Programmable DNAzyme switch integrated with low-background cross-reaction for sensitive and selective SNP...

Area of Science:

• Biotechnology and Molecular Diagnostics
• Genetics and Genomics
• Biosensing Technologies

Background:

• Single-nucleotide polymorphism (SNP) detection is crucial for disease screening, personalized medicine, and crop improvement.
• DNAzymes offer sequence programmability and catalytic activity but suffer from limited selectivity and high nonspecific reactivity for precise SNP discrimination.
• Existing enzyme-free methods often face stoichiometric limitations, impacting target selectivity.

Purpose of the Study:

• To overcome the limitations of current DNAzyme-based SNP detection systems, particularly concerning sequence selectivity and nonspecific reactivity.
• To develop a highly sensitive and selective enzyme-free platform for SNP recognition and genotyping.
• To enhance analytical sensitivity and address the trade-off between selectivity and sensitivity in biosensing.

Main Methods:

• Redesigned the DNAzyme catalytic core to create a competitive molecular switch with an 'activation-silencing' mechanism for single-base specificity.
• Implemented a protein-enzyme-free strategy for SNP recognition, breaking conventional stoichiometric paradigms.
• Introduced a solid-liquid phase cross-reaction mechanism and a cascade system with electrochemical biosensing to boost analytical sensitivity.

Main Results:

• Achieved highly selective SNP recognition with enhanced target selectivity compared to conventional methods.
• Demonstrated a significantly improved detection limit of 11.3 aM for SNPs, surpassing conventional sensors (370 aM).
• Showcased high consistency in genotyping soybean variants, validating the platform's practical applicability.

Conclusions:

• The developed strategy provides an innovative, enzyme-free, and scalable platform for SNP sensing and signal regulation.
• This work offers new concepts for precision genotyping and molecular diagnostics by improving single-base recognition and signal transduction.
• The approach effectively addresses the bottleneck of single-base specificity and enhances detection sensitivity, paving the way for advanced diagnostic tools.