Gene expression profiling to identify potentially relevant disease outcomes and support human health risk assessment for carbon black nanoparticle exposure

In vitro to in vivo extrapolation modeling to facilitate the integration of transcriptomics data into genotoxicity assessment

In vitro transcriptomics holds promise for high-throughput, human-relevant data but is not yet integrated into regulatory decision-making due to the lack of standardized approaches. For genotoxicity assessment, transcriptomic biomarkers such as GENOMARK and TGx-DDI facilitate qualitative and quantitative analysis of complex in vitro transcriptomic datasets. However, advancing their use in quantitative testing requires standardized methods for deriving transcriptomic Points of Departure (tPoDs) and linking them to in vivo responses. Herein, we investigated different approaches to calculate tPoDs and applied in vitro to in vivo extrapolation to obtain administered equivalent doses (AEDs). Human HepaRG cells were exposed for 72 h to 10 known in vivo genotoxicants (glycidol, methyl methanesulfonate, nitrosodimethylamine, 4-nitroquinoline-N-oxide, aflatoxin B1, colchicine, cyclophosphamide, mitomycin C, ethyl methanesulfonate, and N-Nitroso-N-ethylurea) from the highest concentration that induces up to 50 % cytotoxicity through a range of lower concentrations. Gene expression data was generated using a customized version of the TempO-Seq® human S1500 + gene panel. The GENOMARK and TGx-DDI biomarkers produced genotoxic calls for all of these reference genotoxicants. Next, we performed benchmark concentration (BMC) modeling to generate both genotoxicity-specific biomarker (tPoDbiomarkers) and generic tPoDs (tPoD S1500+). High-throughput toxicokinetic models estimated the human AEDs for these tPoDs, which were compared with (a) previously reported genotoxicity-specific AEDs from other New Approach Methodologies, and (b) in vivo PoDs from animal studies. We found that the generic AEDs were more conservative than genotoxicity-specific biomarker AEDs. For six of the nine genotoxicants, transcriptomic AEDs were lower than the in vivo PoDs; refined kinetic models may improve predictions. Overall, in vitro transcriptomic data in HepaRG cells provide protective estimates of in vivo genotoxic concentrations, consistent with other in vitro genotoxicity testing systems.

Integration of multi-omics and benchmark dose modeling to support adverse outcome pathways

BACKGROUND

Recent advancements in omics and benchmark dose (BMD) modeling have facilitated identifying the dose required for a predetermined change in a response (e.g. gene or protein change) that can be used to establish acceptable dose levels for hazardous exposures. Adverse Outcome Pathways (AOPs) describe the causal links between toxicants and adverse effects through key events (KEs). Integrating omics data within the AOP framework quantitatively links early molecular events to later phenotypic effects. In this study, we use omic-based BMD analyses in an in vitro blood model exposed to radiation to identify point of departure (POD) values across KEs to acute myeloid leukemia (www.aopwiki.org/aop/432).

METHODS

Isolated white blood cells were cultured and X-irradiated (1 Gy/minute, 0-6 Gy). Transcriptomic and proteomic changes were assessed 24 h post-exposure. BMD modeling was applied and significantly perturbed genes/proteins and pathways were identified. Those pathways relevant to KEs outlined in AOP 432 were grouped and a POD was determined.

RESULTS

BMD modeling identified 1294 genes and 167 proteins with median BMD lower confident limit (BMD) values of 1.35 and 0.32 Gy, respectively. Pathway analysis identified biological processes related to DNA damage/repair, oxidative stress, cell cycle regulation, immune responses, and cancer development. These findings aligned with the KEs in AOP 432. The BMDL values of canonical pathways associated with these KEs were generally below 0.5 Gy with specific genes (e.g. GADD45A) displaying BMDLs <0.05 Gy.

CONCLUSIONS

This work provides insights into predictive radiation induced mechanisms and associated dose of activity that can be taken into consideration for low dose (< 0.1 Gy) risk analysis.

The interactions of subcellular organelles in pulmonary fibrosis induced by carbon black nanoparticles: a comprehensive review

Owing to the widespread use and improper emissions of carbon black nanoparticles (CBNPs), the adverse effects of CBNPs on human health have attracted much attention. In toxicological research, carbon black is frequently utilized as a negative control because of its low toxicity and poor solubility. However, recent studies have indicated that inhalation exposure to CBNPs could be a risk factor for severe and prolonged pulmonary inflammation and fibrosis. At present, the pathogenesis of pulmonary fibrosis induced by CBNPs is still not fully elucidated, but it is known that with small particle size and large surface area, CBNPs are more easily ingested by cells, leading to organelle damage and abnormal interactions between organelles. Damaged organelle and abnormal organelles interactions lead to cell structure and function disorders, which is one of the important factors in the development and occurrence of various diseases, including pulmonary fibrosis. This review offers a comprehensive analysis of organelle structure, function, and interaction mechanisms, while also summarizing the research advancements in organelles and organelle interactions in CBNPs-induced pulmonary fibrosis.

Impact of black soot emissions on public health in Niger Delta, Nigeria: understanding the severity of the problem

Rivers State, Niger Delta, Nigeria often referred to as the 'treasure bed of the nation' is the seat of crude oil production activities with the accompanying environmental degradation. The severity of the environmental pollution and contaminated air quality took a new turn for the worse in November 2016, when the residents of Port Harcourt city, Rivers State, a major oil producing State experienced for the first time, aerosol deposition of plumes of black soot. This systematic review paper is aimed at quantifying the severity of this public health challenge. Using appropriate search words, the following databases SCOPUS, PUBMED, Google Scholar, and AJOL were searched from 1990 to 2022 to enable comparative analyses of data before and after the emergence of black soot deposition. Air-related morbidities and mortalities such as cerebrospinal meningitis (CSM), chronic bronchitis, measles, pertussis, hemoptysis, cough, pulmonary tuberculosis, pneumonia, and upper respiratory tract infection (URTI), pneumonia, eye irritation, conjunctivitis, traumatic skin outgrowth, cancers, cardiovascular diseases, and child deformities were compared with levels of air pollutants and particulate matter. The results showed that Port Harcourt city's ambient air quality data were above the standard National Ambient Air Quality data and that of other regulatory agencies having higher levels of both inorganic and organic pollutants. There were significant relationships between air pollutants concentration with morbidities. These correlations were significant in the period covering 2016-2022. Consequently, it is concluded that the black soot emissions in Port Harcourt city, Nigeria has worsened the public health situation in the city.

