Systems Toxicology Approach for Assessing Developmental Neurotoxicity in Larval Zebrafish

Abstract

Adverse outcomes that result from chemical toxicity are rarely caused by dysregulation of individual proteins; rather, they are often caused by system-level perturbations in networks of molecular events. To fully understand the mechanisms of toxicity, it is necessary to recognize the interactions of molecules, pathways, and biological processes within these networks. The developing brain is a prime example of an extremely complex network, which makes developmental neurotoxicity one of the most challenging areas in toxicology. We have developed a systems toxicology method that uses a computable biological network to represent molecular interactions in the developing brain of zebrafish larvae. The network is curated from scientific literature and describes interactions between biological processes, signaling pathways, and adverse outcomes associated with neurotoxicity. This allows us to identify important signaling hubs, pathway interactions, and emergent adverse outcomes, providing a more complete understanding of neurotoxicity. Here, we describe the construction of a zebrafish developmental neurotoxicity network and its validation by integration with publicly available neurotoxicity-related transcriptomic datasets. Our network analysis identified consistent regulation of tumor suppressors p53 and retinoblastoma 1 (Rb1) as well as the oncogene Krüppel-like factor (Klf8) in response to chemically induced developmental neurotoxicity. The developed network can be used to interpret transcriptomic data in a neurotoxicological context.

Analysis of Gene-Environment Interactions Related to Developmental Disorders

“…For example, an integrative analysis using CTD, SSC, and ASC revealed a total of 212 gene–environment interaction pairs putatively relevant for ASD, and provided a list of candidate genes susceptible to chemicals associated with ASD, such as valproic acid, benzo(a)pyrene, bisphenol A, particulate matter, and perfluorooctane sulfonic acid ( Santos et al, 2019 ). A novel in silico approach using GEO identified tumor suppressors: p53, retinoblastoma 1, and Kr ü ppel-like factor 8 as leading nodes in the network of developmental neurotoxicity of selective serotonin reuptake inhibitors and antidepressants associated with ASD ( Li et al, 2021 ). A study using CTD and a database of ASD gene networks ( Nelson et al, 2012 ) found that ASD-associated genes are selectively targeted by environmental pollutants such as pesticides, heavy metals, and phthalates ( Carter and Blizard, 2016 ).…”

From Causal Networks to Adverse Outcome Pathways: A Developmental Neurotoxicity Case Study

“…We have developed a semi-automatic pipeline and scripts that take a CTN in BEL format, remove unneeded node and connection annotations and add new functional ones, then reduce the network to a size similar to an AOP network ( Pollesch et al, 2019 ). We performed these operations on a recently developed zebrafish developmental neurotoxicity network as a case study ( Li et al, 2021 ). The network was then separated into subnetworks, based on different starting points (candidate MIEs of different types) and endpoints (pathologies), with a size suitable for close visual inspection.…”

“…The zebrafish causal developmental neurotoxicity network, which is the basis of our AOP development, has been developed in an earlier publication, therefore we here only summarize the most relevant properties of the network for this study, and refer the reader to original publication for details ( Li et al, 2021 ). CTN network development usually starts from a known adverse outcome of interest.…”

“…The choice of developmental neurotoxicity was made, because AOPs related to developmental neurotoxicity are still underrepresented in the AOP-wiki ( Bal-Price et al, 2015 ; Knapen et al, 2015 ; Pistollato et al, 2020 ) and because of the recent activity in incorporation of AOP-based developmental neurotoxicity in vitro assays into chemical risk assessment ( Sachana et al, 2021 ). This also allowed us to use our recently developed zebrafish causal developmental neurotoxicity network as starting point ( Li et al, 2021 ). The new AOPs can be found on the AOP-wiki under Ids 399 and 410 ( https://aopwiki.org/aops/399 and https://aopwiki.org/aops/410 ).…”

RNA-seq reveals potential gene biomarkers in fathead minnows (Pimephales promelas) for exposure to treated wastewater effluent

“…35 Reverse causal reasoning is a method used to support the construction of computational models to help evaluate and predict chemical toxicity. 36,37 Therefore, this approach falls in line with the current push towards more computationally based toxicity assessments. 38 By applying a systems biology approach to our brain-tissue-specific molecular dataset, it was possible to identify potential gene biomarkers that were significantly correlated with exposure to WWTP effluent and with neurologically relevant functions and disease states.…”