Modeling PAH Mixture Interactions in a Human In Vitro Organotypic Respiratory Model.

One of the most significant challenges in human health risk assessment is to evaluate hazards from exposure to environmental chemical mixtures. Polycyclic aromatic hydrocarbons (PAHs) are a class of ubiquitous contaminants typically found as mixtures in gaseous and particulate phases in ambient air pollution associated with petrochemicals from Superfund sites and the burning of fossil fuels. However, little is understood about how PAHs in mixtures contribute to toxicity in lung cells. To investigate mixture interactions and component additivity from environmentally relevant PAHs, two synthetic mixtures were created from PAHs identified in passive air samplers at a legacy creosote site impacted by wildfires. The primary human bronchial epithelial cells differentiated at the air-liquid interface were treated with PAH mixtures at environmentally relevant proportions and evaluated for the differential expression of transcriptional biomarkers related to xenobiotic metabolism, oxidative stress response, barrier integrity, and DNA damage response. Component additivity was evaluated across all endpoints using two independent action (IA) models with and without the scaling of components by toxic equivalence factors. Both IA models exhibited trends that were unlike the observed mixture response and generally underestimated the toxicity across dose suggesting the potential for non-additive interactions of components. Overall, this study provides an example of the usefulness of mixture toxicity assessment with the currently available methods while demonstrating the need for more complex yet interpretable mixture response evaluation methods for environmental samples.

Hypergraph models of biological networks to identify genes critical to pathogenic viral response.

BACKGROUND: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. RESULTS: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. CONCLUSIONS: Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.

Gene co-expression network analysis in zebrafish reveals chemical class specific modules.

BACKGROUND: Zebrafish is a popular animal model used for high-throughput screening of chemical hazards, however, investigations of transcriptomic mechanisms of toxicity are still needed. Here, our goal was to identify genes and biological pathways that Aryl Hydrocarbon Receptor 2 (AHR2) Activators and flame retardant chemicals (FRCs) alter in developing zebrafish. Taking advantage of a compendium of phenotypically-anchored RNA sequencing data collected from 48-h post fertilization (hpf) zebrafish, we inferred a co-expression network that grouped genes based on their transcriptional response. RESULTS: Genes responding to the FRCs and AHR2 Activators localized to distinct regions of the network, with FRCs inducing a broader response related to neurobehavior. AHR2 Activators centered in one region related to chemical stress responses. We also discovered several highly co-expressed genes in this module, including cyp1a, and we subsequently show that these genes are definitively within the AHR2 signaling pathway. Systematic removal of the two chemical types from the data, and analysis of network changes identified neurogenesis associated with FRCs, and regulation of vascular development associated with both chemical classes. We also identified highly connected genes responding specifically to each class that are potential biomarkers of exposure. CONCLUSIONS: Overall, we created the first zebrafish chemical-specific gene co-expression network illuminating how chemicals alter the transcriptome relative to each other. In addition to our conclusions regarding FRCs and AHR2 Activators, our network can be leveraged by other studies investigating chemical mechanisms of toxicity.

Biallelic Mutations in ATP5F1D, which Encodes a Subunit of ATP Synthase, Cause a Metabolic Disorder.

ATP synthase, H+ transporting, mitochondrial F1 complex, δ subunit (ATP5F1D; formerly ATP5D) is a subunit of mitochondrial ATP synthase and plays an important role in coupling proton translocation and ATP production. Here, we describe two individuals, each with homozygous missense variants in ATP5F1D, who presented with episodic lethargy, metabolic acidosis, 3-methylglutaconic aciduria, and hyperammonemia. Subject 1, homozygous for c.245C>T (p.Pro82Leu), presented with recurrent metabolic decompensation starting in the neonatal period, and subject 2, homozygous for c.317T>G (p.Val106Gly), presented with acute encephalopathy in childhood. Cultured skin fibroblasts from these individuals exhibited impaired assembly of F1FO ATP synthase and subsequent reduced complex V activity. Cells from subject 1 also exhibited a significant decrease in mitochondrial cristae. Knockdown of Drosophila ATPsynδ, the ATP5F1D homolog, in developing eyes and brains caused a near complete loss of the fly head, a phenotype that was fully rescued by wild-type human ATP5F1D. In contrast, expression of the ATP5F1D c.245C>T and c.317T>G variants rescued the head-size phenotype but recapitulated the eye and antennae defects seen in other genetic models of mitochondrial oxidative phosphorylation deficiency. Our data establish c.245C>T (p.Pro82Leu) and c.317T>G (p.Val106Gly) in ATP5F1D as pathogenic variants leading to a Mendelian mitochondrial disease featuring episodic metabolic decompensation.