Comparison of acute phase response in mice after inhalation and intratracheal instillation of molybdenum disulphide and tungsten particles

Inhalation studies are the gold standard for assessing the toxicity of airborne materials. They require considerable time, special equipment, and large amounts of test material. Intratracheal instillation is considered a screening and hazard assessment tool as it is simple, quick, allows control of the applied dose, and requires less test material. The particle-induced pulmonary inflammation and acute phase response in mice caused by intratracheal instillation or inhalation of molybdenum disulphide or tungsten particles were compared. End points included neutrophil numbers in bronchoalveolar lavage fluid, Saa3 mRNA levels in lung tissue and Saa1 mRNA levels in liver tissue, and SAA3 plasma protein. Acute phase response was used as a biomarker for the risk of cardiovascular disease. Intratracheal instillation of molybdenum disulphide or tungsten particles did not produce pulmonary inflammation, while molybdenum disulphide particles induced pulmonary acute phase response with both exposure methods and systemic acute phase response after intratracheal instillation. Inhalation and intratracheal instillation showed similar dose-response relationships for pulmonary and systemic acute phase response when molybdenum disulphide was expressed as dosed surface area. Both exposure methods showed similar responses for molybdenum disulphide and tungsten, suggesting that intratracheal instillation can be used for screening particle-induced acute phase response and thereby particle-induced cardiovascular disease.

Assessment of primary and inflammation-driven genotoxicity of carbon black nanoparticles in vitro and in vivo

Carbon black nanoparticles (CBNPs) have a large surface area/volume ratio and are known to generate oxidative stress and inflammation that may result in genotoxicity and cancer. Here, we evaluated the primary and inflammatory response-driven (i.e. secondary) genotoxicity of two CBNPs, Flammruss101 (FL101) and PrintexXE2B (XE2B) that differ in size and specific surface area (SSA), and cause different amounts of reactive oxygen species. Three doses (low, medium and high) of FL101 and XE2B were assessed in vitro in the lung epithelial (A549) and activated THP-1 (THP-1a) monocytic cells exposed in submerged conditions for 6 and 24 h, and in C57BL/6 mice at day 1, 28 and 90 following intratracheal instillation. In vitro, we assessed pro-inflammatory response as IL-8 and IL-1β gene expression, and in vivo, inflammation was determined as inflammatory cell infiltrates in bronchial lavage (BAL) fluid and as histological changes in lung tissue. DNA damage was quantified in vitro and in vivo as DNA strand breaks levels by the alkaline comet assay. Inflammatory responses in vitro and in vivo correlated with dosed CBNPs SSA. Both materials induced DNA damage in THP-1a (correlated with dosed mass), and only XE2B in A549 cells. Non-statistically significant increase in DNA damage in vivo was observed in BAL cells. In conclusion, this study shows dosed SSA predicted inflammation both in vivo and in vitro, whereas dosed mass predicted genotoxicity in vitro in THP-1a cells. The observed lack of correlation between CBNP surface area and genotoxicity provides little evidence of inflammation-driven genotoxicity in vivo and in vitro.

Evaluating the Influences of Confounding Variables on Benchmark Dose using a Case Study in the Field of Ionizing Radiation

Purpose A vast amount of data regarding the effects of radiation stressors on transcriptional changes has been produced over the past few decades. These data have shown remarkable consistency across platforms and experimental design, enabling increased understanding of early molecular effects of radiation exposure. However, the value of transcriptomic data in the context of risk assessment is not clear and represents a gap that is worthy of further consideration. Recently, benchmark dose (BMD) modeling has shown promise in correlating a transcriptional point of departure (POD) to that derived using phenotypic outcomes relevant to human health risk assessment. Although frequently applied in chemical toxicity evaluation, our group has recently demonstrated application within the field of radiation research. This approach allows the possibility to quantitatively compare radiation-induced gene and pathway alterations across various datasets using BMD values and derive meaningful biological effects. However, before BMD modeling can confidently be used, an understanding of the impact of confounding variables on BMD outputs is needed. Methods: To this end, BMD modeling was applied to a publicly available microarray dataset (Gene Expression Omnibus #GSE23515) that used peripheral blood ex-vivo gamma-irradiated at 0.82 Gy/min, at doses of 0, 0.1, 0.5 or 2 Gy, and assessed 6 hours post-exposure. The dataset comprised six female smokers (F-S), six female non-smokers (F-NS), six male smokers (M-S), and six male non-smokers (M-NS). Results: A combined total of 412 genes were fit to models and the BMD distribution was noted to be bi-modal across the four groups. A total of 74, 41, 62 and 62 genes were unique to the F-NS, M-NS, F-S and M-S groups. Sixty-two BMD modeled genes and nine pathways were common across all four groups. There were no differential sensitivity of responses in the robust common genes and pathways. Conclusion: For radiation-responsive genes and pathways common across the study groups, the BMD distribution of transcriptional activity was unaltered by sex and smoking status. Although further validation of the data is needed, these initial findings suggest BMD values for radiation relevant genes and pathways are robust and could be explored further in future studies.

