Nano-risk Science: application of toxicogenomics in an adverse outcome pathway framework for risk assessment of multi-walled carbon nanotubes - PubMed
- ️Fri Jan 01 2016
Nano-risk Science: application of toxicogenomics in an adverse outcome pathway framework for risk assessment of multi-walled carbon nanotubes
Sarah Labib et al. Part Fibre Toxicol. 2016.
Abstract
Background: A diverse class of engineered nanomaterials (ENMs) exhibiting a wide array of physical-chemical properties that are associated with toxicological effects in experimental animals is in commercial use. However, an integrated framework for human health risk assessment (HHRA) of ENMs has yet to be established. Rodent 2-year cancer bioassays, clinical chemistry, and histopathological endpoints are still considered the 'gold standard' for detecting substance-induced toxicity in animal models. However, the use of data derived from alternative toxicological tools, such as genome-wide expression profiling and in vitro high-throughput assays, are gaining acceptance by the regulatory community for hazard identification and for understanding the underlying mode-of-action. Here, we conducted a case study to evaluate the application of global gene expression data in deriving pathway-based points of departure (PODs) for multi-walled carbon nanotube (MWCNT)-induced lung fibrosis, a non-cancer endpoint of regulatory importance.
Methods: Gene expression profiles from the lungs of mice exposed to three individual MWCNTs with different physical-chemical properties were used within the framework of an adverse outcome pathway (AOP) for lung fibrosis to identify key biological events linking MWCNT exposure to lung fibrosis. Significantly perturbed pathways were categorized along the key events described in the AOP. Benchmark doses (BMDs) were calculated for each perturbed pathway and were used to derive transcriptional BMDs for each MWCNT.
Results: Similar biological pathways were perturbed by the different MWCNT types across the doses and post-exposure time points studied. The pathway BMD values showed a time-dependent trend, with lower BMDs for pathways perturbed at the earlier post-exposure time points (24 h, 3d). The transcriptional BMDs were compared to the apical BMDs derived by the National Institute for Occupational Safety and Health (NIOSH) using alveolar septal thickness and fibrotic lesions endpoints. We found that regardless of the type of MWCNT, the BMD values for pathways associated with fibrosis were 14.0-30.4 μg/mouse, which are comparable to the BMDs derived by NIOSH for MWCNT-induced lung fibrotic lesions (21.0-27.1 μg/mouse).
Conclusions: The results demonstrate that transcriptomic data can be used to as an effective mechanism-based method to derive acceptable levels of exposure to nanomaterials in product development when epidemiological data are unavailable.
Keywords: Adverse outcome pathways; Benchmark dose; Case study; Lung fibrosis; Nano; Risk assessment; Toxicogenomics.
Figures
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Comparison of traditional and genomics approaches for determining points of departure for exposure to MWCNT

Schematic of adverse outcome pathway (AOP) for pulmonary injury leading to fibrosis. MIE: molecular initiating event, KE: key event, AO: adverse outcome, AE: associative event, CNT: carbon nanotube, ECM: extracellular matrix. Arrows in inset figure show inflammation at day 1 and fibrosis at day 28 in lung tissue

The distribution of pathway BMD-median values is influenced by post-exposure time. Distributions of pathway BMD-median values for NM-401 (top), NRCWE-026 (center), and Mitsui-7 (bottom). Pathways were only considered in this analysis if they were significant (P < 0.05) with five or more DEGs associated with them and if they had five or more molecules with goodness-of-fit P value > 0.1 and BMD/BMDL ratios < 10. Overlain table indicates the median BMD(L) across all pathways for each time-point

Pathways with the lowest BMD(L) values for each part of AOP per MWCNT and time-point. Figure shows the pathway with the lowest BMDL for the MIE (Molecular Initiating Event) and each KE (Key Event) for NM-401 (solid black circles), NRCWE-026 (square with hatched lines), and Mitsui-7 (star). The 95 % lower confidence interval (BMDL) for each BMD is represented by the error bars. Only significant canonical pathways (P < 0.05, >5 genes) with BMD/BMDL ratios <10 and >5 genes modeled were included. The 3d/7d time-point with the asterisk (*) is a placeholder for 7 days for Mitsui-7 as there was no 3 days time-point recorded. Blank spaces indicate no pathway was significant for that time-point. The KE5 only includes the pathway Fibrosis

Comparison of BMD(L)s derived from four genomics approaches compared to the traditional NIOSH approach. Both AOP-independent (approaches 1 and 2; panels (a) and (b), respectively) and AOP-dependent approaches (approaches 3 and 4; panels (c) and (d), respectively) are shown. The grey lines represent the BMD (right bar) and BMDL (left bar) values for fibrosis apical endpoint [34, 43]. BMD values are represented by solid circles for NM-401, hatched squares for NRCWE-026, and stars for Mitsui-7. The 95 % lower confidence interval (BMDL) for each BMD is represented by the error bars. The 3 days/7 days time-point with the asterisk (*) is a placeholder for 7 days for Mitsui-7 as there was no 3 days time-point recorded
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