Contribution of new technologies to characterization and prediction of adverse effects

A toxicity pathway-based approach for modeling the mode of action framework of lead-induced neurotoxicity

BACKGROUND

The underlying mechanisms of lead (Pb) toxicity are not fully understood, which makes challenges to the traditional risk assessment. There is growing use of the mode of action (MOA) for risk assessment by integration of experimental data and system biology. The current study aims to develop a new pathway-based MOA for assessing Pb-induced neurotoxicity.

METHODS

The available Comparative Toxicogenomic Database (CTD) was used to search genes associated with Pb-induced neurotoxicity followed by developing toxicity pathways using Ingenuity Pathway Analysis (IPA). The spatiotemporal sequence of disturbing toxicity pathways and key events (KEs) were identified by upstream regulator analysis. The MOA framework was constructed by KEs in biological and chronological order.

RESULTS

There were a total of 71 references showing the relationship between lead exposure and neurotoxicity, which contained 2331 genes. IPA analysis showed that the neuroinflammation signaling pathway was the core toxicity pathway in the enriched pathways relevant to Pb-induced neurotoxicity. The upstream regulator analysis demonstrated that the aryl hydrocarbon receptor (AHR) signaling pathway was the upstream regulator of the neuroinflammation signaling pathway (11.76% overlap with upstream regulators, |Z-score|=1.451). Therefore, AHR activation was recognized as the first key event (KE1) in the MOA framework. The following downstream molecular and cellular key events were also identified. The pathway-based MOA framework of Pb-induced neurotoxicity was built starting with AHR activation, followed by an inflammatory response and neuron apoptosis.

CONCLUSION

Our toxicity pathway-based approach not only advances the development of risk assessment for Pb-induced neurotoxicity but also brings new insights into constructing MOA frameworks of risk assessment for new chemicals.

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets

Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.

Critical assessment and integration of separate lines of evidence for risk assessment of chemical mixtures

Humans are exposed to multiple chemicals on a daily basis instead of to just a single chemical, yet the majority of existing toxicity data comes from single-chemical exposure. Multiple factors must be considered such as the route, concentration, duration, and the timing of exposure when determining toxicity to the organism. The need for adequate model systems (in vivo, in vitro, in silico and mathematical) is paramount for better understanding of chemical mixture toxicity. Currently, shortcomings plague each model system as investigators struggle to find the appropriate balance of rigor, reproducibility and appropriateness in mixture toxicity studies. Significant questions exist when comparing single-to mixture-chemical toxicity concerning additivity, synergism, potentiation, or antagonism. Dose/concentration relevance is a major consideration and should be subthreshold for better accuracy in toxicity assessment. Previous work was limited by the technology and methodology of the time, but recent advances have resulted in significant progress in the study of mixture toxicology. Novel technologies have added insight to data obtained from in vivo studies for predictive toxicity testing. These include new in vitro models: omics-related tools, organs-on-a-chip and 3D cell culture, and in silico methods. Taken together, all these modern methodologies improve the understanding of the multiple toxicity pathways associated with adverse outcomes (e.g., adverse outcome pathways), thus allowing investigators to better predict risks linked to exposure to chemical mixtures. As technology and knowledge advance, our ability to harness and integrate separate streams of evidence regarding outcomes associated with chemical mixture exposure improves. As many national and international organizations are currently stressing, studies on chemical mixture toxicity are of primary importance.

Transcriptomic Profiles in Zebrafish Liver Permit the Discrimination of Surface Water with Pollution Gradient and Different Discharges

The present study aims to evaluate the potential of transcriptomic profiles in evaluating the impacts of complex mixtures of pollutants at environmentally relevant concentrations on aquatic vertebrates. The changes in gene expression were determined using microarray in the liver of male zebrafish (Danio rerio) exposed to surface water collected from selected locations on the Hun River, China. The numbers of differentially expressed genes (DEGs) in each treatment ranged from 728 to 3292, which were positively correlated with chemical oxygen demand (COD). Predominant transcriptomic responses included peroxisome proliferator-activated receptors (PPAR) signaling and steroid biosynthesis. Key pathways in immune system were also affected. Notably, two human diseases related pathways, insulin resistance and Salmonella infection were enriched. Clustering analysis and principle component analysis with DEGs differentiated the upstream and downstream site of Shenyang City, and the mainstream and the tributary sites near the junction. Comparison the gene expression profiles of zebrafish exposed to river surface water with those to individual chemicals found higher similarity of the river water with estradiol than several other organic pollutants and metals. Results suggested that the transcriptomic profiles of zebrafish is promising in differentiating surface water with pollution gradient and different discharges and in providing valuable information to support discharge management.