Harmonization of transcriptomic and methylomic analysis in environmental epidemiology studies for potential application in chemical risk assessment

Recent efforts have posited the utility of transcriptomic-based approaches to understand chemical-related perturbations in the context of human health risk assessment. Epigenetic modification (e.g., DNA methylation) can influence gene expression changes and is known to occur as a molecular response to some chemical exposures. Characterization of these methylation events is critical to understand the molecular consequences of chemical exposures. In this context, a novel workflow was developed to interrogate publicly available epidemiological transcriptomic and methylomic data to identify relevant pathway level changes in response to chemical exposure, using inorganic arsenic as a case study. Gene Set Enrichment Analysis (GSEA) was used to identify causal methylation events that result in concomitant downstream transcriptional deregulation. This analysis demonstrated an unequal distribution of differentially methylated regions across the human genome. After mapping these events to known genes, significant enrichment of a subset of these pathways suggested that arsenic-mediated methylation may be both specific and non-specific. Parallel GSEA performed on matched transcriptomic samples determined that a substantially reduced subset of these pathways are enriched and that not all chemically-induced methylation results in a downstream alteration in gene expression. The resulting pathways were found to be representative of well-established molecular events known to occur in response to arsenic exposure. The harmonization of enriched transcriptional patterns with those identified from the methylomic platform promoted the characterization of plausibly causal molecular signaling events. The workflow described here enables significant gene and methylation-specific pathways to be identified from whole blood samples of individuals exposed to environmentally relevant chemical levels. As future efforts solidify specific causal relationships between these molecular events and relevant apical endpoints, this novel workflow could aid risk assessments by identifying molecular targets serving as biomarkers of hazard, informing mechanistic understanding, and characterizing dose ranges that promote relevant molecular/epigenetic signaling events occuring in response to chemical exposures.

Genetic instability of lung induced by carbon black nanoparticles is related with Plk1 signals changes

As a possible carcinogen, carbon black has threatened public health. However, the evidences are insufficient and the mechanism of carcinogenesis is still not specified. Thirty rats were randomly divided into 3 groups, namely 0, 5 and 30 mg/m3 Carbon Black nanoparticles (CBNPs) groups, respectively. Rats were treated with CBNPs by nose-only inhalation for 28 days, 6 h/day. The human bronchial epithelial (16HBE) cells were treated with 0, 50, 100 and 200 μg/mL CBNPs for 24 h. Polo-like kinase 1 (PLK1) overexpression cell line was established by pcDNA3.1-PLK1 stable transfection. Our results showed that CBNPs exposure could induce DNA damage and genetic changes as well as apoptosis in vivo and in vitro. The DNA repair ability increased after CBNPs exposure. Cell cycle process was retarded at the G2/M phases in 16HBE cells after CBNPs treatment. The PLK1, ChK2 GADD45α and XRCC1 expression levels changed in rat lung and 16HBE cells after CBNPs treatment. Compared with NC 16HBE cells, DNA damage and repair, numbers of apoptotic cells and micronucleus (MN) rates, as well as the ChK2, GADD45α, XRCC1 expression levels decreased, whereas cytokinesis block proliferation index (CBPI) and replicative index (RI) increase in PLK overexpression (PLK+/+) cells after CBNPs treatment. This study highlighted that PLK1 related with the genetic toxicity of CBNPs in vitro and in vivo. Our results provided evidences supporting reclassification of carbon black as a human possible carcinogen.

Comparative and mechanistic toxicity assessment of structure-dependent toxicity of carbon-based nanomaterials

The wide application of carbon-based nanomaterials (CNMs) has resulted in the ubiquity of CNMs in the natural environment and they potentially impose adverse consequences on ecosystems and human health. In this study, we comprehensively evaluated and compared potential toxicological effects and mechanisms of seven CNMs in three representative types (carbon blacks, graphene nanoplatelets, and fullerenes), to elucidate the correlation between their physicochemical/structural properties and toxicity. We employed a recently-developed quantitative toxicogenomics-based toxicity testing system with GFP-fused yeast reporter library targeting main cellular stress response pathways, as well as conventional phenotype-based bioassays. The results revealed that DNA damage, oxidative stress, and protein stress were the major mechanisms of action for all the CNMs at sub-cytotoxic concentration levels. The molecular toxicity nature were concentration-dependent, and they exhibited both similarity within the same structural group and distinctiveness among different CNMs, evidencing the structure-driven toxicity of CNMs. The toxic potential based on toxicogenomics molecular endpoints revealed the remarkable impact of size and structure on the toxicity. Furthermore, the phenotypic endpoints derived from conventional phenotype-based bioassays correlated with quantitative molecular endpoints derived from the toxicogenomics assay, suggesting that the selected protein biomarkers captured the main cellular effects that are associated with phenotypic adverse outcomes.

Time-Resolved Selective Electrochemical Sensing of Carbon Particles

This work demonstrated a new method for electrochemical detection of carbon black particles based on impact electrochemistry that was capable of selective detection of carbon black from the insulating oxide particles. We systematically studied the electrochemical collision events with carbon black particle suspension solution (pH 7.0 phosphate buffer) at varying carbon black concentrations using a convective condition and a gold microelectrode. We evaluated the effect of bias potential on the number and magnitude of collision spikes by changing the applied potential in chronoamperometry experiments. Our results showed that the biased potential of +0.4 V was the most suitable potential among the tested potential biases. Current blips were observed in the amperometric i-t response, and the spike numbers scaled linearly with the concentration of carbon black particles in the range of 2.5-20 μM (i.e., mass/volume concentration of 0.03 to 0.24 mg L^-1) with the lowest detection limit of 0.396 μM (i.e., mass/volume concentration of 0.00475 mg L^-1). The selective detection of carbon particles in the presence of representative poorly conductive oxide particles in our experimental conditions was achieved. The sensing mechanism of the sensitive and selective detection of carbon black particles is proposed. This work provides the basis for the development of powerful electroanalytical methods and technologies for the detection and classification of carbon particles in varying environmental conditions such as coalmines, engineered carbon particle factories, and coal power plants.

