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Issue 15, 2020
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Sensitive determination of formamidopyrimidine DNA glucosylase based on phosphate group-modulated multi-enzyme catalysis and fluorescent copper nanoclusters

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Abstract

In this work, a method for quantifying the activity of formamidopyrimidine DNA glucosylase (Fpg) was designed based on phosphate group (P)-modulated multi-enzyme catalysis and fluorescent copper nanoclusters (CuNCs). By eliminating 8-oxoguanine from double-stranded DNA, Fpg generates a nick with P at both 3′ and 5′ termini. Subsequently, part of the DNA is digested by 5′P-activated lambda exonuclease (λ Exo), and the generated 3′P disables exonuclease I (Exo I), resulting in the generation of single-stranded DNA containing poly(thymine) (poly(T)). Using poly(T) as templates, CuNCs were prepared to emit intense fluorescence as the readout of this method. However, in the absence of Fpg, the originally modified 5′P triggers the digestion of λ Exo. In this case, fluorescence emission is not obtained because CuNCs cannot be formed without DNA templates. Therefore, the catalysis of λ Exo and Exo I can be tuned by 5′P and 3′P, which can be further used to determine the activity of Fpg. The fluorescent Fpg biosensor works in a “signal-on” manner with the feature of “zero” background noise, and thus shows desirable analytical features and good performance. Besides, Fpg in serum samples and cell lysate could be accurately detected with the biosensor, indicating the great value of the proposed system in practical and clinical analysis.

Graphical abstract: Sensitive determination of formamidopyrimidine DNA glucosylase based on phosphate group-modulated multi-enzyme catalysis and fluorescent copper nanoclusters

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Supplementary files

Article information


Submitted
08 May 2020
Accepted
16 Jun 2020
First published
17 Jun 2020

Analyst, 2020,145, 5174-5179
Article type
Paper

Sensitive determination of formamidopyrimidine DNA glucosylase based on phosphate group-modulated multi-enzyme catalysis and fluorescent copper nanoclusters

J. Li, M. Zhang, H. Wang, J. Wu, R. Zheng, J. Zhang, Y. Li, Z. Wang and Z. Dai, Analyst, 2020, 145, 5174
DOI: 10.1039/D0AN00928H

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