Toxic and Genomic Influences of Inhaled Nanomaterials as a Basis for Predicting Adverse Outcome

Abstract

An immense variety of different types of engineered nanomaterials are currently being developed and increasingly applied to consumer products. Importantly, engineered nanomaterials may pose unexplored adverse health effects because of their small size. Particularly in occupational settings, the dustiness of certain engineered nanomaterials involves risk of inhalation and influences on lung function. These facts call for quick and cost-effective safety testing practices, such as that obtained through multiparametric high-throughput screening using cultured human lung cells. The predictive value of such in vitro-based testing depends partly on the effectiveness of coverage of the mechanisms underlying toxicity effects. The concept of adverse outcome pathways covers the array of causative effects starting from a molecular initiating event via cellular-, organ-, individual-, and population-level effects. Screening for adverse outcome pathway-related effects that drive the eventual toxic outcome provides a good basis for developing predictive testing methods and data-driven integrated testing strategies for hazard and risk assessment. Temporal and inherited genomic changes are likely to drive many adverse responses to engineered nanomaterials, such as multiwalled carbon nanotubes, of which one specific form has recently been evaluated as possibly carcinogenic. Here, we briefly describe current state-of-the-art strategies for analyzing and understanding genomic influences of engineered nanomaterial exposure, including the selected focus on lung disease, and strategies for using mechanistic knowledge to predict and prevent adverse outcome.

An Integrative Computational Approach for a Prioritization of Key Transcription Regulators Associated With Nanomaterial-Induced Toxicity

“…In conclusion, we have developed an integrative computational approach to prioritize key transcription regulators, their associated biological processes and signaling pathways, which were altered in response to toxic compounds. We applied omics-based tools using a systems biology approach which can have a pivotal role in moving toxicity testing away from in vivo to in vitro and in silico models (Nymark et al, 2018). Our method uses transcriptomics data, generates interaction networks that are specific to each toxic agent, and is independent from bias in the reference databases for pathway mapping as it infers connections and pathways de novo purely based on the data.…”

Adverse outcome pathways as a tool for the design of testing strategies to support the safety assessment of emerging advanced materials at the nanoscale

“…The other effective strategy for identifying KEs and AOs entails use of high-throughput (HT) and high-content (HC) data often referred to as toxicogenomics [9,44]. Toxicogenomic data gives a broad overview of the molecular mechanisms of toxicity initiated by stressors in a wide variety of biological models, and as a result, is expected to feed virtually all blocks of AOPs, from the underlying toxicity mechanism to selection of an MIE, cellular level KEs, tissue and organ level KEs, and the final AO.…”

“…Toxicogenomic data gives a broad overview of the molecular mechanisms of toxicity initiated by stressors in a wide variety of biological models, and as a result, is expected to feed virtually all blocks of AOPs, from the underlying toxicity mechanism to selection of an MIE, cellular level KEs, tissue and organ level KEs, and the final AO. Two main advantages of using toxicogenomics data for advancing AOP development are: i) the comprehensive data supports validation of MIEs and KEs by providing molecular level details, and ii) the data enables identification of sensitive biomarkers for targeted measurement of the KEs identified in the AOP [9,44]. Initiatives have been taken to link biological pathway databases, such as WikiPathways, to AOPs, which enables AOP-linked bioinformatics analysis of toxicogenomics data [13,45].…”

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

“…Adverse outcome pathway (AOP) is a conceptual framework that couples existing knowledge on the links between a molecular initiating event (MIE), such as contact of nanomaterial with Toll-like receptors on the cell surface, with the activation of a chain of causally relevant biological processes or key events (KE), e.g., the production of inflammatory cytokines, with the resulting adverse outcomes (AO) at the level of the organ or the organism (e.g., lung fibrosis) [76,77]. Coupling of gene expression profiling with bioinformatics-driven placement of the results into AOP descriptions has the potential for quantitative analysis of adverse effects that combines in vitro-derived mechanistic analyses with causally relevant modes-of-action and related key events [77,78].…”

“…Coupling of gene expression profiling with bioinformatics-driven placement of the results into AOP descriptions has the potential for quantitative analysis of adverse effects that combines in vitro-derived mechanistic analyses with causally relevant modes-of-action and related key events [77,78]. As AOPs can span different cell types, numerous in vitro assays may need to be associated with a single one [14,76]. The details of the coupling are still being worked out by the community but mapping the results of pathway analyses to KEs is a simple alternative.…”