SARS-CoV-2 genomic surveillance in wastewater as a model for monitoring evolution of endemic viruses

As global SARS-CoV-2 burden and testing frequency have decreased, wastewater surveillance has emerged as a crucial tool for complementing clinical surveillance efforts. Traditional clinical surveillance, reliant on testing and sequencing samples from infected individuals, faces limitations due to variations in testing practices, accessibility, and population health-seeking behavior. This often results in an incomplete representation of virus spread and diversity, primarily capturing symptomatic cases. Wastewater-based testing overcomes these limitations by providing a community-wide sample, offering valuable insights into SARS-CoV-2 transmission dynamics at a significantly lower cost. The study was conducted in South Africa, a middle-income country with extensive wastewater infrastructure and a robust clinical surveillance system, to assess the effectiveness of wastewater genomic surveillance in a real-world setting. South Africa experienced four COVID-19 waves, providing a diverse range of circulating variants for analysis. The study aimed to demonstrate the utility of wastewater-based genomic surveillance in identifying and characterizing circulating SARS-CoV-2 variants, comparing its findings with established clinical genomic surveillance data, and highlighting the potential of this approach for early warning systems.

Prior research has demonstrated the potential of wastewater sequencing for SARS-CoV-2 surveillance. Studies have shown the feasibility of recovering complete virus genomes from wastewater, illustrating comparable lineage dynamics between wastewater and clinical surveillance, and even identifying novel mutations and lineages in wastewater before their appearance in clinical samples. However, the widespread application of wastewater sequencing in low- and middle-income countries has been limited. This study builds upon this existing research by applying wastewater genomic surveillance in a middle-income country context, providing valuable insights into its practicality and effectiveness in a setting with varying levels of healthcare infrastructure and resources.

A total of 325 wastewater samples were collected from 15 wastewater treatment plants across five South African provinces from April 2021 to January 2022. Samples were amplified and sequenced; 229 samples with >1 million reads were included in mutational analysis and heatmap visualization, while 183 samples with >50% whole-genome coverage were used for Freyja analysis. Lineage prevalence was estimated using Freyja, a tool that utilizes a barcode library of lineage-defining mutations. In addition to Freyja analysis, the frequency of variant-specific signature mutations in the spike region was analyzed. Clinical genomic surveillance data from the Network for Genomic Surveillance of South Africa (NGS-SA), which receives randomly selected samples from public and private laboratories, was used for comparison. The NGS-SA utilizes either the Oxford Nanopore Midnight protocol or the Illumina COVIDseq Assay, with all sequences uploaded to GISAID. The study also performed an unbiased analysis of amino acid mutations in the spike gene, comparing wastewater-derived mutations with those reported in the GISAID and Outbreak.info databases. Statistical analysis and visualization were performed using R and custom Python scripts. Ethical approval was obtained from the Human Research Ethics Committee of the University of the Witwatersrand.

The study found that wastewater-based genomic surveillance closely mirrored the trends observed in clinical genomic surveillance. The prevalence of variants (Beta, Delta, Omicron) and their sub-lineages in wastewater samples showed wave-like dynamics that closely paralleled clinical observations. Wastewater sequencing identified the Alpha variant and the Delta sublineage AY.45, the dominant lineage during the Delta wave, and the cryptic circulation of A lineage viruses (including A.25) and Alpha-Delta recombinant lineage XC, which were not detected by clinical surveillance. The rapid rise of Omicron BA.1 and its subsequent displacement by BA.2, along with substantial prevalence of BA.3 and other Omicron recombinants (XE, XAD, XAP), were also observed in the wastewater data. Analysis of signature mutations provided reliable lineage prevalence measurements, even from low-coverage samples. Spike gene-wide mutation analysis revealed distinct mutational profiles for each variant wave, with the transition from Delta to Omicron characterized by a shift in mutations from the N-terminal domain (NTD) to the receptor-binding domain (RBD), fusion peptide (FP), and heptad repeat 1 (HR1) regions. The analysis also identified several rare mutations present in >1% prevalence in wastewater samples but at <1% prevalence in global clinical samples from GISAID, suggesting the potential for early detection of novel variants. These uncommon mutations included S50L, H66Y, T250S, A288T, K444T, Q498H, D627H, L828F, T859N, and Q1201K.

The results demonstrate that wastewater genomic surveillance effectively complements clinical surveillance for SARS-CoV-2. The high concordance between wastewater and clinical data validates the utility of wastewater sequencing for monitoring variant prevalence and transitions. The identification of lineages and mutations not detected clinically highlights the potential for wastewater surveillance to capture a more complete picture of viral diversity and spread, particularly in settings with limited clinical testing. The detection of rare mutations in wastewater before their widespread emergence in clinical samples suggests the potential for early warning systems for novel variants. The findings have significant implications for public health, particularly in resource-constrained settings, where wastewater surveillance can be a cost-effective and comprehensive tool for monitoring viral evolution and guiding public health interventions.

This study successfully demonstrates the value of wastewater-based genomic surveillance for monitoring SARS-CoV-2 variants. The results highlight the method's ability to track variant prevalence, identify novel lineages and mutations, and potentially provide an early warning system for emerging variants. Further research should focus on refining methodologies to address limitations such as inhibition from wastewater matrix components and low virus concentrations, particularly in low- and middle-income countries. The development of improved bioinformatic tools tailored to wastewater data analysis is also crucial. Wastewater surveillance, when combined with clinical data, offers a powerful and cost-effective approach to understanding and responding to viral outbreaks.

The study acknowledges limitations inherent in wastewater-based surveillance, including the complex nature of the wastewater matrix, which can inhibit amplification and sequencing, and challenges associated with low viral loads when incidence is low. The reliance on existing bioinformatic tools and lineage assignments based on clinical data presents limitations in identifying entirely novel variants. Improvements to viral enrichment methods and bioinformatic pipelines are needed to address these issues. The study also notes that the emergence of new variants with poor primer binding can result in lower coverage rates, although this can serve as an early warning signal itself.