4.8 Article

An Engineered Kinetic Amplification Mechanism for Single Nucleotide Variant Discrimination by DNA Hybridization Probes

期刊

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
卷 138, 期 15, 页码 5076-5086

出版社

AMER CHEMICAL SOC
DOI: 10.1021/jacs.6b00277

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资金

  1. NSF CAREER Award [CBET 0954566]
  2. NSF [CCF 1317653]
  3. DARPA Young Faculty Award [YFA N66001-12-1-4225]
  4. Direct For Computer & Info Scie & Enginr
  5. Division of Computing and Communication Foundations [1317694, 1317653] Funding Source: National Science Foundation
  6. Div Of Chem, Bioeng, Env, & Transp Sys
  7. Directorate For Engineering [0954566] Funding Source: National Science Foundation

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Even a single-nucleotide difference between the sequences of two otherwise identical biological nucleic acids can have dramatic functional consequences. Here, we use model-guided reaction pathway engineering to quantitatively improve the performance of selective hybridization probes in recognizing single nucleotide variants (SNVs). Specifically, we build a detection system that combines discrimination by competition with DNA strand displacement-based catalytic amplification. We show, both mathematically and experimentally, that the single nucleotide selectivity of such a system in binding to single-stranded DNA and RNA is quadratically better than discrimination due to competitive hybridization alone. As an additional benefit the integrated circuit inherits the property of amplification and provides at least 10-fold better sensitivity than standard hybridization probes. Moreover, we demonstrate how the detection mechanism can be tuned such that the detection reaction is agnostic to the position of the SNV within the target sequence. in contrast, prior, strand displacement-based probes designed for kinetic discrimination are highly sensitive to position effects. We apply our system to reliably discriminate between different members of the let-7 microRNA family that differ in only a single base position. Our results demonstrate the power of systematic reaction network design to quantitatively improve biotechnology.

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