A new Transcriptional Effect Level Index (TELI) for toxicogenomics-based toxicity assessment.

This study proposes and demonstrates the potential application of a new Transcriptional Effect Level Index (TELI) to convert the information-rich toxicogenomic data into integrated and quantitative endpoints. A library of transcriptional fusions of green fluorescent protein (GFP) that includes different promoters for 91 stress-related genes in E. coli K12, MG1655 is employed to evaluate the gene expression alteration induced by exposure to four nanomaterials (NMs), nano silver (nAg), nano titanium dioxide anatase (nTiO₂_a), nano titanium dioxide rutile (nTiO₂_r), and fullerene soot. TELI is determined for each toxicogenomic assay, and it incorporates the number and identity of genes that had altered expression, the magnitude of alteration, and the temporal pattern of gene expression change in response to toxicant exposure. TELI values exhibit a characteristic "sigmoid" shaped toxicity dose-response curve, based on which TELI(MAX) (the maximal value of TELI), TELI50 (concentration that yields half of TELI(MAX)), NOTEL(TELI) (TELI-based no observed transcriptional effect level), and Slope(TELI) (the slope of TELI-dose response curve) are obtained. TELI-based endpoints are compared to currently used endpoints such as EC50 and no observed transcriptional effect level (NOTEL). The agreement of NOTEL(TELI) and NOTEL values validates the concept and application of TELI. Multiple endpoints derived from TELI can describe the dose response behavior and characteristics more completely and holistically than single points such as NOTEL alone. TELI values determined for genes in each stress response category (e.g., oxidative stress, DNA repair) indicate mode of action (MOA)-related comparative transcriptional level toxicity among compounds, and it reveals detailed information of toxic response pathways such as different DNA damage and repair mechanisms among the NMs. This study presents a methodology for converting the rich toxicogenomic information into a readily usable and transferable format that can be potentially linked to regulation endpoints and incorporated into a decision-making framework.