Atomic Force Microscopy and Infrared Nanospectroscopy of COVID-19 Spike Protein for the Quantification of Adhesion to Common Surfaces.

The COVID-19 pandemic has claimed millions of lives worldwide, sickened many more, and has resulted in severe socioeconomic consequences. As society returns to normal, understanding the spread and persistence of SARS CoV-2 on commonplace surfaces can help to mitigate future outbreaks of coronaviruses and other pathogens. We hypothesize that such an understanding can be aided by studying the binding and interaction of viral proteins with nonbiological surfaces. Here, we propose a methodology for investigating the adhesion of the SARS CoV-2 spike glycoprotein on common inorganic surfaces such as aluminum, copper, iron, silica, and ceria oxides as well as metallic gold. Quantitative adhesion was obtained from the analysis of measured forces at the nanoscale using an atomic force microscope operated under ambient conditions. Without imposing further constraints on the measurement conditions, our preliminary findings suggest that spike glycoproteins interact with similar adhesion forces across the majority of the metal oxides tested with the exception to gold, for which attraction forces ∼10 times stronger than all other materials studied were observed. Ferritin, which was used as a reference protein, was found to exhibit similar adhesion forces as SARS CoV-2 spike protein. This study results show that glycoprotein adhesion forces for similar ambient humidity, tip shape, and contact surface are nonspecific to the properties of metal oxide surfaces, which are expected to be covered by a thin water film. The findings suggest that under ambient conditions, glycoprotein adhesion to metal oxides is primarily controlled by the water capillary forces, and they depend on the surface tension of the liquid water. We discuss further strategies warranted to decipher the intricate nanoscale forces for improved quantification of the adhesion.

MERS-CoV and H5N1 influenza virus antagonize antigen presentation by altering the epigenetic landscape.

Convergent evolution dictates that diverse groups of viruses will target both similar and distinct host pathways to manipulate the immune response and improve infection. In this study, we sought to leverage this uneven viral antagonism to identify critical host factors that govern disease outcome. Utilizing a systems-based approach, we examined differential regulation of IFN-γ-dependent genes following infection with robust respiratory viruses including influenza viruses [A/influenza/Vietnam/1203/2004 (H5N1-VN1203) and A/influenza/California/04/2009 (H1N1-CA04)] and coronaviruses [severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV)]. Categorizing by function, we observed down-regulation of gene expression associated with antigen presentation following both H5N1-VN1203 and MERS-CoV infection. Further examination revealed global down-regulation of antigen-presentation gene expression, which was confirmed by proteomics for both H5N1-VN1203 and MERS-CoV infection. Importantly, epigenetic analysis suggested that DNA methylation, rather than histone modification, plays a crucial role in MERS-CoV-mediated antagonism of antigen-presentation gene expression; in contrast, H5N1-VN1203 likely utilizes a combination of epigenetic mechanisms to target antigen presentation. Together, the results indicate a common mechanism utilized by H5N1-VN1203 and MERS-CoV to modulate antigen presentation and the host adaptive immune response.

Statistically Driven Metabolite and Lipid Profiling of Patients from the Undiagnosed Diseases Network.

Advancements in molecular separations coupled with mass spectrometry have enabled metabolome analyses for clinical cohorts. A population of interest for metabolome profiling is patients with rare disease for which abnormal metabolic signatures may yield clues into the genetic basis, as well as mechanistic drivers of the disease and possible treatment options. We undertook the metabolome profiling of a large cohort of patients with mysterious conditions characterized through the Undiagnosed Diseases Network (UDN). Due to the size and enrollment procedures, collection of the metabolomes for UDN patients took place over 2 years. We describe the study designed to adjust for measurements collected over a long time scale and how this enabled statistical analyses to summarize the metabolome of individual patients. We demonstrate the removal of time-based batch effects, overall statistical characteristics of the UDN population, and two case studies of interest that demonstrate the utility of metabolome profiling for rare diseases.

Unified feature association networks through integration of transcriptomic and proteomic data.

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.

Bioinformatics Resource Manager: a systems biology web tool for microRNA and omics data integration.

