Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41467-018-05733-0
Title: Visual and modular detection of pathogen nucleic acids with enzyme–DNA molecular complexes
Authors: Ho, N.R.Y
Lim, G.S
Sundah, N.R
Lim, D
Loh, T.P 
Shao, H 
Keywords: DNA
enzyme
genomic DNA
nucleic acid
virus DNA
virus RNA
DNA
horseradish peroxidase
nanomaterial
nucleic acid
cell
detection method
DNA
enzyme
genome
molecular analysis
nanoparticle
nucleic acid
pathogen
Article
controlled study
DNA conformation
DNA structure
enzyme activity
false positive result
female
gene locus
human
human cell
Human papillomavirus type 16
Human papillomavirus type 18
limit of detection
microfluidics
molecular recognition
molecular typing
nonhuman
nucleic acid analysis
polymerase chain reaction
signal detection
virus genome
bioassay
chemistry
genetics
isolation and purification
metabolism
Papillomaviridae
Biological Assay
DNA
Horseradish Peroxidase
Humans
Nanostructures
Nucleic Acids
Papillomaviridae
Issue Date: 2018
Publisher: Nature Publishing Group
Citation: Ho, N.R.Y, Lim, G.S, Sundah, N.R, Lim, D, Loh, T.P, Shao, H (2018). Visual and modular detection of pathogen nucleic acids with enzyme–DNA molecular complexes. Nature Communications 9 (1) : 3238. ScholarBank@NUS Repository. https://doi.org/10.1038/s41467-018-05733-0
Abstract: Rapid, visual detection of pathogen nucleic acids has broad applications in infection management. Here we present a modular detection platform, termed enzyme-assisted nanocomplexes for visual identification of nucleic acids (enVision). The system consists of an integrated circuit of enzyme–DNA nanostructures, which function as independent recognition and signaling elements, for direct and versatile detection of pathogen nucleic acids from infected cells. The built-in enzymatic cascades produce a rapid color readout for the naked eye; the assay is thus fast (<2 h), sensitive (<10 amol), and readily quantified with smartphones. When implemented on a configurable microfluidic platform, the technology demonstrates superior programmability to perform versatile computations, for detecting diverse pathogen targets and their virus–host genome integration loci. We further design the enVision platform for molecular-typing of infections in patient endocervical samples. The technology not only improves the clinical inter-subtype differentiation, but also expands the intra-subtype coverage to identify previously undetectable infections. © 2018, The Author(s).
Source Title: Nature Communications
URI: https://scholarbank.nus.edu.sg/handle/10635/174207
ISSN: 2041-1723
DOI: 10.1038/s41467-018-05733-0
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