A set of six Gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays

Traditional methods for cancer risk assessment are retrospective, resource-intensive, and not feasible for the vast majority of environmental chemicals. In earlier studies, we used a set of six biomarkers to accurately identify liver tumorigens in transcript profiles derived from chemically-treated rats using either a Toxicological Priority Index (ToxPi) approach or using derived biomarker thresholds for cancer. The biomarkers consisting of 7-113 genes are used to predict the most common liver cancer molecular initiating events: genotoxicity, cytotoxicity and activation of the xenobiotic receptors AhR, CAR, ER, and PPARα. In the present study, we apply and evaluate the performance of these methods for cancer prediction in an independent rat liver study of 44 chemicals (6 h-7d exposures) examined by Affymetrix arrays. In the first approach, ToxPi ranking of biomarker scores consistently gave the highest scores to tumorigenic chemical-dose pairs; balanced accuracies for identification of liver tumorigenic chemicals were up to 89 %. The second approach used tumorigenic thresholds derived in the present study or from our earlier study that were set at the maximum value for chemical-dose exposures without detectable liver tumor outcomes. Using these thresholds, balanced accuracies were up to 90 %. Both approaches identified all tumorigenic chemicals. Almost all of the tumorigenic chemicals activated more than one MIE. We also compared biomarker responses between two types of profiling platforms (Affymetrix full-genome array, TempO-Seq 1500+ array containing ∼2600 genes) and found that the lack of the full set of biomarker genes on the 1500+ array resulted in decreased ability to identify chemicals that activate the MIEs. Overall, these results demonstrate that predictive approaches based on the 6 biomarkers could be used in short-term assays to identify chemicals and their doses that induce liver tumors, the most common endpoint in rodent bioassays.

Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment

PURPOSE

Benchmark dose (BMD) modeling is used to determine the dose of a stressor at which a predefined increase in any biological effect above background occurs (e.g. 10% increase from control values). BMD analytical tools have the capacity to model transcriptional dose-response data to derive BMDs for genes, pathways and gene ontologies. We recently demonstrated the value of this approach to support various areas of radiation research using predominately 'in-house' generated datasets.

MATERIALS AND METHODS

As a continuation of this work, transcriptomic studies of relevance to ionizing radiation were retrieved through the Gene Expression Omnibus (GEO). The datasets were compiled and filtered, then analyzed using BMDExpress. The objective was to determine the reproducibility of BMD values in relation to pathways and genes across different exposure scenarios and compare to those derived using cytogenetic endpoints. A number of graphic visualization approaches were used to determine if BMD outputs could be correlated to parameters such as dose-rate, radiation quality and cell type.

RESULTS

Curated studies were diverse and derived from experiments with varied design and intent. Despite this, common genes and pathways were identified with low and high dose thresholds. The higher BMD values were associated with immune response and cell death, while transcripts with lower BMD values were generally related to the classic DNA damage response/repair processes, centered on TP53 signaling. Analysis of datasets with relatively similar dose-ranges under comparable experimental conditions showed a bi-modal distribution with a high degree of consistency in BMD values across shared genes and pathways, particularly for those below the 25th percentile of total distribution by dose. The median BMD values were noted to be approximately 0.5 Gy for genes/pathways that comprised mode 1. Furthermore, transcriptional BMD values derived from a subset of genes using in vivo and in vitro datasets were in accord to those using cytogenetic endpoints.

CONCLUSION

Overall, the results from this work highlight the value of the BMD methodology to derive meaningful outputs that are consistent across different models, provided the studies are conducted using a similar dose-range.

Soil exposed to silver nanoparticles reveals significant changes in community structure and altered microbial transcriptional profiles

Anthropogenic activities can disrupt soil ecosystems, normally resulting in reduced soil microbial health. Regulatory agencies need to determine the effects of uncharacterized substances on soil microbial health to establish the safety of these chemicals if they end up in the environment. Previous work has focused on measuring traditional ecotoxicologial endpoints within the categories of microbial biomass, activity, and community structure/diversity. Because these tests can be labor intensive, lengthy to conduct, and cannot measure changes in individual gene functions, we wanted to establish whether metatranscriptomics could be used as a more sensitive endpoint and provide a perspective on community function that is more informative than taxonomic identification of microbes alone. We spiked a freshly collected sandy loam soil (Vulcan, Alberta, Canada) with 0, 60, 145, 347, 833, and 2000 mg kg^-1 of silver nanoparticles (AgNPs), a known antagonist of microorganisms due to its propensity for dissolution of toxic silver ions. Assessments performed in our previous work using traditional tests demonstrated the toxicity of AgNPs on soil microbial processes. We expanded this analysis with genomics-based tests by measuring changes in community taxonomic structure and function using 16S rDNA profiling and metatranscriptomics. In addition to identifying bacterial taxa affected by AgNPs, we found that genes involved in heavy metal resistance (e.g., the CzcA efflux pump) and other toxicity response pathways were highly upregulated in the presence of silver. Dose-response analysis using BMDExpress2 software successfully modeled many physiologically relevant genes responding to low concentrations of AgNPs. We found that the transcriptomic point of departure (BMD₅₀) was lower than the IC50s calculated using the traditional tests in our previous work. These results suggest that dose-response modeling of metatranscriptomic gene expression is a useful tool in soil microbial health assessment. SUMMARY: Genomics-based endpoints for the assessment of soil microbial health can be used to perform quantitative dose-response modeling, and soil-based RNAseq adds functional insights.

Carbon black nanoparticles induce pulmonary fibrosis through NLRP3 inflammasome pathway modulated by miR-96 targeted FOXO3a

Carbon black nanoparticle (CBNP) is a core constituent of air pollutants like fine particulate matter (PM2.5) as well as a common manufactural material. It was proved to pose adverse effects on lung function and even provoke pulmonary fibrosis. However, the underlying mechanisms of CBNPs-induced pulmonary fibrosis remain unclear. The present study aimed to investigate the mechanism of fibrotic effects caused by CBNPs in rat lung and human bronchial epithelial (16HBE) cells. Forty-nine male rats were randomly subjected to 7 groups, means the 14-day exposure group (30 mg/m3), the 28-day exposure groups (5 mg/m3 and 30 mg/m3), the 90-day exposure group (30 mg/m3) and their respective controls. Rats were nose-only-inhaled CBNPs. 16HBE cells were treated with 0, 50, 100 and 200 μg/mL CBNPs respectively for 24 h. Besides, Forkhead transcription factor class O (FOXO)3a and miR-96 overexpression or suppression 16HBE cells were established to reveal relative mechanisms. Our results suggested CBNPs induced pulmonary fibrosis in time- and dose-dependent manners. CBNPs induced persisting inflammation in rat lung as observed by histopathology and cytology analyses in whole lung lavage fluid (WLL). Both in vivo and in vitro, CBNPs exposure significantly increased the expression of NLRP3 inflammasome, accompanied by the increased reactive oxygen species (ROS), decreased miR-96 and increased FOXO3a expressions dose -and time-dependently. MiR-96 overexpression or FOXO3a suppression could partially rescue the fibrotic effects through inhibiting NLRP3 inflammasome. Conclusively, our research show that CBNPs-induced pulmonary fibrosis was at least partially depended on activation of NLRP3 inflammasome which modulated by miR-96 targeting FOXO3a.

