Cecchetto M et al. (2023), An innovative index to incorporate transcriptom...

XB-ART-59557

Environ Res 2023 Jun 15;227:115745. doi: 10.1016/j.envres.2023.115745.

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An innovative index to incorporate transcriptomic data into weight of evidence approaches for environmental risk assessment.

Cecchetto M , Peruzza L , Giubilato E , Bernardini I , Rovere GD , Marcomini A , Regoli F , Bargelloni L , Patarnello T , Semenzin E , Milan M .

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The sharp decrease in the cost of RNA-sequencing and the rapid improvement in computational analysis of eco-toxicogenomic data have brought new insights into the adverse effects of chemicals on aquatic organisms. Yet, transcriptomics is generally applied qualitatively in environmental risk assessments, hampering more effective exploitation of this evidence through multidisciplinary studies. In view of this limitation, a methodology is here presented to quantitatively elaborate transcriptional data in support to environmental risk assessment. The proposed methodology makes use of results from the application of Gene Set Enrichment Analysis to recent studies investigating the response of Mytilus galloprovincialis and Ruditapes philippinarum exposed to contaminants of emerging concern. The degree of changes in gene sets and the relevance of physiological reactions are integrated in the calculation of a hazard index. The outcome is then classified according to five hazard classes (from absent to severe), providing an evaluation of whole-transcriptome effects of chemical exposure. The application to experimental and simulated datasets proved that the method can effectively discriminate different levels of altered transcriptomic responses when compared to expert judgement (Spearman correlation coefficient of 0.96). A further application to data collected in two independent studies of Salmo trutta and Xenopus tropicalis exposed to contaminants confirmed the potential extension of the methodology to other aquatic species. This methodology can serve as a proof of concept for the integration of "genomic tools" in environmental risk assessment based on multidisciplinary investigations. To this end, the proposed transcriptomic hazard index can now be incorporated into quantitative Weight of Evidence approaches and weighed, with results from other types of analysis, to elucidate the role of chemicals in adverse ecological effects.

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Species referenced: Xenopus tropicalis

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	Graphical abstract
	Fig. 1. Plot depicting the Hallmark gene sets selected for this study, grouped together in eight higher categories. Percentages reflect the number of genes involved in each category for bivalves. The list of gene sets is reported also in SI (Table S1).
	Fig. 2. Flowchart for the calculations of the Transcriptomics Hazard Index.
	Fig. 3. Correlations between the THI index and different transcriptomic metrics from the experimental datasets presented in Table 2: a) THI and number of DEGs; b) number of significant GSEA pathways and number of DEGs; c) number of significant GSEA pathways and THI; d) mean ES and THI. Solid blue line represents the regression line with its confidence interval as shaded grey area. Correlation test was assessed by means of Spearman's rank correlation coefficient (S). Additional statistical details are depicted in the subtitle of each plot: "p" denotes the significance of the correlation, the effect size and confidence interval are identified by "ρ" and "CI95%", while "n" indicates the number of observations. Quantities on axes are all nondimensional.
	Fig. 4. Comparison of THI values with the averaged expert qualitative evaluation converted into nominal scale from 1 (absent hazard) to 5 (severe hazard). Error bars give an indication of experts' variability in attributing a class of hazard. The red line represents complete correlation between the two variables, while the observed Spearman correlation coefficient is reported above the graph.