BACKGROUND: The Bioinformatics Resource Manager (BRM) is a web-based tool developed to facilitate identifier conversion and data integration for Homo sapiens (human), Mus musculus (mouse), Rattus norvegicus (rat), Danio rerio (zebrafish), and Macaca mulatta (macaque), as well as perform orthologous conversions among the supported species. In addition to providing a robust means of identifier conversion, BRM also incorporates a suite of microRNA (miRNA)-target databases upon which to query target genes or to perform reverse target lookups using gene identifiers. RESULTS: BRM has the capability to perform cross-species identifier lookups across common identifier types, directly integrate datasets across platform or species by performing identifier retrievals in the background, and retrieve miRNA targets from multiple databases simultaneously and integrate the resulting gene targets with experimental mRNA data. Here we use workflows provided in BRM to integrate RNA sequencing data across species to identify common biomarkers of exposure after treatment of human lung cells and zebrafish to benzo[a]pyrene (BAP). We further use the miRNA Target workflow to experimentally determine the role of miRNAs as regulators of BAP toxicity and identify the predicted functional consequences of miRNA-target regulation in our system. The output from BRM can easily and directly be uploaded to freely available visualization tools for further analysis. From these examples, we were able to identify an important role for several miRNAs as potential regulators of BAP toxicity in human lung cells associated with cell migration, cell communication, cell junction assembly and regulation of cell death. CONCLUSIONS: Overall, BRM provides bioinformatics tools to assist biologists having minimal programming skills with analysis and integration of high-content omics' data from various transcriptomic and proteomic platforms. BRM workflows were developed in Java and other open-source technologies and are served publicly using Apache Tomcat at https://cbb.pnnl.gov/brm/ .

P-Mart: Interactive Analysis of Ion Abundance Global Proteomics Data.

The use of mass-spectrometry-based techniques for global protein profiling of biomedical or environmental experiments has become a major focus in research centered on biomarker discovery; however, one of the most important issues recently highlighted in the new era of omics data generation is the ability to perform analyses in a robust and reproducible manner. This has been hypothesized to be one of the issues hindering the ability of clinical proteomics to successfully identify clinical diagnostic and prognostic biomarkers of disease. P-Mart ( https://pmart.labworks.org ) is a new interactive web-based software environment that enables domain scientists to perform quality-control processing, statistics, and exploration of large-complex proteomics data sets without requiring statistical programming. P-Mart is developed in a manner that allows researchers to perform analyses via a series of modules, explore the results using interactive visualization, and finalize the analyses with a collection of output files documenting all stages of the analysis and a report to allow reproduction of the analysis.

pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data.

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography-MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

Combination Attenuation Offers Strategy for Live Attenuated Coronavirus Vaccines.

With an ongoing threat posed by circulating zoonotic strains, new strategies are required to prepare for the next emergent coronavirus (CoV). Previously, groups had targeted conserved coronavirus proteins as a strategy to generate live attenuated vaccine strains against current and future CoVs. With this in mind, we explored whether manipulation of CoV NSP16, a conserved 2'O methyltransferase (MTase), could provide a broad attenuation platform against future emergent strains. Using the severe acute respiratory syndrome-CoV mouse model, an NSP16 mutant vaccine was evaluated for protection from heterologous challenge, efficacy in the aging host, and potential for reversion to pathogenesis. Despite some success, concerns for virulence in the aged and potential for reversion makes targeting NSP16 alone an untenable approach. However, combining a 2'O MTase mutation with a previously described CoV fidelity mutant produced a vaccine strain capable of protection from heterologous virus challenge, efficacy in aged mice, and no evidence for reversion. Together, the results indicate that targeting the CoV 2'O MTase in parallel with other conserved attenuating mutations may provide a platform strategy for rapidly generating live attenuated coronavirus vaccines.IMPORTANCE Emergent coronaviruses remain a significant threat to global public health and rapid response vaccine platforms are needed to stem future outbreaks. However, failure of many previous CoV vaccine formulations has clearly highlighted the need to test efficacy under different conditions and especially in vulnerable populations such as the aged and immunocompromised. This study illustrates that despite success in young models, the 2'O methyltransferase mutant carries too much risk for pathogenesis and reversion in vulnerable models to be used as a stand-alone vaccine strategy. Importantly, the 2'O methyltransferase mutation can be paired with other attenuating approaches to provide robust protection from heterologous challenge and in vulnerable populations. Coupled with increased safety and reduced pathogenesis, the study highlights the potential for 2'O methyltransferase attenuation as a major component of future live attenuated coronavirus vaccines.