N6-methyladenosine-dependent primary microRNA-126 processing activated PI3K-AKT-mTOR pathway drove the development of pulmonary fibrosis induced by nanoscale carbon black particles in rats

The pulmonary fibrosis could be caused by long-term inhalation of carbon black (CB) particles. Studies on the mechanisms of pulmonary fibrosis induced by CB are required to develop the stratagem of prevention and treatment on fibrosis. The RNA-binding protein DiGeorge syndrome critical region gene 8 (DGCR8)-dependent pri-miRNAs processing is regulated by N⁶-methyladenosine (m⁶A) modification, which targets the downstream signal pathway. However, its role in pulmonary fibrosis has not been known clearly. In the present study, rats inhaled CB at dose of 0, 5 or 30 mg/m³ for 28 days, 6 h/day, respectively. The rats inhaled CB at dose of 0 or 30 mg/m³ for 14 days, 28 days and 90 days, respectively. In vitro experiments, the normal human bronchial epithelial cell line (16HBE) was treated with CB (0, 50, 100 and 200 μg/mL) for 24 h. In vitro and vivo study, the levels of fibrosis indicators including α-SMA, vimentin, collagen-I and hydroxyproline in CB treatment groups statistically increased in dose- or time- dependent manners compared with the control. After CB treatment, PI3K-AKT-mTOR pathway was activated and regulated by miRNA-126. We found that both of m⁶A modifications of pri-miRNA-126 and its binding with DGCR8 were decreased after CB treatment, which resulted in the reduction of mature miRNA-126 accompanied by accumulation of unprocessed pri-miRNA-126. This work demonstrated that m⁶A modification of pri-miRNA-126 and its binding with DGCR8 decreases blocked miRNA-126 maturation, and then activated the PI3K/AKT/mTOR pathway, which drove the fibro genesis in the lung after CB exposure.

Short- and long-term gene expression profiles induced by inhaled TiO2 nanostructured aerosol in rat lung

The number of workers potentially exposed to nanoparticles (NPs) during industrial processes is increasing, although the toxicological properties of these compounds still need to be fully characterized. As NPs may be aerosolized during industrial processes, inhalation represents their main route of occupational exposure. Here, the short- and long-term pulmonary toxicological properties of titanium dioxide were studied, using conventional and molecular toxicological approaches. Fischer 344 rats were exposed to 10 mg/m³ of a TiO₂ nanostructured aerosol (NSA) by nose-only inhalation for 6 h/day, 5 days/week for 4 weeks. Lung samples were collected up to 180 post-exposure days. Biochemical and cytological analyses of bronchoalveolar lavage (BAL) showed a strong inflammatory response up to 3 post-exposure days, which decreased overtime. In addition, gene expression profiling revealed overexpression of genes involved in inflammation that was maintained 6 months after the end of exposure (long-term response). Genes involved in oxidative stress and vascular changes were also up-regulated. Long-term response was characterized by persistent altered expression of a number of genes up to 180 post-exposure days, despite the absence of significant histopathological changes. The physiopathological consequences of these changes are not fully understood, but they should raise concerns about the long-term pulmonary effects of inhaled biopersistent NPs such as TiO₂.

Editor's Highlight: Application of Gene Set Enrichment Analysis for Identification of Chemically Induced, Biologically Relevant Transcriptomic Networks and Potential Utilization in Human Health Risk Assessment

The rate of new chemical development in commerce combined with a paucity of toxicity data for legacy chemicals presents a unique challenge for human health risk assessment. There is a clear need to develop new technologies and incorporate novel data streams to more efficiently inform derivation of toxicity values. One avenue of exploitation lies in the field of transcriptomics and the application of gene expression analysis to characterize biological responses to chemical exposures. In this context, gene set enrichment analysis (GSEA) was employed to evaluate tissue-specific, dose-response gene expression data generated following exposure to multiple chemicals for various durations. Patterns of transcriptional enrichment were evident across time and with increasing dose, and coordinated enrichment plausibly linked to the etiology of the biological responses was observed. GSEA was able to capture both transient and sustained transcriptional enrichment events facilitating differentiation between adaptive versus longer term molecular responses. When combined with benchmark dose (BMD) modeling of gene expression data from key drivers of biological enrichment, GSEA facilitated characterization of dose ranges required for enrichment of biologically relevant molecular signaling pathways, and promoted comparison of the activation dose ranges required for individual pathways. Median transcriptional BMD values were calculated for the most sensitive enriched pathway as well as the overall median BMD value for key gene members of significantly enriched pathways, and both were observed to be good estimates of the most sensitive apical endpoint BMD value. Together, these efforts support the application of GSEA to qualitative and quantitative human health risk assessment.

