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Molecular identification of SARS-CoV-2 variants of concern at urban wastewater treatment plants across South Africa
Preprint
in En
| PREPRINT-MEDRXIV
| ID: ppmedrxiv-22283506
ABSTRACT
The use of wastewater for SARS-CoV-2 surveillance is a useful complementary tool to clinical surveillance. The aims of this study were to characterize SARS-CoV-2 from wastewater samples, and to identify variants of concern present in samples collected from wastewater treatment plants in South African urban metros from April 2021 to January 2022. A total of 325 samples were collected from 15 wastewater treatment plants. Nucleic acids were extracted from concentrated samples, and subjected to amplicon-based whole genome sequencing. To identify variants of concerns and lineages, we used the Freyja tool (https//github.com/andersen-lab/Freyja), which assigns each sample with the prevalence of each variant present. We also used signature mutation analysis to identify variants in each wastewater treatment site. A heatmap was generated to identify patterns of emerging mutations in the spike gene using Excel conditional formatting. Using the Freyja tool, the Beta variant was detected and became predominate from April to June 2021 followed by the Delta variant and lastly the Omicron variant. Our heatmap approach was able to identify a pattern during the changes of predominate variant in wastewater with the emergence of mutations and the loss of others. In conclusion, sequencing of SARS-CoV-2 from wastewater largely corresponded with sequencing from clinical specimens. Our heatmap has the potential to detect new variants prior to emergence in clinical samples and this may be particularly useful during times of low disease incidence between waves, when few numbers of positive clinical samples are collected and submitted for testing. A limitation of wastewater sequencing is that it is not possible to identify new variants, as variants are classified based on known mutations in clinical strains.
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Full text:
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Collection:
09-preprints
Database:
PREPRINT-MEDRXIV
Language:
En
Year:
2022
Document type:
Preprint