Toxicokinetics of benzo[a]pyrene in humans: Extensive metabolism as determined by UPLC-accelerator mass spectrometry following oral micro-dosing.

Benzo[a]pyrene (BaP), is a known human carcinogen (International Agency for Research on Cancer (IARC) class 1). The remarkable sensitivity (zepto-attomole 14C in biological samples) of accelerator mass spectrometry (AMS) makes possible, with de minimus risk, pharmacokinetic (PK) analysis following [14C]-BaP micro-dosing of humans. A 46 ng (5 nCi) dose was given thrice to 5 volunteers with minimum 2 weeks between dosing and plasma collected over 72 h. [14C]-BaPeq PK analysis gave plasma Tmax and Cmax values of 1.25 h and 29-82 fg/mL, respectively. PK parameters were assessed by non- compartment and compartment models. Intervals between dosing ranged from 20 to 420 days and had little impact on intra-individual variation. DNA, extracted from peripheral blood mononuclear cells (PBMCs) of 4 volunteers, showed measurable levels (LOD ~ 0.5 adducts/1011 nucleotides) in two individuals 2-3 h post-dose, approximately three orders of magnitude lower than smokers or occupationally-exposed individuals. Little or no DNA binding was detectable at 48-72 h. In volunteers the allelic variants CYP1B1*1/*⁎1, *1/*3 or *3/*3 and GSTM1*0/0 or *1 had no impact on [14C]-BaPeq PK or DNA adduction with this very limited sample. Plasma metabolites over 72 h from two individuals (one CYP1B1*1/*1 and one CYP1B1*3/*3) were analyzed by UPLC-AMS. In both individuals, parent [14C]-BaP was a minor constituent even at the earliest time points and metabolite profiles markedly distinct. AMS, coupled with UPLC, could be used in humans to enhance the accuracy of pharmacokinetics, toxicokinetics and risk assessment of environmental carcinogens.

Ion Mobility Spectrometry and the Omics: Distinguishing Isomers, Molecular Classes and Contaminant Ions in Complex Samples.

Ion mobility spectrometry (IMS) is a widely used analytical technique providing rapid gas phase separations. IMS alone is useful, but its coupling with mass spectrometry (IMS-MS) and various front-end separation techniques has greatly increased the molecular information achievable from different omic analyses. IMS-MS analyses are specifically gaining attention for improving metabolomic, lipidomic, glycomic, proteomic and exposomic analyses by increasing measurement sensitivity (e.g. S/N ratio), reducing the detection limit, and amplifying peak capacity. Numerous studies including national security-related analyses, disease screenings and environmental evaluations are illustrating that IMS-MS is able to extract information not possible with MS alone. Furthermore, IMS-MS has shown great utility in salvaging molecular information for low abundance molecules of interest when high concentration contaminant ions are present in the sample by reducing detector suppression. This review highlights how IMS-MS is currently being used in omic analyses to distinguish structurally similar molecules, isomers, molecular classes and contaminant ions.

Development of an environmental health tool linking chemical exposures, physical location and lung function.

BACKGROUND: A challenge in environmental health research is collecting robust data sets to facilitate comparisons between personal chemical exposures, the environment and health outcomes. To address this challenge, the Exposure, Location and lung Function (ELF) tool was designed in collaboration with communities that share environmental health concerns. These concerns centered on respiratory health and ambient air quality. The ELF collects exposure to polycyclic aromatic hydrocarbons (PAHs), given their association with diminished lung function. Here, we describe the ELF as a novel environmental health assessment tool. METHODS: The ELF tool collects chemical exposure for 62 PAHs using passive sampling silicone wristbands, geospatial location data and respiratory lung function measures using a paired hand-held spirometer. The ELF was tested by 10 individuals with mild to moderate asthma for 7 days. Participants wore a wristband each day to collect PAH exposure, carried a cell phone, and performed spirometry daily to collect respiratory health measures. Location data was gathered using the geospatial positioning system technology in an Android cell-phone. RESULTS: We detected and quantified 31 PAHs across the study population. PAH exposure data showed spatial and temporal sensitivity within and between participants. Location data was used with existing datasets such as the Toxics Release Inventory and the National Oceanic and Atmospheric Administration (NOAA) Hazard Mapping System. Respiratory health outcomes were validated using criteria from the American Thoracic Society with 94% of participant data meeting standards. Finally, the ELF was used with a high degree of compliance (> 90%) by community members. CONCLUSIONS: The ELF is a novel environmental health assessment tool that allows for personal data collection spanning chemical exposures, location and lung function measures as well as self-reported information.

