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.

A compendium of multi-omics data illuminating host responses to lethal human virus infections

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.

Predicting personal PAH exposure using high dimensional questionnaire and wristband data

BACKGROUND

Polycyclic aromatic hydrocarbons (PAHs) are a class of pervasive environmental pollutants with a variety of known health effects. While significant work has been completed to estimate personal exposure to PAHs, less has been done to identify sources of these exposures. Comprehensive characterization of reported sources of personal PAH exposure is a critical step to more easily identify individuals at risk of high levels of exposure and for developing targeted interventions based on source of exposure.

OBJECTIVE

In this study, we leverage data from a New York (NY)-based birth cohort to identify personal characteristics or behaviors associated with personal PAH exposure and develop models for the prediction of PAH exposure.

METHODS

We quantified 61 PAHs measured using silicone wristband samplers in association with 75 questionnaire variables from 177 pregnant individuals. We evaluated univariate associations between each compound and questionnaire variable, conducted regression tree analysis for each PAH compound and completed a principal component analysis of for each participant's entire PAH exposure profile to determine the predictors of PAH levels.

RESULTS

Regression tree analyses of individual compounds and exposure mixture identified income, time spent outdoors, maternal age, country of birth, transportation type, and season as the variables most frequently predictive of exposure.

Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration

Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.

Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration

Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.

3' RNA-seq is superior to standard RNA-seq in cases of sparse data but inferior at identifying toxicity pathways in a model organism

Introduction: The application of RNA-sequencing has led to numerous breakthroughs related to investigating gene expression levels in complex biological systems. Among these are knowledge of how organisms, such as the vertebrate model organism zebrafish (Danio rerio), respond to toxicant exposure. Recently, the development of 3' RNA-seq has allowed for the determination of gene expression levels with a fraction of the required reads compared to standard RNA-seq. While 3' RNA-seq has many advantages, a comparison to standard RNA-seq has not been performed in the context of whole organism toxicity and sparse data. Methods and results: Here, we examined samples from zebrafish exposed to perfluorobutane sulfonamide (FBSA) with either 3' or standard RNA-seq to determine the advantages of each with regards to the identification of functionally enriched pathways. We found that 3' and standard RNA-seq showed specific advantages when focusing on annotated or unannotated regions of the genome. We also found that standard RNA-seq identified more differentially expressed genes (DEGs), but that this advantage disappeared under conditions of sparse data. We also found that standard RNA-seq had a significant advantage in identifying functionally enriched pathways via analysis of DEG lists but that this advantage was minimal when identifying pathways via gene set enrichment analysis of all genes. Conclusions: These results show that each approach has experimental conditions where they may be advantageous. Our observations can help guide others in the choice of 3' RNA-seq vs standard RNA sequencing to query gene expression levels in a range of biological systems.

Metatranscriptomics analysis reveals a novel transcriptional and translational landscape during Middle East respiratory syndrome coronavirus infection

Among all RNA viruses, coronavirus RNA transcription is the most complex and involves a process termed "discontinuous transcription" that results in the production of a set of 3'-nested, co-terminal genomic and subgenomic RNAs during infection. While the expression of the classic canonical set of subgenomic RNAs depends on the recognition of a 6- to 7-nt transcription regulatory core sequence (TRS), here, we use deep sequence and metagenomics analysis strategies and show that the coronavirus transcriptome is even more vast and more complex than previously appreciated and involves the production of leader-containing transcripts that have canonical and noncanonical leader-body junctions. Moreover, by ribosome protection and proteomics analyses, we show that both positive- and negative-sense transcripts are translationally active. The data support the hypothesis that the coronavirus proteome is much vaster than previously noted in the literature.

