Y-switch: a spring-loaded synthetic gene switch for robust DNA/RNA signal amplification and detection.
Journal
Nucleic acids research
ISSN: 1362-4962
Titre abrégé: Nucleic Acids Res
Pays: England
ID NLM: 0411011
Informations de publication
Date de publication:
16 Aug 2024
16 Aug 2024
Historique:
accepted:
31
07
2024
revised:
04
07
2024
received:
29
02
2024
medline:
16
8
2024
pubmed:
16
8
2024
entrez:
16
8
2024
Statut:
aheadofprint
Résumé
Nucleic acid tests (NATs) are essential for biomedical diagnostics. Traditional NATs, often complex and expensive, have prompted the exploration of toehold-mediated strand displacement (TMSD) circuits as an economical alternative. However, the wide application of TMSD-based reactions is limited by 'leakage'-the spurious activation of the reaction leading to high background signals and false positives. Here, we introduce the Y-Switch, a new TMSD cascade design that recognizes a custom nucleic acid input and generates an amplified output. The Y-Switch is based on a pair of thermodynamically spring-loaded DNA modules. The binding of a predefined nucleic acid target triggers an intermolecular reaction that activates a T7 promoter, leading to the perpetual transcription of a fluorescent aptamer that can be detected by a smartphone camera. The system is designed to permit the selective depletion of leakage byproducts to achieve high sensitivity and zero-background signal in the absence of the correct trigger. Using Zika virus (ZIKV)- and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-derived nucleic acid sequences, we show that the assay generates a reliable target-specific readout. Y-Switches detect native RNA under isothermal conditions without reverse transcription or pre-amplification, with a detection threshold as low as ∼200 attomole. The modularity of the assay allows easy re-programming for the detection of other targets by exchanging a single sequence domain. This work provides a low-complexity and high-fidelity synthetic biology tool for point-of-care diagnostics and for the construction of more complex biomolecular computations.
Identifiants
pubmed: 39149901
pii: 7734162
doi: 10.1093/nar/gkae680
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : BMBF
Organisme : NanoMatFutur
ID : 13XP5098
Informations de copyright
© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.