Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

Gene co-expression network analysis in zebrafish reveals chemical class specific modules

BACKGROUND

Zebrafish is a popular animal model used for high-throughput screening of chemical hazards, however, investigations of transcriptomic mechanisms of toxicity are still needed. Here, our goal was to identify genes and biological pathways that Aryl Hydrocarbon Receptor 2 (AHR2) Activators and flame retardant chemicals (FRCs) alter in developing zebrafish. Taking advantage of a compendium of phenotypically-anchored RNA sequencing data collected from 48-h post fertilization (hpf) zebrafish, we inferred a co-expression network that grouped genes based on their transcriptional response.

RESULTS

Genes responding to the FRCs and AHR2 Activators localized to distinct regions of the network, with FRCs inducing a broader response related to neurobehavior. AHR2 Activators centered in one region related to chemical stress responses. We also discovered several highly co-expressed genes in this module, including cyp1a, and we subsequently show that these genes are definitively within the AHR2 signaling pathway. Systematic removal of the two chemical types from the data, and analysis of network changes identified neurogenesis associated with FRCs, and regulation of vascular development associated with both chemical classes. We also identified highly connected genes responding specifically to each class that are potential biomarkers of exposure.

CONCLUSIONS

Overall, we created the first zebrafish chemical-specific gene co-expression network illuminating how chemicals alter the transcriptome relative to each other. In addition to our conclusions regarding FRCs and AHR2 Activators, our network can be leveraged by other studies investigating chemical mechanisms of toxicity.

Comparative mechanisms of PAH toxicity by benzo[a]pyrene and dibenzo[def,p]chrysene in primary human bronchial epithelial cells cultured at air-liquid interface

Current assumption for assessing carcinogenic risk of polycyclic aromatic hydrocarbons (PAHs) is that they function through a common mechanism of action; however, recent studies demonstrate that PAHs can act through unique mechanisms potentially contributing to cancer outcomes in a non-additive manner. Using a primary human 3D bronchial epithelial culture (HBEC) model, we assessed potential differences in mechanism of toxicity for two PAHs, benzo[a]pyrene (BAP) and dibenzo[def,p]chrysene (DBC), compared to a complex PAH mixture based on short-term biosignatures identified from transcriptional profiling. Differentiated bronchial epithelial cells were treated with BAP (100-500 μg/ml), DBC (10 μg/ml), and coal tar extract (CTE 500-1500 μg/ml, SRM1597a) for 48 h and gene expression was measured by RNA sequencing or quantitative PCR. Comparison of BAP and DBC gene signatures showed that the majority of genes (~60%) were uniquely regulated by treatment, including signaling pathways for inflammation and DNA damage by DBC and processes for cell cycle, hypoxia and oxidative stress by BAP. Specifically, BAP upregulated targets of AhR, NRF2, and KLF4, while DBC downregulated these same targets, suggesting a chemical-specific pattern in transcriptional regulation involved in antioxidant response, potentially contributing to differences in PAH potency. Other processes were regulated in common by all PAH treatments, BAP, DBC and CTE, including downregulation of genes involved in cell adhesion and reduced functional measurements of barrier integrity. This work supports prior in vivo studies and demonstrates the utility of profiling short-term biosignatures in an organotypic 3D model to identify mechanisms linked to carcinogenic risk of PAHs in humans.

Simulation of the Bottleneck Controlling Access into a Rieske Active Site: Predicting Substrates of Naphthalene 1,2-Dioxygenase

Naphthalene 1,2-dioxygenase (NDO) has been computationally understudied despite the extensive experimental knowledge obtained for this enzyme, including numerous crystal structures and over 100 demonstrated substrates. In this study, we have developed a substrate prediction model that moves away from the traditional active-site-centric approach to include the energetics of substrate entry into the active site. By comparison with experimental data, the accuracy of the model for predicting substrate oxidation is 92%, with a positive predictive value of 93% and a negative predictive value of 98%. Also, the present analysis has revealed that the amino acid residues that provided the largest energetic barrier for compounds entering the active site are residues F224, L227, P234, and L235. In addition, F224 is proposed to play a role in controlling ligand entrance via π-π stacking stabilization as well as providing stabilization via T-shaped π-π interactions once the ligand has reached the active-site cavity. Overall, we present a method capable of being scaled to computationally discover thousands of substrates of NDO, and we present parameters to be used for expanding the prediction method to other members of the Rieske non-heme iron oxygenase family.

Mechanism-Based Classification of PAH Mixtures to Predict Carcinogenic Potential

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway-based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP, or environmental PAH mixtures (Mix 1-3) following a 2-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC >> BaP = Mix2 = Mix3 > Mix1 = Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared with tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (P < .05) for DNA damage, apoptosis, response to chemical stimulus, and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. These data further provide a 'source-to-outcome' model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action-based risk assessment could be employed for environmental PAH mixtures.