The Superfund Research Program Analytics Portal: linking environmental chemical exposure to biological phenotypes

The OSU/PNNL Superfund Research Program (SRP) represents a longstanding collaboration to quantify Polycyclic Aromatic Hydrocarbons (PAHs) at various superfund sites in the Pacific Northwest and assess their potential impact on human health. To link the chemical measurements to biological activity, we describe the use of the zebrafish as a high-throughput developmental toxicity model that provides quantitative measurements of the exposure to chemicals. Toward this end, we have linked over 150 PAHs found at Superfund sites to the effect of these same chemicals in zebrafish, creating a rich dataset that links environmental exposure to biological response. To quantify this response, we have implemented a dose-response modelling pipeline to calculate benchmark dose parameters which enable potency comparison across over 500 chemicals and 12 of the phenotypes measured in zebrafish. We provide a rich dataset for download and analysis as well as a web portal that provides public access to this dataset via an interactive web site designed to support exploration and re-use of these data by the scientific community at http://srp.pnnl.gov .

Multi-omics of NET formation and correlations with CNDP1, PSPB, and L-cystine levels in severe and mild COVID-19 infections

The detailed mechanisms of COVID-19 infection pathology remain poorly understood. To improve our understanding of SARS-CoV-2 pathology, we performed a multi-omics and correlative analysis of an immunologically naïve SARS-CoV-2 clinical cohort from blood plasma of uninfected controls, mild, and severe infections. Consistent with previous observations, severe patient populations showed an elevation of pulmonary surfactant levels. Intriguingly, mild patients showed a statistically significant elevation in the carnosine dipeptidase modifying enzyme (CNDP1). Mild and severe patient populations showed a strong elevation in the metabolite L-cystine (oxidized form of the amino acid cysteine) and enzymes with roles in glutathione metabolism. Neutrophil extracellular traps (NETs) were observed in both mild and severe populations, and NET formation was higher in severe vs. mild samples. Our correlative analysis suggests a potential protective role for CNDP1 in suppressing PSPB release from the pulmonary space whereas NET formation correlates with increased PSPB levels and disease severity. In our discussion we put forward a possible model where NET formation drives pulmonary occlusions and CNDP1 promotes antioxidation, pleiotropic immune responses, and vasodilation by accelerating histamine synthesis.

Coupling Environmental Whole Mixture Toxicity Screening with Unbiased RNA-Seq Reveals Site-Specific Biological Responses in Zebrafish

Passive sampling device (PSD) extracts paired with developmental toxicity assays in Danio Rerio (zebrafish) are excellent sensors for whole mixture toxicity associated with the bioavailable non-polar organics at environmental sites. We expand this concept by incorporating RNA-Seq in 48-h post fertilization zebrafish statically exposed to PSD extracts from two Portland Harbor Superfund Site locations: river mile 6.5W (RM 6.5W) and river mile 7W (RM 7W). RM 6.5W contained higher concentrations of polycyclic aromatic hydrocarbons (PAHs), but the diagnostic ratios of both extracts indicated similar PAH sourcing and composition. Developmental screens determined RM 6.5W to be more toxic with the most sensitive endpoint being a "wavy" notochord malformation. Differential gene expression from exposure to both extracts was largely parallel, although more pronounced for RM 6.5W. When compared to the gene expression associated with individual chemical exposures, PSD extracts produced some gene signatures parallel to PAHs but were more closely matched by oxygenated-PAHs. Additionally, differential expression, reminiscent of the wavy notochord phenotype, was not accounted for by either class of chemical, indicating the potential of other contaminants driving mixture toxicity. These techniques offer a compelling method for non-targeted hazard characterization of whole mixtures in an in vivo vertebrate system without requiring complete chemical characterization.

Missing data in multi-omics integration: Recent advances through artificial intelligence

Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.

