Enhancing SARS-CoV‑2 Lineage Surveillance through the Integration of a Simple and Direct qPCR-Based Protocol Adaptation with Established Machine Learning Algorithms

• Published in: Analytical chemistry (Washington) Vol. 96; no. 46; pp. 18537 - 18544
• Main Authors: Aksenen, Cleber Furtado, Ferreira, Debora Maria Almeida, Jeronimo, Pedro Miguel Carneiro, Costa, Thais de Oliveira, de Souza, Ticiane Cavalcante, Lino, Bruna Maria Nepomuceno Sousa, Farias, Allysson Allan de, Miyajima, Fabio
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 19.11.2024
• Subjects: Algorithms; analytical chemistry; Assaying; Assessments; Brazil; Coronaviruses; Cost effectiveness; COVID-19; COVID-19 - diagnosis; COVID-19 - virology; data collection; DNA probes; DNA-directed RNA polymerase; Dyes; Electrophoresis; Epidemiology; gel electrophoresis; Gene deletion; Gene sequencing; Genomic analysis; Genomics; Humans; Learning algorithms; Machine Learning; monitoring; Next-generation sequencing; Observational learning; pandemic; Pandemics; pathogens; Polymerase chain reaction; Public health; quantitative polymerase chain reaction; rapid methods; Real-Time Polymerase Chain Reaction - methods; RNA; RNA, Viral - analysis; RNA, Viral - genetics; SARS-CoV-2 - genetics; SARS-CoV-2 - isolation & purification; Severe acute respiratory syndrome coronavirus 2; Support vector machines; Surveillance; Viral diseases; Whole genome sequencing; Brazil
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/acs.analchem.4c04492

Emerging and evolving Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) lineages, adapted to changing epidemiological conditions, present unprecedented challenges to global public health systems. Here, we introduce an adapted analytical approach that complements genomic sequencing, applying a cost-effective quantitative polymerase chain reaction (qPCR)-based assay. Viral RNA samples from SARS-CoV-2 positive cases detected by diagnostic laboratories or public health network units in Ceará, Brazil, were tracked for genomic surveillance and analyzed by using paired-end sequencing combined with integrative genomic analysis. Validation of a key structural variation was conducted with gel electrophoresis for the presence of a specific open reading frame 7a(ORF7a) gene deletion within the “BE.9” lineages tracked. The analytical innovation of our method is the optimization of a simple intercalating dye-based qPCR assay through repositioning primers from the ARTIC v4.1 amplicon panel to detect large molecular patterns. This assay distinguishes between “BE.9” and “non-BE.9” lineages, particularly BQ.1, without the need for expensive probes or sequencing. The protocol was validated against lineage predictions from next-generation sequencing (NGS) using 525 paired samples, achieving 93.3% sensitivity, 95.1% specificity, and 92.4% agreement, as measured by Cohen’s Kappa coefficient. Machine learning (ML) models were trained using the melting curves from intercalating dye-based qPCR of 1724 samples, enabling highly accurate lineage assignment. Among them, the support vector machine (SVM) model had the best performance and after fine-tuning showed ∼96.52% (333/345) accuracy in comparison to the test data set. Our integrated approach provides an adapted analytical method that is both cost-effective and scalable, suitable for rapid assessment of emerging variants, especially in resource-limited settings. In this work, the protocol is applied to improve the monitoring of SARS-CoV-2 sublineages but can be extended to track any key molecular signature, including large insertions and deletions (indels) commonly observed in pathogenic agent subtypes. By offering a complement to traditional sequencing methods and utilizing easily trainable machine learning algorithms, our methodology contributes to enhanced molecular surveillance strategies and supports global efforts in pandemic control.