Coupling Genome-wide Transcriptomics and Developmental Toxicity Profiles in Zebrafish to Characterize Polycyclic Aromatic Hydrocarbon (PAH) Hazard.

Polycyclic Aromatic Hydrocarbons (PAHs) are diverse environmental pollutants associated with adverse human health effects. Many studies focus on the carcinogenic effects of a limited number of PAHs and there is an increasing need to understand mechanisms of developmental toxicity of more varied yet environmentally relevant PAHs. A previous study characterized the developmental toxicity of 123 PAHs in zebrafish. Based on phenotypic responses ranging from complete inactivity to acute mortality, we classified these PAHs into eight bins, selected 16 representative PAHs, and exposed developing zebrafish to the concentration of each PAH that induced 80% phenotypic effect. We conducted RNA sequencing at 48 h post fertilization to identify gene expression changes as a result of PAH exposure. Using the Context Likelihood of Relatedness algorithm, we inferred a network that links the PAHs based on coordinated gene responses to PAH exposure. The 16 PAHs formed two broad clusters: Cluster A was transcriptionally more similar to the controls, while Cluster B consisted of PAHs that were generally more developmentally toxic, significantly elevated cyp1a transcript levels, and induced Ahr2-dependent Cyp1a protein expression in the skin confirmed by gene-silencing studies. We found that cyp1a transcript levels were associated with transcriptomic response, but not with PAH developmental toxicity. While all cluster B PAHs predominantly activated Ahr2, they also each enriched unique pathways like ion transport signaling, which likely points to differing molecular events between the PAHs downstream of Ahr2. Thus, using a systems biology approach, we have begun to evaluate, classify, and define mechanisms of PAH toxicity.

Systematic reviews and meta-analyses of human and animal evidence of prenatal diethylhexyl phthalate exposure and changes in male anogenital distance.

Male reproductive alterations found in animals and humans following in utero phthalate exposure include decreased anogenital distance (AGD) and other reproductive-tract malformations. The aim of this investigation was to conduct systematic reviews of human and animal evidence of the effect of in utero exposure to diethylhexyl phthalate (DEHP) on anogenital distance (AGD) in males. PubMed, Embase, and Toxline were searched for relevant human and experimental animal studies on August 15, 2016. Search results were screened for relevance, and studies that met the inclusion criteria were evaluated for quality and data extracted for analysis. Confidence in the human and animal bodies of evidence was assessed and hazard conclusions reached by integrating evidence streams. The search yielded 6 relevant human studies and 19 animal studies. Meta-analysis of 5 human observational prospective cohort studies showed that increased maternal urinary concentrations of DEHP metabolites were associated with decreased AGD in boys (-4.07 [CI, -6.49 to -1.66] % decrease per log10 rise in DEHP metabolites). Meta-analysis and meta-regression of the 19 experimental animal studies found reduced AGD with DEHP treatment, with a dose-response gradient, and with heterogeneity explained by species and strain. There is a moderate level of evidence from human investigations and a high level of data from animal studies that in utero exposure to DEHP decreases AGD. Based upon the available human and animal evidence, and consideration of mechanistic data, DEHP is presumed to be a reproductive hazard to humans on the basis of effects on AGD.

Polybrominated diphenyl ether (PBDE) neurotoxicity: a systematic review and meta-analysis of animal evidence.

A recent systematic review (SR) and meta-analysis of human studies found an association between prenatal serum polybrominated diphenyl ethers (PBDE) concentrations and a decrease in the IQ of children. A SR of experimental developmental animal PBDE-mediated neurotoxicity studies was performed in the present study. Outcomes assessed included measures related to learning, memory, and attention, which parallel the intelligence-related outcomes evaluated in the human studies SR. PubMed, Embase, and Toxline were searched for relevant experimental non-human mammalian studies. Evaluation of risk of bias (RoB) and overall body of evidence followed guidance developed by the National Toxicology Program. Animal studies using varying designs and outcomes were available for BDEs 47, 99, 153, 203, 206, and 209 and the technical mixture DE-71. Study reporting of methods and results was often incomplete leading to concerns regarding RoB. A meta-analysis of 6 Morris water maze studies showed evidence of a significant increase in last trial latency (effect size of 25.8 [CI, 20.3 to 31.2]) in PBDE-exposed animals with low heterogeneity. For most endpoints, there were unexplained inconsistencies across studies and no consistent evidence of a dose-response relationship. There is a "moderate" level of evidence that exposure to BDEs 47, 99, and 209 affects learning. For other PBDEs and other endpoints, the level of evidence was "low" or "very low". The meta-analysis led to stronger conclusions than that based upon a qualitative review of the evidence. The SR also identified RoB concerns that might be remedied by better study reporting.