Concentration-response gene expression analysis in zebrafish reveals phenotypically-anchored transcriptional responses to retene

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental contaminants and are associated with human disease. Canonically, many PAHs induce toxicity via activation of the aryl hydrocarbon receptor (AHR) pathway. While the interaction between PAHs and the AHR is well-established, understanding which AHR-regulated transcriptional effects directly result in observable phenotypes and which are adaptive or benign is important to better understand PAH toxicity. Retene is a frequently detected PAH in environmental sampling and has been associated with AHR2-dependent developmental toxicity in zebrafish, though its mechanism of toxicity has not been fully elucidated. To interrogate transcriptional changes causally associated with retene toxicity, we conducted whole-animal RNA sequencing at 48 h post-fertilization after exposure to eight retene concentrations. We aimed to identify the most sensitive transcriptomic responses and to determine whether this approach could uncover gene sets uniquely differentially expressed at concentrations which induce a phenotype. We identified a concentration-response relationship for differential gene expression in both number of differentially expressed genes (DEGs) and magnitude of expression change. Elevated expression of cyp1a at retene concentrations below the threshold for teratogenicity suggested that while cyp1a expression is a sensitive biomarker of AHR activation, it may be too sensitive to serve as a biomarker of teratogenicity. Genes differentially expressed at only non-teratogenic concentrations were enriched for transforming growth factor-β (TGF-β) signaling pathway disruption while DEGs identified at only teratogenic concentrations were significantly enriched for response to xenobiotic stimulus and reduction-oxidation reaction activity. DEGs which spanned both non-teratogenic and teratogenic concentrations showed similar disrupted biological processes to those unique to teratogenic concentrations, indicating these processes were disrupted at low exposure concentrations. Gene co-expression network analysis identified several gene modules, including those associated with PAHs and AHR2 activation. One, Module 7, was strongly enriched for AHR2-associated genes and contained the strongest responses to retene. Benchmark concentration (BMC) of Module seven genes identified a median BMC of 7.5 µM, nearly the highest retene concentration with no associated teratogenicity, supporting the hypothesis that Module seven genes are largely responsible for retene toxicity.

Impact of acute temperature and air pollution exposures on adult lung function: A panel study of asthmatics

Background

Individuals with respiratory conditions, such as asthma, are particularly susceptible to adverse health effects associated with higher levels of ambient air pollution and temperature. This study evaluates whether hourly levels of fine particulate matter (PM_2.5) and dry bulb globe temperature (DBGT) are associated with the lung function of adult participants with asthma.

Methods and findings

Global positioning system (GPS) location, respiratory function (measured as forced expiratory volume at 1 second (FEV₁)), and self-reports of asthma medication usage and symptoms were collected as part of the Exposure, Location, and Lung Function (ELF) study. Hourly ambient PM_2.5 and DBGT exposures were estimated by integrating air quality and temperature public records with time-activity patterns using GPS coordinates for each participant (n = 35). The relationships between acute PM_2.5, DBGT, rescue bronchodilator use, and lung function collected in one week periods and over two seasons (summer/winter) were analyzed by multivariate regression, using different exposure time frames.

In separate models, increasing levels in PM_2.5, but not DBGT, were associated with rescue bronchodilator use. Conversely DBGT, but not PM_2.5, had a significant association with FEV₁. When DBGT and PM_2.5 exposures were placed in the same model, the strongest association between cumulative PM_2.5 exposures and the use of rescue bronchodilator was identified at the 0–24 hours (OR = 1.030; 95% CI = 1.012–1.049; p-value = 0.001) and 0–48 hours (OR = 1.030; 95% CI = 1.013–1.057; p-value = 0.001) prior to lung function measure. Conversely, DBGT exposure at 0 hours (β = 3.257; SE = 0.879; p-value>0.001) and 0–6 hours (β = 2.885; SE = 0.903; p-value = 0.001) hours before a reading were associated with FEV₁. No significant interactions between DBGT and PM_2.5 were observed for rescue bronchodilator use or FEV₁.

Conclusions

Short-term increases in PM_2.5 were associated with increased rescue bronchodilator use, while DBGT was associated with higher lung function (i.e. FEV₁). Further studies are needed to continue to elucidate the mechanisms of acute exposure to PM_2.5 and DBGT on lung function in asthmatics.

