodeficiency disorders.

Estimating relative noise to signal in DNA microarray data

A method was proposed for estimating noise relative to signal in microarray data. A signal to noise index, SNI, was defined and used to measure the level of signal compared to the noise contained in two microarray data sets. Simulations were conducted to generate the quantitative relationship between the SNI and its measurement of relative noise. The method was applied to two well known microarray data sets. Relative noise was estimated for both data sets, and the results were consistent with the observations in the original papers, demonstrating the proposed method is reliable for estimating relative noise in microarray data.

Toxicogenomics and cancer susceptibility: advances with next-generation sequencing

The aim of this review is to comprehensively summarize the recent achievements in the field of toxicogenomics and cancer research regarding genetic-environmental interactions in carcinogenesis and detection of genetic aberrations in cancer genomes by next-generation sequencing technology. Cancer is primarily a genetic disease in which genetic factors and environmental stimuli interact to cause genetic and epigenetic aberrations in human cells. Mutations in the germline act as either high-penetrance alleles that strongly increase the risk of cancer development, or as low-penetrance alleles that mildly change an individual's susceptibility to cancer. Somatic mutations, resulting from either DNA damage induced by exposure to environmental mutagens or from spontaneous errors in DNA replication or repair are involved in the development or progression of the cancer. Induced or spontaneous changes in the epigenome may also drive carcinogenesis. Advances in next-generation sequencing technology provide us opportunities to accurately, economically, and rapidly identify genetic variants, somatic mutations, gene expression profiles, and epigenetic alterations with single-base resolution. Whole genome sequencing, whole exome sequencing, and RNA sequencing of paired cancer and adjacent normal tissue present a comprehensive picture of the cancer genome. These new findings should benefit public health by providing insights in understanding cancer biology, and in improving cancer diagnosis and therapy.