Hybridization chain reactions targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Tzu Heng Wu, Chia Chen Chang, Ching Hsu Yang, Wei Yin Lin, Tan Joy Ee, Chii Wann Lin*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

18 Scopus citations

Abstract

In this work, hybridization chain reactions (HCRs) toward Severe Acute Respiratory Syndrome Coronavirus 2 (SARS–CoV-2) nucleocapsid phosphoproteins gene loci and human RNase P are proposed to provide an isothermal amplification screening tool. The proposed chain reactions target the complementary DNA (cDNA) of SARS–CoV-2, with loci corresponding to gold-standard polymerase chain reaction (PCR) loci. Four hybridization chain reaction reactions are demonstrated herein, targeting N1/N2/N3 loci and human RNase P. The design of the hybridization chain reaction, herein, is assisted with an algorithm. The algorithm helps to search target sequences with low local secondary structure and high hybridization efficiency. The loop domain of the fuel hairpin molecule H1 and H2, which are the tunable segments in such reactions, are used as an optimization parameter to improve the hybridization efficiency of the chain reaction. The algorithm-derived HCR reactions were validated with gel electrophoresis. All proposed reactions exhibit a hybridization complex with a molecular mass >1.5k base pairs, which is clear evidence of chain reaction. The hybridization efficiency trend revealed by gel electrophoresis corresponds nicely to the simulated data from the algorithm. The HCR reactions and the corresponding algorithm serve as a basis to further SARS–CoV-2 sensing applications and facilitate better screening strategies for the prevention of on-going pandemics.

Original languageEnglish
Article number3216
JournalInternational Journal of Molecular Sciences
Volume21
Issue number9
DOIs
StatePublished - 02 05 2020

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Algorithm
  • CoV-2
  • Hybridization chain reaction
  • SARS

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