Evaluating predictive relationships between wristbands and urine for assessment of personal PAH exposure

During events like the COVID-19 pandemic or a disaster, researchers may need to switch from collecting biological samples to personal exposure samplers that are easy and safe to transport and wear, such as silicone wristbands. Previous studies have demonstrated significant correlations between urine biomarker concentrations and chemical levels in wristbands. We build upon those studies and use a novel combination of descriptive statistics and supervised statistical learning to evaluate the relationship between polycyclic aromatic hydrocarbon (PAH) concentrations in silicone wristbands and hydroxy-PAH (OH-PAH) concentrations in urine. In New York City, 109 participants in a longitudinal birth cohort wore one wristband for 48 h and provided a spot urine sample at the end of the 48-hour period during their third trimester of pregnancy. We compared four PAHs with the corresponding seven OH-PAHs using descriptive statistics, a linear regression model, and a linear discriminant analysis model. Five of the seven PAH and OH-PAH pairs had significant correlations (Pearson's r = 0.35-0.64, p ≤ 0.003) and significant chi-square tests of independence for exposure categories (p ≤ 0.009). For these five comparisons, the observed PAH or OH-PAH concentration could predict the other concentration within a factor of 1.47 for 50-80% of the measurements (depending on the pair). Prediction accuracies for high exposure categories were at least 1.5 times higher compared to accuracies based on random chance. These results demonstrate that wristbands and urine provide similar PAH exposure assessment information, which is critical for environmental health researchers looking for the flexibility to switch between biological sample and wristband collection.

The Ahr2-Dependent wfikkn1 Gene Influences Zebrafish Transcriptome, Proteome, and Behavior

The aryl hydrocarbon receptor (AHR) is required for vertebrate development and is also activated by exogenous chemicals, including polycyclic aromatic hydrocarbons (PAHs) and TCDD. AHR activation is well-understood, but roles of downstream molecular signaling events are largely unknown. From previous transcriptomics in 48-hours post fertilization (hpf) zebrafish exposed to several PAHs and TCDD, we found wfikkn1 was highly co-expressed with cyp1a (marker for AHR activation). Thus, we hypothesized wfikkn1's role in AHR signaling, and showed that wfikkn1 expression was Ahr2 (zebrafish ortholog of human AHR)-dependent in developing zebrafish exposed to TCDD. To functionally characterize wfikkn1, we made a CRISPR-Cas9 mutant line with a 16-bp deletion in wfikkn1's exon, and exposed wildtype and mutants to DMSO or TCDD. 48-hpf mRNA sequencing revealed over 700 genes that were differentially expressed (p < 0.05, log2FC > 1) between each pair of treatment combinations, suggesting an important role for wfikkn1 in altering both the 48-hpf transcriptome and TCDD-induced expression changes. Mass spectrometry-based proteomics of 48-hpf wildtype and mutants revealed 325 significant differentially expressed proteins. Functional enrichment demonstrated wfikkn1 was involved in skeletal muscle development and played a role in neurological pathways after TCDD exposure. Mutant zebrafish appeared morphologically normal but had significant behavior deficiencies at all life stages, and absence of Wfikkn1 did not significantly alter TCDD-induced behavior effects at all life stages. In conclusion, wfikkn1 did not appear to be significantly involved in TCDD's overt toxicity but is likely a necessary functional member of the AHR signaling cascade.

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.

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.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04197-2.

A resource of lipidomics and metabolomics data from individuals with undiagnosed diseases

Every year individuals experience symptoms that remain undiagnosed by healthcare providers. In the United States, these rare diseases are defined as a condition that affects fewer than 200,000 individuals. However, there are an estimated 7000 rare diseases, and there are an estimated 25-30 million Americans in total (7.6-9.2% of the population as of 2018) affected by such disorders. The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Mass spectrometry-based metabolomics and lipidomics analyses could advance the collective understanding of individual symptoms and advance diagnoses for individuals with heretofore undiagnosed disease. Here, we report the mass spectrometry-based metabolomics and lipidomics analyses of blood plasma, urine, and cerebrospinal fluid from 148 patients within the UDN and their families, as well as from a reference population of over 100 individuals with no known metabolic diseases. The raw and processed data are available to the research community so that they might be useful in the diagnoses of current or future patients suffering from undiagnosed disorders.