Silicone wristbands compared with traditional polycyclic aromatic hydrocarbon exposure assessment methods.

Currently there is a lack of inexpensive, easy-to-use technology to evaluate human exposure to environmental chemicals, including polycyclic aromatic hydrocarbons (PAHs). This is the first study in which silicone wristbands were deployed alongside two traditional personal PAH exposure assessment methods: active air monitoring with samplers (i.e., polyurethane foam (PUF) and filter) housed in backpacks, and biological sampling with urine. We demonstrate that wristbands worn for 48 h in a non-occupational setting recover semivolatile PAHs, and we compare levels of PAHs in wristbands to PAHs in PUFs-filters and to hydroxy-PAH (OH-PAH) biomarkers in urine. We deployed all samplers simultaneously for 48 h on 22 pregnant women in an established urban birth cohort. Each woman provided one spot urine sample at the end of the 48-h period. Wristbands recovered PAHs with similar detection frequencies to PUFs-filters. Of the 62 PAHs tested for in the 22 wristbands, 51 PAHs were detected in at least one wristband. In this cohort of pregnant women, we found more significant correlations between OH-PAHs and PAHs in wristbands than between OH-PAHs and PAHs in PUFs-filters. Only two comparisons between PAHs in PUFs-filters and OH-PAHs correlated significantly (rs = 0.53 and p = 0.01; rs = 0.44 and p = 0.04), whereas six comparisons between PAHs in wristbands and OH-PAHs correlated significantly (rs = 0.44 to 0.76 and p = 0.04 to <0.0001). These results support the utility of wristbands as a biologically relevant exposure assessment tool which can be easily integrated into environmental health studies. Graphical abstract PAHs detected in samples collected from urban pregnant women.

Dibenzo[def,p]chrysene transplacental carcinogenesis in wild-type, Cyp1b1 knockout, and CYP1B1 humanized mice.

The cytochrome P450 (CYP) 1 family is active toward numerous environmental pollutants, including polycyclic aromatic hydrocarbons (PAHs). Utilizing a mouse model, null for Cyp1b1 and expressing human CYP1B1, we tested the hypothesis that hCYP1B1 is important for dibenzo[def,p]chrysene (DBC) transplacental carcinogenesis. Wild-type mCyp1b1, transgenic hCYP1B1 (mCyp1b1 null background), and mCyp1b1 null mice were assessed. Each litter had an equal number of siblings with Ahrb-1/d and Ahrd/d alleles. Pregnant mice were dosed (gavage) on gestation day 17 with 6.5 or 12 mg/kg of DBC or corn oil. At 10 months of age, mortality, general health, lymphoid disease and lung tumor incidence, and multiplicity were assessed. hCYP1B1 genotype did not impact lung tumor multiplicity, but tended to enhance incidence compared to Cyp1b1 wild-type mice (P = 0.07). As with Cyp1b1 in wild-type mice, constitutive hCYP1B1 protein is non-detectable in liver but was induced with 2,3,7,8-tetrachlorodibenzo-p-dioxin. Wild-type mice were 59% more likely to succumb to T-cell Acute Lymphoblastic Leukemia (T-ALL). Unlike an earlier examination of the Ahr genotype in this model (Yu et al., Cancer Res, 2006;66:755-762), but in agreement with a more recent study (Shorey et al., Toxicol Appl Pharmacol, 2013;270:60-69), this genotype was not associated with lung tumor incidence, multiplicity, or mortality. Sex was not significant with respect to lung tumor incidence or mortality but males exhibited significantly greater multiplicity. Lung tumor incidence was greater in mCyp1b1 nulls compared to wild-type mice. To our knowledge, this is the first application of a humanized mouse model in transplacental carcinogenesis. © 2016 Wiley Periodicals, Inc.