PROTEOMAS: a workflow enabling harmonized proteomic meta-analysis and proteomic signature mapping
Aileen Bahl1, Celine Ibrahim1, Kristina Plate1
1Department of Chemicals and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany.
Abstract:
Toxicological evaluation of substances in regulation still often relies on animal experiments. Understanding the substances' mode-of-action is crucial to develop alternative test strategies. Omics methods are promising tools to achieve this goal. Until now, most attention was focused on transcriptomics, while proteomics is not yet routinely applied in toxicology despite the large number of datasets available in public repositories. Exploiting the full potential of these datasets is hampered by differences in measurement procedures and follow-up data processing. Here we present the tool PROTEOMAS, which allows meta-analysis of proteomic data from public origin. The workflow was designed for analyzing proteomic studies in a harmonized way and to ensure transparency in the analysis of proteomic data for regulatory purposes. It agrees with the Omics Reporting Framework guidelines of the OECD with the intention to integrate proteomics to other omic methods in regulatory toxicology. The overarching aim is to contribute to the development of AOPs and to understand the mode of action of substances. To demonstrate the robustness and reliability of our workflow we compared our results to those of the original studies. As a case study, we performed a meta-analysis of 25 proteomic datasets to investigate the toxicological effects of nanomaterials at the lung level. PROTEOMAS is an important contribution to the development of alternative test strategies enabling robust meta-analysis of proteomic data. This workflow commits to the FAIR principles (Findable, Accessible, Interoperable and Reusable) of computational protocols.
Insights
A new tool, PROTEOMAS, enables harmonized analysis of public proteomics data for regulatory toxicology. This supports developing alternative testing strategies and understanding substance mechanisms of action, reducing animal testing.
Area of Science:
- Toxicology
- Proteomics
- Computational Biology
Background:
- Regulatory toxicology heavily relies on animal testing, necessitating alternative strategies.
- Omics methods, particularly proteomics, offer potential for understanding substance mechanisms but face data integration challenges.
- Publicly available proteomics datasets are underutilized due to inconsistent data processing.
Purpose of the Study:
- To introduce PROTEOMAS, a workflow for harmonized meta-analysis of public proteomics data.
- To facilitate the integration of proteomics into regulatory toxicology and support the development of Adverse Outcome Pathways (AOPs).
- To enhance transparency and reliability in analyzing proteomic data for regulatory decision-making.
Main Methods:
- Development of the PROTEOMAS workflow for standardized proteomic data analysis.
- Adherence to OECD Omics Reporting Framework guidelines.
- Meta-analysis of 25 proteomic datasets investigating nanomaterial lung toxicity as a case study.
Main Results:
- Demonstrated robustness and reliability of the PROTEOMAS workflow through comparison with original study findings.
- Successful meta-analysis of diverse proteomic datasets, yielding insights into toxicological effects.
- The workflow adheres to FAIR data principles, promoting data accessibility and reusability.
Conclusions:
- PROTEOMAS provides a robust solution for meta-analysis of proteomic data, advancing alternative testing strategies in toxicology.
- The tool contributes to understanding substance mechanisms of action and developing AOPs.
- Standardized analysis of proteomics data supports regulatory toxicology and reduces reliance on animal experiments.
