Scientific Reports (Dec 2022)

Monitoring SARS-CoV-2 variant transitions using differences in diagnostic cycle threshold values of target genes

  • Antoni E. Bordoy,
  • Verónica Saludes,
  • David Panisello Yagüe,
  • Gemma Clarà,
  • Laia Soler,
  • Alexia Paris de León,
  • Cristina Casañ,
  • Ana Blanco-Suárez,
  • Mercedes Guerrero-Murillo,
  • Beatriz Rodríguez-Ponga,
  • Marc Noguera-Julian,
  • Francesc Català-Moll,
  • Irina Pey,
  • Maria Pilar Armengol,
  • Maria Casadellà,
  • Mariona Parera,
  • Raquel Pluvinet,
  • Lauro Sumoy,
  • Bonaventura Clotet,
  • Montserrat Giménez,
  • Elisa Martró,
  • Pere-Joan Cardona,
  • Ignacio Blanco

DOI
https://doi.org/10.1038/s41598-022-25719-9
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 7

Abstract

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Abstract Monitoring the emergence of new SARS-CoV-2 variants is important to detect potential risks of increased transmission or disease severity. We investigated the identification of SARS-CoV-2 variants from real-time reverse transcriptase polymerase chain reaction (RT-PCR) routine diagnostics data. Cycle threshold (Ct) values of positive samples were collected from April 2021 to January 2022 in the Northern Metropolitan Area of Barcelona (n = 15,254). Viral lineage identification from whole genome sequencing (WGS) was available for 4618 (30.3%) of these samples. Pairwise differences in the Ct values between gene targets (ΔCt) were analyzed for variants of concern or interest circulating in our area. A specific delay in the Ct of the N-gene compared to the RdRp-gene (ΔCtNR) was observed for Alpha, Delta, Eta and Omicron. Temporal differences in ΔCtNR correlated with the dynamics of viral replacement of Alpha by Delta and of Delta by Omicron according to WGS results. Using ΔCtNR, prediction of new variants of concern at early stages of circulation was achieved with high sensitivity and specificity (91.1% and 97.8% for Delta; 98.5% and 90.8% for Omicron). Thus, tracking population-wide trends in ΔCt values obtained from routine diagnostics testing in combination with WGS could be useful for real-time management and response to local epidemics.