The Toxicological Mechanisms of Environmental Soot (Black Carbon) and Carbon Black: Focus on Oxidative Stress and Inflammatory Pathways

The environmental soot and carbon blacks (CBs) cause many diseases in humans, but their underlying mechanisms of toxicity are still poorly understood. Both are formed after the incomplete combustion of hydrocarbons but differ in their constituents and percent carbon contents. For the first time, “Sir Percival Pott” described soot as a carcinogen, which was subsequently confirmed by many others. The existing data suggest three main types of diseases due to soot and CB exposures: cancer, respiratory diseases, and cardiovascular dysfunctions. Experimental models revealed the involvement of oxidative stress, DNA methylation, formation of DNA adducts, and Aryl hydrocarbon receptor activation as the key mechanisms of soot- and CB-induced cancers. Metals including Si, Fe, Mn, Ti, and Co in soot also contribute in the reactive oxygen species (ROS)-mediated DNA damage. Mechanistically, ROS-induced DNA damage is further enhanced by eosinophils and neutrophils via halide (Cl⁻ and Br⁻) dependent DNA adducts formation. The activation of pulmonary dendritic cells, T helper type 2 cells, and mast cells is crucial mediators in the pathology of soot- or CB-induced respiratory disease. Polyunsaturated fatty acids (PUFAs) were also found to modulate T cells functions in respiratory diseases. Particularly, telomerase reverse transcriptase was found to play the critical role in soot- and CB-induced cardiovascular dysfunctions. In this review, we propose integrated mechanisms of soot- and CB-induced toxicity emphasizing the role of inflammatory mediators and oxidative stress. We also suggest use of antioxidants and PUFAs as protective strategies against soot- and CB-induced disorders.

Network Analysis Reveals Similar Transcriptomic Responses to Intrinsic Properties of Carbon Nanomaterials in Vitro and in Vivo

Understanding the complex molecular alterations related to engineered nanomaterial (ENM) exposure is essential for carrying out toxicity assessment. Current experimental paradigms rely on both in vitro and in vivo exposure setups that often are difficult to compare, resulting in questioning the real efficacy of cell models to mimic more complex exposure scenarios at the organism level. Here, we have systematically investigated transcriptomic responses of the THP-1 macrophage cell line and lung tissues of mice, after exposure to several carbon nanomaterials (CNMs). Under the assumption that the CNM exposure related molecular alterations are mixtures of signals related to their intrinsic properties, we inferred networks of responding genes, whose expression levels are coordinately altered in response to specific CNM intrinsic properties. We observed only a minute overlap between the sets of intrinsic property-correlated genes at different exposure scenarios, suggesting specific transcriptional programs working in different exposure scenarios. However, when the effects of the CNM were investigated at the level of significantly altered molecular functions, a broader picture of substantial commonality emerged. Our results imply that in vitro exposures can efficiently recapitulate the complex molecular functions altered in vivo. In this study, altered molecular pathways in response to specific CNM intrinsic properties have been systematically characterized from transcriptomic data generated from multiple exposure setups. Our computational approach to the analysis of network response modules further revealed similarities between in vitro and in vivo exposures that could not be detected by traditional analysis of transcriptomics data. Our analytical strategy also opens a possibility to look for pathways of toxicity and understanding the molecular and cellular responses identified across predefined biological themes.

Barriers to the use of toxicogenomics data in human health risk assessment: A survey of Canadian risk assessors

Regulatory agencies worldwide need to modernize human health risk assessment (HHRA) to meet challenges of the 21st century. Toxicogenomics is at the core of this improvement. Today, however, the use of toxicogenomics data in HHRA is very limited. The purpose of this survey was to identify barriers to the application of toxicogenomics data in HHRA by human health risk assessors. An online survey targeting Canadian risk assessors gathered information on their knowledge and perception of toxicogenomics, their current and future inclusion of toxicogenomics data in HHRA, and barriers to the use of such data. Twenty-nine (29) participants completed a questionnaire after 2 months of solicitation. The results show that the application of toxicogenomics data in Canada is marginal, with 85% of respondents reporting that they never or rarely used such data. Knowledge of toxicogenomics by Canadian risk assessors is also limited: about two-thirds of respondents (68%) were not at all or only slightly familiar with the concept. Lack of guidelines for toxicogenomics data interpretation, data quality assessment and on their use in HHRA, were found to be major barriers. In conclusion, there is a need for interventions aimed at facilitating the use of toxicogenomics data in HHRA, when available.

Systems Biology to Support Nanomaterial Grouping

The assessment of potential health risks of engineered nanomaterials (ENMs) is a challenging task due to the high number and great variety of already existing and newly emerging ENMs. Reliable grouping or categorization of ENMs with respect to hazards could help to facilitate prioritization and decision making for regulatory purposes. The development of grouping criteria, however, requires a broad and comprehensive data basis. A promising platform addressing this challenge is the systems biology approach. The different areas of systems biology, most prominently transcriptomics, proteomics and metabolomics, each of which provide a wealth of data that can be used to reveal novel biomarkers and biological pathways involved in the mode-of-action of ENMs. Combining such data with classical toxicological data would enable a more comprehensive understanding and hence might lead to more powerful and reliable prediction models. Physico-chemical data provide crucial information on the ENMs and need to be integrated, too. Overall statistical analysis should reveal robust grouping and categorization criteria and may ultimately help to identify meaningful biomarkers and biological pathways that sufficiently characterize the corresponding ENM subgroups. This chapter aims to give an overview on the different systems biology technologies and their current applications in the field of nanotoxicology, as well as to identify the existing challenges.

Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment

There is increasing interest in the use of quantitative transcriptomic data to determine benchmark dose (BMD) and estimate a point of departure (POD) for human health risk assessment. Although studies have shown that transcriptional PODs correlate with those derived from apical endpoint changes, there is no consensus on the process used to derive a transcriptional POD. Specifically, the subsets of informative genes that produce BMDs that best approximate the doses at which adverse apical effects occur have not been defined. To determine the best way to select predictive groups of genes, we used published microarray data from dose–response studies on six chemicals in rats exposed orally for 5, 14, 28, and 90 days. We evaluated eight approaches for selecting genes for POD derivation and three previously proposed approaches (the lowest pathway BMD, and the mean and median BMD of all genes). The relationship between transcriptional BMDs derived using these 11 approaches and PODs derived from apical data that might be used in chemical risk assessment was examined. Transcriptional BMD values for all 11 approaches were remarkably aligned with corresponding apical PODs, with the vast majority of toxicogenomics PODs being within tenfold of those derived from apical endpoints. We identified at least four approaches that produce BMDs that are effective estimates of apical PODs across multiple sampling time points. Our results support that a variety of approaches can be used to derive reproducible transcriptional PODs that are consistent with PODs produced from traditional methods for chemical risk assessment.

Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field

Recent advances in "-omics" technologies have simplified capacity to concurrently assess expression profiles of thousands of targets in a cellular system. However, compilation and analysis of "omics" data in support of human health protection remains a challenge. Benchmark dose (BMD) modeling is currently being employed in chemical risk assessment to estimate acceptable levels of exposure. Although typically applied to conventional endpoints, newer software has enabled this application to be extended to transcriptomic datasets. BMD analytical tools now have the capacity to model transcriptional dose-response data to derive meaningful BMD values for genes, pathways and gene ontologies. In this report, radiation data obtained from the Gene Expression Omnibus (GEO) were analyzed to generate BMD values for transcriptional responses. The datasets comprised microarray analyses of human blood gamma-irradiated ex vivo (0-20 Gy) and human-derived cell lines exposed to alpha particle radiation (0.5-1.5 Gy). The distributions of BMDs for statistically significant genes and pathways in response to radiation exposure were examined and compared across studies. BMD modeling could identify pathway/gene sensitivities across wide radiation dose ranges, experimental conditions (time-points, cell types) and radiation qualities. BMD analysis offered a new approach to examine transcriptional data. The results were shown to provide information on transcriptional thresholds of effects to support refined risk assessments for low dose ionizing radiation exposures, derive gene-based values for relative biological effectiveness and identify pathways involved in radiation sensitivities across cell types which may extend to applications a clinical setting. Environ. Mol. Mutagen. 57:589-604, 2016. © 2016 Wiley Periodicals, Inc.

Toxicogenomics Case Study: Furan

Development of pragmatic methodologies for human health risk assessment is required to address current regulatory challenges. We applied three toxicogenomic approaches—quantitative, predictive, and mechanistic—to a case study in mice exposed for 3 weeks to the hepatocarcinogen furan. We modeled the dose response of a variety of transcriptional endpoints and found that they produced benchmark doses similar to the furan-dependent cancer benchmark doses. Meta-analyses showed strong similarity between furan-dependent gene expression changes and those associated with several hepatic pathologies. Molecular pathways facilitated the development of a molecular mode of action for furan-induced hepatocellular carcinogenicity. Finally, we compared transcriptomic profiles derived from formalin-fixed and paraffin-embedded (FFPE) samples with those from high-quality frozen samples to evaluate whether archival samples are a viable option for toxicogenomic studies. The advantage of using FFPE tissues is that they are very well characterized (phenotypically); the disadvantage is that formalin degrades biomacromolecules, including RNA. We found that FFPE samples can be used for toxicogenomics using a ribo-depletion RNA-seq protocol. Our case study demonstrates the utility of toxicogenomics data to human health risk assessment, the potential of archival FFPE tissue samples, and identifies viable strategies toward the reduction of animal usage in chemical testing.

Modifications of carbon black nanoparticle surfaces modulate type II pneumocyte homoeostasis

Inhalation uptake of carbon black nanoparticles (CBNP) bears the risk of morphological and functional lung impairment attributed to the highly reactive particle surface area. Chemical particle surface modifications might affect particle-cell interactions; however, thus far these alterations have not been determined. This is the first in vivo study comparing particle-induced acute lung injury using Printex(®)90 (Pr90, 7 µg), Printex®90 covered by benzo[a]pyrene or 9-nitroanthracene (BaP-Pr90, NA-Pr90, 7 µg, 15% BaP or NA by weight), and acetylene carbon black (CB) with polycyclic aromatic hydrocarbons (PAH-AB, 7 µg, 20% PAH by weight). All particles were suspended in distilled water with bovine serum albumin (BSA). In addition, the influence of suspension media was tested using Printex®90 suspended without BSA (Pr90(-BSA), 7 µg). Quartz (DQ12, 7 µg), 70 µl saline (NaCl), and distilled water with or without BSA (H2O(+/-BSA)) were used as reference and controls. It was postulated that CBNP surface modifications trigger pulmonary responses. After oropharyngeal particle aspiration, lung functions were measured 2 d postexposure, followed by lung preparation for histological or bronchoalveolar lavage fluid (BALF) examinations and type II pneumocyte isolation on d 3. Head-out body plethysmography revealed reduced flow rates induced by PAH-AB. Examinations of BALF demonstrated reduced influx of macrophages after exposure to Pr90(-BSA) and decreased lymphocyte levels after Pr90(+BSA) or BaP-Pr90 treatment. Further, CBNP induced changes in mRNA expressions (surfactant proteins) in type II pneumocytes. These findings indicate that CBNP surface area and media modulate interactions between NP and lung cells in short-term experiments.

BMDExpress Data Viewer - a visualization tool to analyze BMDExpress datasets

Regulatory agencies increasingly apply benchmark dose (BMD) modeling to determine points of departure for risk assessment. BMDExpress applies BMD modeling to transcriptomic datasets to identify transcriptional BMDs. However, graphing and analytical capabilities within BMDExpress are limited, and the analysis of output files is challenging. We developed a web‐based application, BMDExpress Data Viewer (http://apps.sciome.com:8082/BMDX_Viewer/), for visualizing and graphing BMDExpress output files. The application consists of “Summary Visualization” and “Dataset Exploratory” tools. Through analysis of transcriptomic datasets of the toxicants furan and 4,4′‐methylenebis(N,N‐dimethyl)benzenamine, we demonstrate that the “Summary Visualization Tools” can be used to examine distributions of gene and pathway BMD values, and to derive a potential point of departure value based on summary statistics. By applying filters on enrichment P‐values and minimum number of significant genes, the “Functional Enrichment Analysis” tool enables the user to select biological processes or pathways that are selectively perturbed by chemical exposure and identify the related BMD. The “Multiple Dataset Comparison” tool enables comparison of gene and pathway BMD values across multiple experiments (e.g., across timepoints or tissues). The “BMDL‐BMD Range Plotter” tool facilitates the observation of BMD trends across biological processes or pathways. Through our case studies, we demonstrate that BMDExpress Data Viewer is a useful tool to visualize, explore and analyze BMDExpress output files. Visualizing the data in this manner enables rapid assessment of data quality, model fit, doses of peak activity, most sensitive pathway perturbations and other metrics that will be useful in applying toxicogenomics in risk assessment. © 2015 Her Majesty the Queen in Right of Canada. Journal of Applied Toxicology published by John Wiley & Sons, Ltd.