CATMoS: Collaborative Acute Toxicity Modeling Suite

BACKGROUND

Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals.

OBJECTIVES

The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg).

METHODS

An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches.

RESULTS

The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results.

DISCUSSION

CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

The multi-dimensional embryonic zebrafish platform predicts flame retardant bioactivity

Flame retardant chemicals (FRCs) commonly added to many consumer products present a human exposure burden associated with adverse health effects. Under pressure from consumers, FRC manufacturers have adopted some purportedly safer replacements for first-generation brominated diphenyl ethers (BDEs). In contrast, second and third-generation organophosphates and other alternative chemistries have limited bioactivity data available to estimate their hazard potential. In order to evaluate the toxicity of existing and potential replacement FRCs, we need efficient screening methods. We built a 61-FRC library in which we systemically assessed developmental toxicity and potential neurotoxicity effects in the embryonic zebrafish model. Data were compared to publicly available data generated in a battery of cell-based in vitro assays from ToxCast, Tox21, and other alternative models. Of the 61 FRCs, 19 of 45 that were tested in the ToxCast assays were bioactive in our zebrafish model. The zebrafish assays detected bioactivity for 10 of the 12 previously classified developmental neurotoxic FRCs. Developmental zebrafish were sufficiently sensitive at detecting FRC structure-bioactivity impacts that we were able to build a classification model using 13 physicochemical properties and 3 embryonic zebrafish assays that achieved a balanced accuracy of 91.7%. This work illustrates the power of a multi-dimensional in vivo platform to expand our ability to predict the hazard potential of new compounds based on structural relatedness, ultimately leading to reliable toxicity predictions based on chemical structure.

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.

The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections

Despite high sequence similarity between pandemic and seasonal influenza viruses, there is extreme variation in host pathogenicity from one viral strain to the next. Identifying the underlying mechanisms of variability in pathogenicity is a critical task for understanding influenza virus infection and effective management of highly pathogenic influenza virus disease. We applied a network-based modeling approach to identify critical functions related to influenza virus pathogenicity using large transcriptomic and proteomic datasets from mice infected with six influenza virus strains or mutants. Our analysis revealed two pathogenicity-related gene expression clusters; these results were corroborated by matching proteomics data. We also identified parallel downstream processes that were altered during influenza pathogenesis. We found that network bottlenecks (nodes that bridge different network regions) were highly enriched in pathogenicity-related genes, while network hubs (highly connected network nodes) were significantly depleted in these genes. We confirmed that this trend persisted in a distinct virus: Severe Acute Respiratory Syndrome Coronavirus (SARS). The role of epidermal growth factor receptor (EGFR) in influenza pathogenesis, one of the bottleneck regulators with corroborating signals across transcript and protein expression data, was tested and validated in additional mouse infection experiments. We demonstrate that EGFR is important during influenza infection, but the role it plays changes for lethal versus non-lethal infections. Our results show that by using association networks, bottleneck genes that lack hub characteristics can be used to predict a gene's involvement in influenza virus pathogenicity. We also demonstrate the utility of employing multiple network approaches for analyzing host response data from viral infections.

Time-dependent behavioral data from zebrafish reveals novel signatures of chemical toxicity using point of departure analysis

