Cycle Threshold (Ct) Values of SARS-CoV-2 Detected with the GeneXpert® System and a Mutation Associated with Different Target Gene Failure

Yamashita, Katsushi; Taniguchi, Terumi; Niizeki, Noriyasu; Nagao, Yuki; Suzuki, Akira; Toguchi, Akihiro; Takebayashi, Shiori; Ishikawa, Jinko; Nagura, Osanori; Furuhashi, Kazuki; Iwaizumi, Moriya; Maekawa, Masato

doi:10.3390/cimb45050262

Cited by 2 publications

References 34 publications

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Viral Genome Surveillance via Modifiable Microarray Sequencing and a Supervised Stack Ensemble Neural Network Model: SARS-CoV-2 as a Case Study

Plackowski,

Ebinumoliseh,

Ogbuewu

et al. 2024

Preprint

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Background Viral outbreaks, including Dengue, Zika, Ebola, and particularly SARS-CoV-2, have caused significant global impacts and unprecedented losses of life. SARS-CoV-2, in particular, continues to be a leading cause of death worldwide and in the United States, with many individuals experiencing prolonged symptoms. In this study, we present a novel genomic surveillance approach that combines a stack-ensembled neural network and microarray genome resequencing by hybridization. Results The resequencing microarray features ~ 240,000 probes for approximately 30,000 nucleotides per genomic sample. The data utilized were derived from our previously reported cost-effective and rapid full-genome tiling array technology. Our base-calling algorithms were enhanced with 48 input features per base position and multiple scanning exposure times. The training dataset included 570,000 data points from which over 12,000 neural network models were developed. To assess the accuracy of our stack-ensembled models in base-calling and variant identification, we analyzed genomic data from four clinical samples with a cycle threshold value ≤ 24 via neural network and logistic regression meta-models. Conclusions Our models demonstrated accuracies exceeding 99% and coverages comparable to existing standards. Microarray genome resequencing of clinical viral samples provides significant benefits in terms of cost-effectiveness, speed, and flexibility, allowing for the surveillance of diverse viral genomes without the need for extensive algorithm retraining.

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Viral Genome Surveillance via Modifiable Microarray Sequencing and a Supervised Stack Ensemble Neural Network Model: SARS-CoV-2 as a Case Study

Plackowski,

Ebinumoliseh,

Ogbuewu

et al. 2024

Preprint

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SARS-CoV-2 Nucleocapsid Protein Mutations Found in Switzerland Disrupt N-Gene Amplification in Commonly Used Multiplex RT-PCR Assay

Hilti,

Wehrli,

Roditscheff

et al. 2023

Pathogens

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At the end of 2021, we observed an increase in N-gene target failures (NGTF) with the TaqPathTM COVID-19 CE-IVD RT-PCR Kit from Thermo Fisher Scientific (TaqPath). We subsequently used whole-genome sequencing (Oxford Nanopore Technology) to identify potential issues with N-gene PCR efficacy. Among 168,101 positive samples with a cycle threshold (CT) value <30 from August 2021 to May 2022, 194 specimens without N-gene amplification by PCR were identified (0.12%). Most NGTF samples originated from a wave of infection attributable to the Delta variant (B.1.617.2) and its sublineages. Sequencing revealed the nucleotide substitution G28922T (A217S) in 151 samples (88.8%). The substitution G215C, a hallmark mutation for Delta lineages, was concurrently present in all of these samples. Ten samples (5.9%) carried the deletion 28,913–28,918 (del214/215), eight samples (4.7%) the deletion 28,913–28,915 (del214) and one sample (0.6%) the deletion 28,892–28,930 (del207–219). Samples showing intact N-gene amplification by PCR lacked these specific mutations, but delayed-type amplification (i.e., partial or pNGTF) was attributable to the exclusive presence of A217S. As the N gene is a common target in many RT-PCR methods for SARS-CoV-2, an in-depth analysis of single-target failures using a combination with viral whole genome sequencing may allow for the identification of diagnostic flaws and eventual new variants.

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Cycle Threshold (Ct) Values of SARS-CoV-2 Detected with the GeneXpert® System and a Mutation Associated with Different Target Gene Failure

Cited by 2 publications

References 34 publications

Viral Genome Surveillance via Modifiable Microarray Sequencing and a Supervised Stack Ensemble Neural Network Model: SARS-CoV-2 as a Case Study

Viral Genome Surveillance via Modifiable Microarray Sequencing and a Supervised Stack Ensemble Neural Network Model: SARS-CoV-2 as a Case Study

SARS-CoV-2 Nucleocapsid Protein Mutations Found in Switzerland Disrupt N-Gene Amplification in Commonly Used Multiplex RT-PCR Assay

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