We developed BMDExpress Data Viewer, which contains two collections of tools, “Summary Visualization Tools” and “Dataset Exploratory Tools,” to visualize and analyze BMDExpress output files. Through two case studies, we demonstrate the capabilities of graphically examining transcriptomic dose–response datasets in a risk assessment context by comparing and observing trends in transcriptomic benchmark doses (BMDs) for biological processes and pathways. Our results illustrate that BMDExpress Data Viewer is a useful tool to visualize, explore and analyze BMDExpress output files.

Integrating toxicogenomics into human health risk assessment: lessons learned from the benzo[a]pyrene case study

The use of short-term toxicogenomic tests to predict cancer (or other health effects) offers considerable advantages relative to traditional toxicity testing methods. The advantages include increased throughput, increased mechanistic data, and significantly reduced costs. However, precisely how toxicogenomics data can be used to support human health risk assessment (RA) is unclear. In a companion paper ( Moffat et al. 2014 ), we present a case study evaluating the utility of toxicogenomics in the RA of benzo[a]pyrene (BaP), a known human carcinogen. The case study is meant as a proof-of-principle exercise using a well-established mode of action (MOA) that impacts multiple tissues, which should provide a best case example. We found that toxicogenomics provided rich mechanistic data applicable to hazard identification, dose-response analysis, and quantitative RA of BaP. Based on this work, here we share some useful lessons for both research and RA, and outline our perspective on how toxicogenomics can benefit RA in the short- and long-term. Specifically, we focus on (1) obtaining biologically relevant data that are readily suitable for establishing an MOA for toxicants, (2) examining the human relevance of an MOA from animal testing, and (3) proposing appropriate quantitative values for RA. We describe our envisioned strategy on how toxicogenomics can become a tool in RA, especially when anchored to other short-term toxicity tests (apical endpoints) to increase confidence in the proposed MOA, and emphasize the need for additional studies on other MOAs to define the best practices in the application of toxicogenomics in RA.

Impact of Genomics Platform and Statistical Filtering on Transcriptional Benchmark Doses (BMD) and Multiple Approaches for Selection of Chemical Point of Departure (PoD)

Many regulatory agencies are exploring ways to integrate toxicogenomic data into their chemical risk assessments. The major challenge lies in determining how to distill the complex data produced by high-content, multi-dose gene expression studies into quantitative information. It has been proposed that benchmark dose (BMD) values derived from toxicogenomics data be used as point of departure (PoD) values in chemical risk assessments. However, there is limited information regarding which genomics platforms are most suitable and how to select appropriate PoD values. In this study, we compared BMD values modeled from RNA sequencing-, microarray-, and qPCR-derived gene expression data from a single study, and explored multiple approaches for selecting a single PoD from these data. The strategies evaluated include several that do not require prior mechanistic knowledge of the compound for selection of the PoD, thus providing approaches for assessing data-poor chemicals. We used RNA extracted from the livers of female mice exposed to non-carcinogenic (0, 2 mg/kg/day, mkd) and carcinogenic (4, 8 mkd) doses of furan for 21 days. We show that transcriptional BMD values were consistent across technologies and highly predictive of the two-year cancer bioassay-based PoD. We also demonstrate that filtering data based on statistically significant changes in gene expression prior to BMD modeling creates more conservative BMD values. Taken together, this case study on mice exposed to furan demonstrates that high-content toxicogenomics studies produce robust data for BMD modelling that are minimally affected by inter-technology variability and highly predictive of cancer-based PoD doses.

Technical guide for applications of gene expression profiling in human health risk assessment of environmental chemicals

Toxicogenomics promises to be an important part of future human health risk assessment of environmental chemicals. The application of gene expression profiles (e.g., for hazard identification, chemical prioritization, chemical grouping, mode of action discovery, and quantitative analysis of response) is growing in the literature, but their use in formal risk assessment by regulatory agencies is relatively infrequent. Although additional validations for specific applications are required, gene expression data can be of immediate use for increasing confidence in chemical evaluations. We believe that a primary reason for the current lack of integration is the limited practical guidance available for risk assessment specialists with limited experience in genomics. The present manuscript provides basic information on gene expression profiling, along with guidance on evaluating the quality of genomic experiments and data, and interpretation of results presented in the form of heat maps, pathway analyses and other common approaches. Moreover, potential ways to integrate information from gene expression experiments into current risk assessment are presented using published studies as examples. The primary objective of this work is to facilitate integration of gene expression data into human health risk assessments of environmental chemicals.

A gene signature for gold nanoparticle-exposed human cell lines

A unique four-gene signature for AuNP exposure was identified using the cDNA microarray and evaluated by qPCR and biological assays in mammalian cell lines.

Incorporating epigenetic data into the risk assessment process for the toxic metals arsenic, cadmium, chromium, lead, and mercury: strategies and challenges

Exposure to toxic metals poses a serious human health hazard based on ubiquitous environmental presence, the extent of exposure, and the toxicity and disease states associated with exposure. This global health issue warrants accurate and reliable models derived from the risk assessment process to predict disease risk in populations. There has been considerable interest recently in the impact of environmental toxicants such as toxic metals on the epigenome. Epigenetic modifications are alterations to an individual's genome without a change in the DNA sequence, and include, but are not limited to, three commonly studied alterations: DNA methylation, histone modification, and non-coding RNA expression. Given the role of epigenetic alterations in regulating gene and thus protein expression, there is the potential for the integration of toxic metal-induced epigenetic alterations as informative factors in the risk assessment process. In the present review, epigenetic alterations induced by five high priority toxic metals/metalloids are prioritized for analysis and their possible inclusion into the risk assessment process is discussed.