High-content imaging of larval zebrafish behavior can be used as a screening approach to rapidly evaluate the relative potential for chemicals to cause toxicity. However, most statistical methods applied to these data transform movement values to incidence-based "hits" and calculate lowest effect levels (LELs), which loses individual fish resolution of behavior and defies hazard ranking due to reliance on applied dose levels. We developed a parallelizable workflow to calculate benchmark dose (BMD) values from dynamic, high-content zebrafish behavior data that scales for high-throughput chemical screening. To capture the zebrafish movement response from light to dark stimulus, we summarized time-dependent data using both area under the curve and the immediate change at the transition point into two novel metrics that characterized abnormal behavior as a function of chemical concentration. The BMD workflow was applied to calculate BMD₁₀ values of 1,060 ToxCast chemicals for 24 zebrafish endpoints, including behavior, mortality and morphology. The BMD₁₀ values provided better precision and separation than LELs for clustering chemicals since they were derived from models that best-fit their concentration-response curves. Analysis of BMD₁₀ values revealed behavioral signatures as the most sensitive endpoints. High concordance in chemical activity was observed between ToxCast in vitro data and zebrafish in vivo behavioral data, however ToxPi analysis indicated that rankings based on in vitro data were not a reliable predictor of in vivo rankings for lower potency chemicals. This analysis method will enable the use of high-content zebrafish behavioral screening data for BMD analysis in toxicological hazard assessment.

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.

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.

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/.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2805-6) contains supplementary material, which is available to authorized users.

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.

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.

Discovery of common chemical exposures across three continents using silicone wristbands

To assess differences and trends in personal chemical exposure, volunteers from 14 communities in Africa (Senegal, South Africa), North America (United States (U.S.)) and South America (Peru) wore 262 silicone wristbands. We analysed wristband extracts for 1530 unique chemicals, resulting in 400 860 chemical data points. The number of chemical detections ranged from 4 to 43 per wristband, with 191 different chemicals detected, and 1339 chemicals were not detected in any wristband. No two wristbands had identical chemical detections. We detected 13 potential endocrine disrupting chemicals in over 50% of all wristbands and found 36 chemicals in common between chemicals detected in three geographical wristband groups (Africa, North America and South America). U.S. children (less than or equal to 11 years) had the highest percentage of flame retardant detections compared with all other participants. Wristbands worn in Texas post-Hurricane Harvey had the highest mean number of chemical detections (28) compared with other study locations (10-25). Consumer product-related chemicals and phthalates were a high percentage of chemical detections across all study locations (36-53% and 18-42%, respectively). Chemical exposures varied among individuals; however, many individuals were exposed to similar chemical mixtures. Our exploratory investigation uncovered personal chemical exposure trends that can help prioritize certain mixtures and chemical classes for future studies.

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.

Expanding the Spectrum of BAF-Related Disorders: De Novo Variants in SMARCC2 Cause a Syndrome with Intellectual Disability and Developmental Delay

SMARCC2 (BAF170) is one of the invariable core subunits of the ATP-dependent chromatin remodeling BAF (BRG1-associated factor) complex and plays a crucial role in embryogenesis and corticogenesis. Pathogenic variants in genes encoding other components of the BAF complex have been associated with intellectual disability syndromes. Despite its significant biological role, variants in SMARCC2 have not been directly associated with human disease previously. Using whole-exome sequencing and a web-based gene-matching program, we identified 15 individuals with variable degrees of neurodevelopmental delay and growth retardation harboring one of 13 heterozygous variants in SMARCC2, most of them novel and proven de novo. The clinical presentation overlaps with intellectual disability syndromes associated with other BAF subunits, such as Coffin-Siris and Nicolaides-Baraitser syndromes and includes prominent speech impairment, hypotonia, feeding difficulties, behavioral abnormalities, and dysmorphic features such as hypertrichosis, thick eyebrows, thin upper lip vermilion, and upturned nose. Nine out of the fifteen individuals harbor variants in the highly conserved SMARCC2 DNA-interacting domains (SANT and SWIRM) and present with a more severe phenotype. Two of these individuals present cardiac abnormalities. Transcriptomic analysis of fibroblasts from affected individuals highlights a group of differentially expressed genes with possible roles in regulation of neuronal development and function, namely H19, SCRG1, RELN, and CACNB4. Our findings suggest a novel SMARCC2-related syndrome that overlaps with neurodevelopmental disorders associated with variants in BAF-complex subunits.

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]-BaP_eq PK analysis gave plasma T_max and C_max 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]-BaP_eq 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.