Transcriptome profiling of HepG2 cells exposed to the flame retardant 9,10-dihydro-9-oxa-10-phosphaphenanthrene 10-oxide (DOPO)

The flame retardant, 9,10-dihydro-9-oxa-10-phosphaphenanthrene 10-oxide (DOPO), has been receiving great interest given its superior fire protection properties, and its predicted low level of persistence, bioaccumulation, and toxicity. However, empirical toxicological data that are essential for a complete hazard assessment are severely lacking. In this study, we attempted to identify the potential toxicological modes of action by transcriptome (RNA-seq) profiling of the human liver hepatocellular carcinoma cell line, HepG2. Such insight may help in identifying compounds of concern and potential toxicological phenotypes. DOPO was found to have little cytotoxic potential, with lower effective concentrations compared to other flame retardants studied in the same cell line. Differentially expressed genes revealed a wide range of molecular effects including changes in protein, energy, DNA, and lipid metabolism, along with changes in cellular stress response pathways. In response to 250 μM DOPO, the most perturbed biological processes were fatty acid metabolism, androgen metabolism, glucose transport, and renal function and development, which is in agreement with other studies that observed similar effects of other flame retardants in other species. However, treatment with 2.5 μM DOPO resulted in very few differentially expressed genes and failed to indicate any potential effects on biology, despite such concentrations likely being orders of magnitude greater than would be encountered in the environment. This, together with the low levels of cytotoxicity, supports the potential replacement of the current flame retardants by DOPO, although further studies are needed to establish the nephrotoxicity and endocrine disruption of DOPO.

Effects of dispersible MoS2 nanosheets and Nano-silver coexistence on the metabolome of yeast

As a new rising star in the post-graphene two-dimensional materials (2DMs), molybdenum disulfide (MoS₂) attracts increasing attentions and is widely applied. However, the chemical and toxicological interaction between MoS₂ and other co-contaminants is still poorly understood. Nano-silver (N-Ag) is the most commonly used nanomaterial in commercial products and distributed widely in the environment. Herein, we investigated the effects of chitosan functionalized MoS₂ (CS-MoS₂) nanosheets, a water-dispersible form of MoS₂, on the microbial toxicity of N-Ag. We found that the incorporation of CS-MoS₂ nanosheets attenuated the oxidative stress induced by N-Ag on yeast cells, while caused more membrane stress. In addition, the inhibition of N-Ag on the metabolic activities of yeast cells could be attenuated by CS-MoS₂ nanosheets as well. The coexistence of N-Ag and CS-MoS₂ nanosheets mainly perturbed the amino acid-related metabolic pathways in yeast cells, and phosphoric acid was a potential nanotoxicity biomarker. We further found that CS-MoS₂ nanosheets dramatically absorbed the Ag ion released from N-Ag, which might be responsible for its attenuation effect on the microbial toxicity of N-Ag. Our findings provide more new insights for the ecotoxicity evaluation of MoS₂ and other 2DMs.

Effects of chlorpyrifos and trichloropyridinol on HEK 293 human embryonic kidney cells

Chlorpyrifos (CPF) [O, O-diethyl -O-3, 5, 6-trichloro-2-pyridyl phosphorothioate] is an organophosphate insecticide widely used for agricultural and urban pest control. Trichloropyridinol (TCP; 3,5,6-trichloro-2-pyridinol), the primary metabolite of CPF, is often used as a generic biomarker of exposure for CPF and related compounds. Human embryonic kidney 293 (HEK 293) cells were exposed to CPF and TCP with varying concentrations and exposure periods. Cell cultures enable the cost-effective study of specific biomarkers to help determine toxicity pathways to predict the effects of chemical exposures without relying on whole animals. Both CPF and TCP were found to induce cytotoxic effects with CPF being more toxic than TCP with EC₅₀ values of 68.82 μg/mL and 146.87 μg·ml^-1 respectively. Cell flow cytometric analyses revealed that exposure to either CPF or TCP leads to an initial burst of apoptotic induction followed by a slow recruitment of cells leading towards further apoptosis. CPF produced a strong induction of IL6, while TCP exposure resulted in a strong induction of IL1α. Importantly, the concentrations of CPF and TCP required for these cytokine inductions were higher than those required to induce apoptosis. These data suggest CPF and TCP are cytotoxic to HEK 293 cells but that the mechanism may not be related to an inflammatory response. CPF and TCP also varied in their effects on the HEK 293 proteome with 5 unique proteins detected after exposure to CPF and 31 unique proteins after TCP exposure.

