Can GM soybean be reliably quantified after screening? A risk-based approach for optimizing GMO testing workflow.

Area of Science:

• Food Science
• Molecular Biology
• Biotechnology

Background:

• European Union (EU) regulations mandate testing for genetically modified organisms (GMOs) in food and feed.
• Current GMO testing workflows involve DNA extraction, screening, identification, and quantification.
• Low-level GMO presence can lead to false positives during screening, causing resource inefficiency.

Purpose of the Study:

• To develop a statistical framework to predict the presence of soybean GMO events.
• To optimize the GMO testing workflow by minimizing resource expenditure on non-quantifiable samples.
• To provide a proof of concept for statistically optimizing GMO testing compliance.

Main Methods:

• A statistical framework was developed using quantification cycle (Cq) values from real-time PCR.
• Analysis focused on the difference in Cq values (ΔCq) between specific screening elements (P35S, T-nos, CP4 epsps) and a reference gene (lectin).
• The approach was validated in-house using real-life and spiked soybean samples.

Main Results:

• The developed statistical framework successfully predicted the presence of soybean GMO events.
• The method demonstrated feasibility in distinguishing between quantifiable and non-quantifiable GMO positives.
• In-house verification confirmed the approach's effectiveness on various sample types.

Conclusions:

• The statistical framework offers a method to optimize GMO testing workflows.
• This approach can significantly reduce time and resources in laboratories by preemptively identifying potentially non-quantifiable samples.
• The study provides a foundation for statistically driven optimization in GMO compliance testing.