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    Probing the mutation independent interaction of DNA probes with SARS-CoV-2 variants through a combination of surface-enhanced Raman scattering and machine learning

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    Author
    Moitra, Parikshit
    Chaichi, Ardalan
    Abid Hasan, Syed Mohammad
    Dighe, Ketan
    Alafeef, Maha
    Prasad, Alisha
    Gartia, Manas Ranjan
    Pan, Dipanjan
    Date
    2022-03-22
    Journal
    Biosensors & Bioelectronics
    Type
    Article
    
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    See at
    https://doi.org/10.1016/j.bios.2022.114200
    Abstract
    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution has been characterized by the emergence of sets of mutations impacting the virus characteristics, such as transmissibility and antigenicity, presumably in response to the changing immune profile of the human population. The presence of mutations in the SARS-CoV-2 virus can potentially impact therapeutic and diagnostic test performances. We design and develop here a unique set of DNA probes i.e., antisense oligonucleotides (ASOs) which can interact with genetic sequences of the virus irrespective of its ongoing mutations. The probes, developed herein, target a specific segment of the nucleocapsid phosphoprotein (N) gene of SARS-CoV-2 with high binding efficiency which do not mutate among the known variants. Further probing into the interaction profile of the ASOs reveals that the ASO-RNA hybridization remains unaltered even for a hypothetical single point mutation at the target RNA site and diminished only in case of the hypothetical double or triple point mutations. The mechanism of interaction among the ASOs and SARS-CoV-2 RNA is then explored with a combination of surface-enhanced Raman scattering (SERS) and machine learning techniques. It has been observed that the technique, described herein, could efficiently discriminate between clinically positive and negative samples with ∼100% sensitivity and ∼90% specificity up to 63 copies/mL of SARS-CoV-2 RNA concentration. Thus, this study establishes N gene targeted ASOs as the fundamental machinery to efficiently detect all the current SARS-CoV-2 variants regardless of their mutations.
    Rights/Terms
    Copyright © 2022 Elsevier B.V. All rights reserved.
    Keyword
    SARS-CoV-2 variants
    surface-enhanced Raman scattering
    selective and ultrasensitive diagnosis
    mutation resistant probe
    Oligonucleotides, Antisense
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/18539
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.bios.2022.114200
    Scopus Count
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    UMB Coronavirus Publications
    UMB Open Access Articles

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