SOLA: dissecting dose-response patterns in multi-omics data using a semi-supervised workflow.

Area of Science:

• Ecotoxicology
• Systems Biology
• Bioinformatics

Background:

• Omics data are increasingly used in ecotoxicology to study environmental stressor effects.
• Investigating complex non-monotonic dose-response patterns with multi-omics data remains challenging.

Purpose of the Study:

• To develop a novel semi-supervised network analysis workflow (SOLA) as an alternative to benchmark dose (BMD) modeling.
• To analyze multi-omics data from *Daphnia magna* exposed to chronic gamma radiation.
• To gain new insights into the dose-dependent effects of radiation.

Main Methods:

• Unsupervised co-expression network analysis to group genes by dose response.
• Supervised classification of gene modules.
• Regulatory network reconstruction using transcription factor binding motifs.
• Differential co-expression network analysis across datasets.
• Pathway enrichment analysis integrating transcriptomics and metabolomics data.

Main Results:

• The SOLA workflow identified known and novel effects of gamma radiation on *Daphnia magna*.
• The analysis revealed shifts in biological responses across different radiation dose rates.
• The study provides a new approach for multi-omics dose-response analysis.

Conclusions:

• The developed SOLA workflow offers a powerful alternative for analyzing complex multi-omics dose-response data.
• This approach enhances understanding of environmental stressor impacts at a mechanistic level.
• SOLA can be applied to future ecotoxicological studies involving various stressors and omics datasets.