Abstract

Multiplexing and quantification of nucleic acids, both have, in their own right, significant and extensive use in biomedical related fields. Currently, the ability to detect several nucleic acid targets in a single-reaction scales linearly with the number of targets; an expensive and time-consuming feat. Here, we propose a new methodology based on multidimensional standard curves that extends the use of real-time PCR data obtained by common qPCR instruments. By applying this novel methodology, we achieve simultaneous single-channel multiplexing and enhanced quantification of multiple targets using only real-time amplification data. This is obtained without the need of fluorescent probes, agarose gels, melting curves or sequencing analysis. Given the importance and demand for tackling challenges in antimicrobial resistance, the proposed method is applied to four of the most prominent carbapenem-resistant genes: blaOXA-48, blaNDM, blaVIM, and blaKPC, which account for 97% of the UK’s reported carbapenemase-producing Enterobacteriaceae.

Highlights

  • This work demonstrates simultaneous multiplex qPCR and absolute quantification using standard curves, employing only a single fluorescence channel without post-PCR analysis; such that it can be used with conventional qPCR instruments

  • The proposed method is validated for the detection of the β-lactamase genes blaOXA‐48, blaNDM, blaVIM, and blaKPC using bacterial isolates from clinical samples in a single-reaction; without fluorescent probes, agarose gels, melting curves or sequencing analysis

  • Each amplification reaction was performed equipment to be used as a screening tool in healthcare systems; and (iii) multiplexed nucleic acid amplification techniques (NAATs) have not been able to meet the can be successfully adopted within existing healthcare settings

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Summary

■ RESULTS AND DISCUSSION

It is shown that simultaneous enhanced quantification and multiplexing of blaOXA‐48, blaNDM, blaVIM, and blaKPC β-lactamase genes in bacterial isolates can be achieved by using multidimensional standard curves constructed using fluorescent amplification curves in qPCR. Moniri et al.[1] shows that considering multiple features contains sufficient information gain in order to discriminate outliers from a specific target using a MSC. Average Mahalanobis distance between multidimensional standard curves and sample test points (bacterial isolates) used for target identification. Shows the average figure of merit (with standard deviation and p-values) for each target using the three chosen features (Ct, Cy, and −log10(F0)) and M0. This is expected given the nature of the multidimensional framework as M0 is constructed using a linear combination of the other features in order to minimize the average figure of merit. M0 provides an automated solution for quantification that is robust in the sense that it will always be the best performing method

■ CONCLUSION
■ ACKNOWLEDGMENTS
■ REFERENCES
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