Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates
Rapid and accurate identification of patients colonised with carbapenemase-producing organisms (CPOs) is essential to adopt prompt prevention measures to reduce the risk of transmission. Recent studies have demonstrated the ability to combine machine learning (ML) algorithms with real-time digital P...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:0a7847a7d8ce458e8ac77c290dce86032021-11-30T12:41:03ZCoupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates2296-889X10.3389/fmolb.2021.775299https://doaj.org/article/0a7847a7d8ce458e8ac77c290dce86032021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmolb.2021.775299/fullhttps://doaj.org/toc/2296-889XRapid and accurate identification of patients colonised with carbapenemase-producing organisms (CPOs) is essential to adopt prompt prevention measures to reduce the risk of transmission. Recent studies have demonstrated the ability to combine machine learning (ML) algorithms with real-time digital PCR (dPCR) instruments to increase classification accuracy of multiplex PCR assays when using synthetic DNA templates. We sought to determine if this novel methodology could be applied to improve identification of the five major carbapenem-resistant genes in clinical CPO-isolates, which would represent a leap forward in the use of PCR-based data-driven diagnostics for clinical applications. We collected 253 clinical isolates (including 221 CPO-positive samples) and developed a novel 5-plex PCR assay for detection of blaIMP, blaKPC, blaNDM, blaOXA-48, and blaVIM. Combining the recently reported ML method “Amplification and Melting Curve Analysis” (AMCA) with the abovementioned multiplex assay, we assessed the performance of the AMCA methodology in detecting these genes. The improved classification accuracy of AMCA relies on the usage of real-time data from a single-fluorescent channel and benefits from the kinetic/thermodynamic information encoded in the thousands of amplification events produced by high throughput real-time dPCR. The 5-plex showed a lower limit of detection of 10 DNA copies per reaction for each primer set and no cross-reactivity with other carbapenemase genes. The AMCA classifier demonstrated excellent predictive performance with 99.6% (CI 97.8–99.9%) accuracy (only one misclassified sample out of the 253, with a total of 160,041 positive amplification events), which represents a 7.9% increase (p-value <0.05) compared to conventional melting curve analysis. This work demonstrates the use of the AMCA method to increase the throughput and performance of state-of-the-art molecular diagnostic platforms, without hardware modifications and additional costs, thus potentially providing substantial clinical utility on screening patients for CPO carriage.Luca MigliettaLuca MigliettaAhmad MoniriIvana PennisiKenny Malpartida-CardenasHala AbbasKerri Hill-CawthorneFrances BoltElita JauneikaiteElita JauneikaiteFrances DaviesFrances DaviesAlison HolmesAlison HolmesPantelis GeorgiouJesus Rodriguez-ManzanoFrontiers Media S.A.articledigital PCR (dPCR)infectious diseasemoleuclar diagnosticsdata driven (DD)real-time PCRBiology (General)QH301-705.5ENFrontiers in Molecular Biosciences, Vol 8 (2021) |
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digital PCR (dPCR) infectious disease moleuclar diagnostics data driven (DD) real-time PCR Biology (General) QH301-705.5 |
| spellingShingle |
digital PCR (dPCR) infectious disease moleuclar diagnostics data driven (DD) real-time PCR Biology (General) QH301-705.5 Luca Miglietta Luca Miglietta Ahmad Moniri Ivana Pennisi Kenny Malpartida-Cardenas Hala Abbas Kerri Hill-Cawthorne Frances Bolt Elita Jauneikaite Elita Jauneikaite Frances Davies Frances Davies Alison Holmes Alison Holmes Pantelis Georgiou Jesus Rodriguez-Manzano Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates |
| description |
Rapid and accurate identification of patients colonised with carbapenemase-producing organisms (CPOs) is essential to adopt prompt prevention measures to reduce the risk of transmission. Recent studies have demonstrated the ability to combine machine learning (ML) algorithms with real-time digital PCR (dPCR) instruments to increase classification accuracy of multiplex PCR assays when using synthetic DNA templates. We sought to determine if this novel methodology could be applied to improve identification of the five major carbapenem-resistant genes in clinical CPO-isolates, which would represent a leap forward in the use of PCR-based data-driven diagnostics for clinical applications. We collected 253 clinical isolates (including 221 CPO-positive samples) and developed a novel 5-plex PCR assay for detection of blaIMP, blaKPC, blaNDM, blaOXA-48, and blaVIM. Combining the recently reported ML method “Amplification and Melting Curve Analysis” (AMCA) with the abovementioned multiplex assay, we assessed the performance of the AMCA methodology in detecting these genes. The improved classification accuracy of AMCA relies on the usage of real-time data from a single-fluorescent channel and benefits from the kinetic/thermodynamic information encoded in the thousands of amplification events produced by high throughput real-time dPCR. The 5-plex showed a lower limit of detection of 10 DNA copies per reaction for each primer set and no cross-reactivity with other carbapenemase genes. The AMCA classifier demonstrated excellent predictive performance with 99.6% (CI 97.8–99.9%) accuracy (only one misclassified sample out of the 253, with a total of 160,041 positive amplification events), which represents a 7.9% increase (p-value <0.05) compared to conventional melting curve analysis. This work demonstrates the use of the AMCA method to increase the throughput and performance of state-of-the-art molecular diagnostic platforms, without hardware modifications and additional costs, thus potentially providing substantial clinical utility on screening patients for CPO carriage. |
| format |
article |
| author |
Luca Miglietta Luca Miglietta Ahmad Moniri Ivana Pennisi Kenny Malpartida-Cardenas Hala Abbas Kerri Hill-Cawthorne Frances Bolt Elita Jauneikaite Elita Jauneikaite Frances Davies Frances Davies Alison Holmes Alison Holmes Pantelis Georgiou Jesus Rodriguez-Manzano |
| author_facet |
Luca Miglietta Luca Miglietta Ahmad Moniri Ivana Pennisi Kenny Malpartida-Cardenas Hala Abbas Kerri Hill-Cawthorne Frances Bolt Elita Jauneikaite Elita Jauneikaite Frances Davies Frances Davies Alison Holmes Alison Holmes Pantelis Georgiou Jesus Rodriguez-Manzano |
| author_sort |
Luca Miglietta |
| title |
Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates |
| title_short |
Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates |
| title_full |
Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates |
| title_fullStr |
Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates |
| title_full_unstemmed |
Coupling Machine Learning and High Throughput Multiplex Digital PCR Enables Accurate Detection of Carbapenem-Resistant Genes in Clinical Isolates |
| title_sort |
coupling machine learning and high throughput multiplex digital pcr enables accurate detection of carbapenem-resistant genes in clinical isolates |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/0a7847a7d8ce458e8ac77c290dce8603 |
| work_keys_str_mv |
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