Pesticide toxicogenomics across scales: in vitro transcriptome predicts mechanisms and outcomes of exposure in vivo

In vitro Omics analysis (i.e. transcriptome) is suggested to predict in vivo toxicity and adverse effects in humans, although the causal link between high-throughput data and effects in vivo is not easily established. Indeed, the chemical-organism interaction can involve processes, such as adaptation, not established in cell cultures. Starting from this consideration we investigate the transcriptomic response of immortalized thyrocytes to ethylenthiourea and chlorpyrifos. In vitro data revealed specific and common genes/mechanisms of toxicity, controlling the proliferation/survival of the thyrocytes and unrelated hematopoietic cell lineages. These results were phenotypically confirmed in vivo by the reduction of circulating T4 hormone and the development of pancytopenia after long exposure. Our data imply that in vitro toxicogenomics is a powerful tool in predicting adverse effects in vivo, experimentally confirming the vision described as Tox21c (Toxicity Testing in the 21st century) although not fully recapitulating the biocomplexity of a living animal.

Effects of Graphene Oxide and Oxidized Carbon Nanotubes on the Cellular Division, Microstructure, Uptake, Oxidative Stress, and Metabolic Profiles

Nanomaterial oxides are common formations of nanomaterials in the natural environment. Herein, the nanotoxicology of typical graphene oxide (GO) and carboxyl single-walled carbon nanotubes (C-SWCNT) was compared. The results showed that cell division of Chlorella vulgaris was promoted at 24 h and then inhibited at 96 h after nanomaterial exposure. At 96 h, GO and C-SWCNT inhibited the rates of cell division by 0.08-15% and 0.8-28.3%, respectively. Both GO and C-SWCNT covered the cell surface, but the uptake percentage of C-SWCNT was 2-fold higher than that of GO. C-SWCNT induced stronger plasmolysis and mitochondrial membrane potential loss and decreased the cell viability to a greater extent than GO. Moreover, C-SWCNT-exposed cells exhibited more starch grains and lysosome formation and higher reactive oxygen species (ROS) levels than GO-exposed cells. Metabolomics analysis revealed significant differences in the metabolic profiles among the control, C-SWCNT and GO groups. The metabolisms of alkanes, lysine, octadecadienoic acid and valine was associated with ROS and could be considered as new biomarkers of ROS. The nanotoxicological mechanisms involved the inhibition of fatty acid, amino acid and small molecule acid metabolisms. These findings provide new insights into the effects of GO and C-SWCNT on cellular responses.

Envelopment-Internalization Synergistic Effects and Metabolic Mechanisms of Graphene Oxide on Single-Cell Chlorella vulgaris Are Dependent on the Nanomaterial Particle Size

The interactions between nanomaterials and cells are fundamental in biological responses to nanomaterials. However, the size-dependent synergistic effects of envelopment and internalization as well as the metabolic mechanisms of nanomaterials have remained unknown. The nanomaterials tested here were larger graphene oxide nanosheets (GONS) and small graphene oxide quantum dots (GOQD). GONS intensively entrapped single-celled Chlorella vulgaris, and envelopment by GONS reduced the cell permeability. In contrast, GOQD-induced remarkable shrinkage of the plasma membrane and then enhanced cell permeability through strong internalization effects such as plasmolysis, uptake of nanomaterials, an oxidative stress increase, and inhibition of cell division and chlorophyll biosynthesis. Metabolomics analysis showed that amino acid metabolism was sensitive to nanomaterial exposure. Shrinkage of the plasma membrane is proposed to be linked to increases in the isoleucine levels. The inhibition of cell division and chlorophyll a biosynthesis was associated with decreases in aspartic acid and serine, the precursors of chlorophyll a. The increases in mitochondrial membrane potential loss and oxidative stress were correlated with an increase in linolenic acid. The above metabolites can be used as indicators of the corresponding biological responses. These results enhance our systemic understanding of the size-dependent biological effects of nanomaterials.