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
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.

Auteurs

Krishna Gupta (K)

Institute for Biofunctional Polymer Materials, Leibniz Institute of Polymer Research Dresden, Dresden, Germany.
Faculty of Chemistry and Food Chemistry, TU Dresden, Dresden, Germany.

Elisha Krieg (E)

Institute for Biofunctional Polymer Materials, Leibniz Institute of Polymer Research Dresden, Dresden, Germany.
Faculty of Chemistry and Food Chemistry, TU Dresden, Dresden, Germany.

Classifications MeSH