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Combining time-resolved transcriptomics and proteomics data for Adverse Outcome Pathway refinement in ecotoxicology
Ruben Bakker1, Jacintha Ellers1, Dick Roelofs2
1Amsterdam Institute for Life and Environment (A-LIFE), Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, the Netherlands.
Environmental risk assessment benefits from Adverse Outcome Pathways (AOPs). Multi-omics data, like transcriptomics and proteomics, show synchronized responses to imidacloprid in soil invertebrates, aiding AOP development.
Area of Science:
- Environmental toxicology
- Ecotoxicology
- Environmental chemistry
Background:
- Conventional Environmental Risk Assessment (ERA) relies on soil concentrations and apical endpoints, often overlooking intermediate biological responses.
- The Adverse Outcome Pathway (AOP) framework integrates multi-level biological responses, offering predictive insights into adverse outcomes.
- Integrating transcriptomic and proteomic data is crucial for a comprehensive understanding of toxicological response cascades and AOP development.
Approach:
- Investigated time-resolved transcriptomic and proteomic shifts in the springtail Folsomia candida following exposure to the neonicotinoid insecticide imidacloprid.
- Analyzed multi-omics data at 12-hour intervals up to 72 hours post-exposure to assess temporal dynamics.
- Employed cross-correlation analyses to determine the synchronization between gene transcript and protein abundance changes.
Key Points:
- The most significant shifts in both transcript and protein abundances occurred at 48 hours post-imidacloprid exposure.
- Cross-correlation analysis revealed that transcript and protein abundances for most genes were highly correlated without a significant time lag.
- This indicates that simultaneous multi-omics data collection is effective for capturing synchronized response cascades.
Conclusions:
- Combined analysis of transcriptomic and proteomic data from the same time-point can significantly enhance AOP development.
- The findings support the use of synchronized multi-omics data for improving predictive models in environmental risk assessment.
- This research facilitates the development of novel biomarkers for detecting neonicotinoid insecticides and similar chemicals in soil ecosystems.
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