Computing Sciences and Computer Engineering
Background: Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting.
Methods: Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways.
Results: Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach.
Conclusions: Findings from this proof-of-concept study suggest that our approach has a great potential in providing a novel and sensitive tool for threshold setting in chemical risk assessment. In future work, we plan to analyze more time-series datasets with a full spectrum of concentrations and sufficient replications per treatment. The pathway alteration-derived thresholds will also be compared with those derived from apical endpoints such as cell growth rate.
(2013). Differential Reconstructed Gene Interaction Networks for Deriving Toxicity Threshold In Chemical Risk Assessment. BMC Bioinformatics.
Available at: https://aquila.usm.edu/fac_pubs/17936
Supplementary file 1. Breakdown of the two types of differentially expressed genes. Y = Yes, N = No.
Additional File 2.xlsx (3572 kB)
Supplementary file 2-4. Connectivity between 176 genes in the network reconstructed from E. coli cells treated with 1000 mg/L naphthenic acids
Additional File 3.xlsx (41 kB)
Supplementary file 3. Differential edges derived by pair-wise comparison between the top 704 edges of the four reconstructed networks (control vs. low/mid/high)
Additional File 4.xlsx (51 kB)
Supplementary file 4 - Pathway mapping. Gene annotation, GO terms, and Ecocyc and KEGG pathway mapping
Additional File 5.xlsx (25 kB)