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 (r_s = 0.53 and p = 0.01; r_s = 0.44 and p = 0.04), whereas six comparisons between PAHs in wristbands and OH-PAHs correlated significantly (r_s = 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

Transcriptomic and phenotypic profiling in developing zebrafish exposed to thyroid hormone receptor agonists

There continues to be a need to develop in vivo high-throughput screening (HTS) and computational methods to screen chemicals for interaction with the estrogen, androgen, and thyroid pathways and as complements to in vitro HTS assays. This study explored the utility of an embryonic zebrafish HTS approach to identify and classify endocrine bioactivity using phenotypically-anchored transcriptome profiling. Transcriptome analysis was conducted on zebrafish embryos exposed to 25 estrogen-, androgen-, or thyroid-active chemicals at concentrations that elicited adverse malformations or mortality at 120 h post-fertilization in 80% of animals exposed. Analysis of the top 1000 significant differentially expressed transcripts and developmental toxicity profiles across all treatments identified a unique transcriptional and phenotypic signature for thyroid hormone receptor agonists. This unique signature has the potential to be used as a tiered in vivo HTS and may aid in identifying chemicals that interact with the thyroid hormone receptor.

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.

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.

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.

Multi-platform 'Omics Analysis of Human Ebola Virus Disease Pathogenesis

The pathogenesis of human Ebola virus disease (EVD) is complex. EVD is characterized by high levels of virus replication and dissemination, dysregulated immune responses, extensive virus- and host-mediated tissue damage, and disordered coagulation. To clarify how host responses contribute to EVD pathophysiology, we performed multi-platform 'omics analysis of peripheral blood mononuclear cells and plasma from EVD patients. Our results indicate that EVD molecular signatures overlap with those of sepsis, imply that pancreatic enzymes contribute to tissue damage in fatal EVD, and suggest that Ebola virus infection may induce aberrant neutrophils whose activity could explain hallmarks of fatal EVD. Moreover, integrated biomarker prediction identified putative biomarkers from different data platforms that differentiated survivors and fatalities early after infection. This work reveals insight into EVD pathogenesis, suggests an effective approach for biomarker identification, and provides an important community resource for further analysis of human EVD severity.

Implications of Bioremediation of Polycyclic Aromatic Hydrocarbon-Contaminated Soils for Human Health and Cancer Risk

Bioremediation uses soil microorganisms to degrade polycyclic aromatic hydrocarbons (PAHs) into less toxic compounds and can be performed in situ, without the need for expensive infrastructure or amendments. This review provides insights into the cancer risks associated with PAH-contaminated soils and places bioremediation outcomes in a context relevant to human health. We evaluated which bioremediation strategies were most effective for degrading PAHs and estimated the cancer risks associated with PAH-contaminated soils. Cancer risk was statistically reduced in 89% of treated soils following bioremediation, with a mean degradation of 44% across the B2 group PAHs. However, all 180 treated soils had postbioremediation cancer risk values that exceeded the U.S. Environmental Protection Agency (USEPA) health-based acceptable risk level (by at least a factor of 2), with 32% of treated soils exceeding recommended levels by greater than 2 orders of magnitude. Composting treatments were most effective at biodegrading PAHs in soils (70% average reduction compared with 28-53% for the other treatment types), which was likely due to the combined influence of the rich source of nutrients and microflora introduced with organic compost amendments. Ultimately, bioremediation strategies, in the studies reviewed, were unable to successfully remove carcinogenic PAHs from contaminated soils to concentrations below the target cancer risk levels recommended by the USEPA.

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.

