Tracking the circulating SARS-CoV-2 variant of concern in South Africa using wastewater-based epidemiology

Abstract

This study uses wastewater-based epidemiology (WBE) to rapidly and, through targeted surveillance, track the geographical distribution of SARS-CoV-2 variants of concern (Alpha, Beta and Delta) within 24 wastewater treatment plants (WWTPs) in the Western Cape of South Africa. Information obtained was used to identify the circulating variant of concern (VOC) within a population and retrospectively trace when the predominant variant was introduced. Genotyping analysis of SARS-CoV-2 showed that 50% of wastewater samples harbored signature mutations linked to the Beta variant before the third wave, with the Delta variant absent within the population. Over time, the prevalence of the beta variant decreased steadily. The onset of the third wave resulted in the Delta variant becoming the predominant variant, with a 100% prevalence supporting the theory that the Delta variant was driving the third wave. In silico molecular docking analysis showed that the signature mutations of the Delta variant increased binding to host proteins, suggesting a possible molecular mechanism that increased viral infectivity of the Delta variant.

Figure 1

Temporal analysis of SARS-Cov-2 viral load in wastewater against diagnosed COVID-19 cases in Western Cape.

Figure 2

Mutation frequency linked to key Variants of Concern (VOC). Frequency of viral RNA showing spike protein mutation linked to the (A) Beta variant with three key substitutions in the RBD (K417N, E484K and N501Y), (B) the Alpha variant with the N501Y, P681H and 69/70 Deletion and, (C) the Delta variant harbouring mutations for P681R and L452R.

Figure 3

Temporal analysis of SARS-CoV-2 variants of concern compared to diagnosed COVID-19 cases. Data suggests that the Beta variant predominance decreases with dominance of Delta variant. Data is represented as a stacked bar graph. Each colour represents the average frequency of a variant type that occurs across all WWTPs, and the proportion of that specific variant compared to other variants is given by week.

Figure 4

Temporal analysis of the City of Cape Town municipality WWTP. The frequency of the spike protein mutations during the epidemiological weeks seeks to explain patterns of the shift of key mutations linked to 3 variants of concern; (A) Beta, (B) Alpha, and (C) Delta variant detected over time. The mutational frequency on the y axis is represented by a stacked bar graph. Each colour represents the average mutation frequency of a variant type, which refers to the proportion of wastewater samples that were positive for a mutation divided by the total number of samples.

Figure 5

Spatial analysis of the rapidly evolving Delta variant over three months. The Delta variant was initially detected in the Melkbosstrand WWTP, where after it spread to the remaining catchment areas. Spatial mapping showed that the Delta variant was detected in more than 95% of the WWTP. Red-Beta variant, Green-Delta and Black indicates the presence of the P681H mutation. All maps were produced using ArcGIS 10.6.1 (https://www.arcgis.com/).

Figure 6

Schematic representation of SARS-CoV-2 spike glycoprotein: Schematic representation of SARS-CoV-2 spike glycoprotein: (A) Surface area representation of trimeric spike protein conferring an “open conformation” RBD domain (S1 domain depicted in blue and the S2 domain represented in purple) (B) Atomistic representation of characterized subdomains within the monomeric spike protein and the spatial mapping of defined protein binding regions. The identified VOCs within the RBD, NTD and S1/S2 boundary site are also demonstrated. The Alpha variant mutation and deletion are represented in green, the Beta variant in red and the Delta variant in orange.