DNA framework-based molecular transformer for logic-driven precision diagnostics.

Area of Science:

• Biotechnology
• Molecular Diagnostics
• Synthetic Biology

Background:

• Precision diagnostics face challenges integrating diverse biomarkers due to molecular heterogeneity and inconsistent identification kinetics.
• Robust logic analysis for complex biomarker data remains a significant hurdle in molecular diagnostics.

Purpose of the Study:

• To develop a universal molecular transformer for normalizing diverse biomarker information.
• To enable programmable molecular decision-making using Boolean logic gates for enhanced diagnostics.

Main Methods:

• Utilized DNA tetrahedral frameworks (DTFs) as programmable scaffolds for molecular transformation.
• Converted diverse biomarkers (proteins, nucleic acids) into uniform 22-nucleotide DNA sequences.
• Implemented Boolean logic gates (AND, OR, INHIBIT) for molecular computation on a gold-chip platform.

Main Results:

• Achieved statistically significant discrimination between prostate cancer, benign prostatic hyperplasia, and healthy individuals (P < 0.0001).
• Successfully utilized only two biomarkers (prostate-specific antigen and ALU115) for accurate classification.
• Demonstrated the system's capability to unify signal transformation for Boolean logic computation.

Conclusions:

• The DTF-based molecular transformer resolves critical challenges in molecular heterogeneity for diagnostics.
• Established a versatile platform for next-generation precision diagnostics with programmable molecular logic.
• This approach offers a novel strategy for complex biomarker data integration and analysis.