MPLEx: a method for simultaneous pathogen inactivation and extraction of samples for multi-omics profiling

The continued emergence and spread of infectious agents is of great concern, and systems biology approaches to infectious disease research can advance our understanding of host-pathogen relationships and facilitate the development of new therapies and vaccines. Molecular characterization of infectious samples outside of appropriate biosafety containment can take place only subsequent to pathogen inactivation. Herein, we describe a modified Folch extraction using chloroform/methanol that facilitates the molecular characterization of infectious samples by enabling simultaneous pathogen inactivation and extraction of proteins, metabolites, and lipids for subsequent mass spectrometry-based multi-omics measurements. This single-sample metabolite, protein and lipid extraction (MPLEx) method resulted in complete inactivation of clinically important bacterial and viral pathogens with exposed lipid membranes, including Yersinia pestis, Salmonella Typhimurium, and Campylobacter jejuni in pure culture, and Yersinia pestis, Campylobacter jejuni, and West Nile, MERS-CoV, Ebola, and influenza H7N9 viruses in infection studies. In addition, >99% inactivation, which increased with solvent exposure time, was also observed for pathogens without exposed lipid membranes including community-associated methicillin-resistant Staphylococcus aureus, Clostridium difficile spores and vegetative cells, and adenovirus type 5. The overall pipeline of inactivation and subsequent proteomic, metabolomic, and lipidomic analyses was evaluated using a human epithelial lung cell line infected with wild-type and mutant influenza H7N9 viruses, thereby demonstrating that MPLEx yields biomaterial of sufficient quality for subsequent multi-omics analyses. Based on these experimental results, we believe that MPLEx will facilitate systems biology studies of infectious samples by enabling simultaneous pathogen inactivation and multi-omics measurements from a single specimen with high success for pathogens with exposed lipid membranes.

A Syndromic Neurodevelopmental Disorder Caused by De Novo Variants in EBF3

Early B cell factor 3 (EBF3) is a member of the highly evolutionarily conserved Collier/Olf/EBF (COE) family of transcription factors. Prior studies on invertebrate and vertebrate animals have shown that EBF3 homologs are essential for survival and that loss-of-function mutations are associated with a range of nervous system developmental defects, including perturbation of neuronal development and migration. Interestingly, aristaless-related homeobox (ARX), a homeobox-containing transcription factor critical for the regulation of nervous system development, transcriptionally represses EBF3 expression. However, human neurodevelopmental disorders related to EBF3 have not been reported. Here, we describe three individuals who are affected by global developmental delay, intellectual disability, and expressive speech disorder and carry de novo variants in EBF3. Associated features seen in these individuals include congenital hypotonia, structural CNS malformations, ataxia, and genitourinary abnormalities. The de novo variants affect a single conserved residue in a zinc finger motif crucial for DNA binding and are deleterious in a fly model. Our findings indicate that mutations in EBF3 cause a genetic neurodevelopmental syndrome and suggest that loss of EBF3 function might mediate a subset of neurologic phenotypes shared by ARX-related disorders, including intellectual disability, abnormal genitalia, and structural CNS malformations.

The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus

BACKGROUND

The complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ anti-immune strategies to facilitate their replication, highly pathogenic virus infections can also cause an excessive immune response that exacerbates, rather than reduces pathogenicity. To investigate this dichotomy in severe acute respiratory syndrome coronavirus (SARS-CoV), we developed a transcriptional network model of SARS-CoV infection in mice and used the model to prioritize candidate regulatory targets for further investigation.

RESULTS

We validated our predictions in 18 different knockout (KO) mouse strains, showing that network topology provides significant predictive power to identify genes that are important for viral infection. We identified a novel player in the immune response to virus infection, Kepi, an inhibitory subunit of the protein phosphatase 1 (PP1) complex, which protects against SARS-CoV pathogenesis. We also found that receptors for the proinflammatory cytokine tumor necrosis factor alpha (TNFα) promote pathogenesis, presumably through excessive inflammation.

CONCLUSIONS

The current study provides validation of network modeling approaches for identifying important players in virus infection pathogenesis, and a step forward in understanding the host response to an important infectious disease. The results presented here suggest the role of Kepi in the host response to SARS-CoV, as well as inflammatory activity driving pathogenesis through TNFα signaling in SARS-CoV infections. Though we have reported the utility of this approach in bacterial and cell culture studies previously, this is the first comprehensive study to confirm that network topology can be used to predict phenotypes in mice with experimental validation.