In a recent study published in the Nature journal, researchers assessed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission using wastewater sequencing.
“Before wastewater sequencing, the only way to do this was through clinical testing, which is not feasible at a large scale, especially in areas with limited resources, public participation, or the capacity to do sufficient testing and sequencing. We’ve shown that wastewater sequencing can successfully track regional infection dynamics with fewer limitations and biases than clinical testing to the benefit of almost any community.”
Timely detection of emerging SARS-CoV-2 variants is essential for public health interventions. Detecting the SARS-CoV-2 ribonucleic acid (RNA) in wastewater can provide an effective indicator of regional viral dynamics, even though clinical testing for the inference of prevailing viral lineages is unfeasible to scale.
Study: Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission. Image Credit: w.tab / Shutterstock
About the study
In the present study, the team reported a high-resolution approach for assessing community SARS-CoV-2 transmission by surveilling wastewater genomic profiles and estimating viral concentration.
The team performed SARS-CoV-2 genome sequencing from wastewater samples obtained daily from almost 131 wastewater samplers across 360 campus buildings. The epidemiological transmission links were identified by sequencing all the wastewater and clinical samples that tested positive for SARS-CoV-2 using amplicon sequencing. The team also obtained and tested 21,383 wastewater samples, among which 19,944 were from the University of California San Diego (UCSD) campus and 1,475 were from the greater San Diego region. Sequences obtained from a total of 600 campus wastewater samples were compared to 759 genomes sourced from campus clinical swabs. The team employed a building-level wastewater surveillance system enabled by a geographic information system (GIS) that covered 360 buildings present on the UCSD campus.
“Wastewater sampling essentially allowed us to ‘swab the noses’ of every person upstream from the collector daily and to use that information to concentrate viral detection efforts at the individual level.”
The team also analyzed the effectiveness of the genomic surveillance of the wastewater in assessing viral spread within a community. This was achieved by collecting near-complete viral genomes corresponding to wastewater samples having high cycle quantification (Cq) values. Furthermore, the team captured the viral diversity present in the community biospecimens by developing a tool called Freyja that evaluated the relative abundance of viral lineages present in mixed samples. Freyja could effectively recover relative linage abundance in mixed samples and perform site-specific weighting to explain the non-constant variance in single nucleotide variant (SNV) frequency estimated across sites.
Freyja was validated by sequencing spike-in synthetic mixtures obtained with five primary SARS-CoV-2 lineages, namely, lineage A, Beta, Delta, Epsilon, and Gamma at different concentrations ranging between 5% and 100% per sample. Furthermore, the team assessed if the wastewater could facilitate early detection of novel viral lineage by employing Freyja in the wastewater sequencing data. The team subsequently compared the collection dates corresponding to positive samples to the dates when the clinical samples were collected.
Additionally, the effectiveness of wastewater surveillance in detecting novel viral variants was tested by aggregating all data related to wastewater sequencing. This data was further used to predict the temporal profile correlating to the community prevalence of lineages.
The study results showed that SARS-CoV-2-positivity of the wastewater samples was strongly associated with the number of clinical positive samples. This indicated that the wastewater samples could effectively represent the dynamics of community infection as per total viral load. Furthermore, the team found that the genetic diversity of SARS-CoV-2 was remarkably greater among the wastewater samples as compared to the clinical samples. This indicated that several viral lineages, which were shed from different persons, were present in the wastewater samples while the clinical samples comprised only a single viral lineage.
Validation of Freyja revealed that Freyja consistently recovered the estimated lineage abundances for all sample mixtures. The team also noted that Freyja robustly identified the same lineages as detected in the quantitative polymerase chain reaction (qPCR) testing, and also recognized additional lineages having SNVs. Altogether, this showed that Freyja robustly evaluated abundance in viral lineages from samples comprising mixed lineages.
The team noted that Alpha and Delta lineages were detected in the wastewater samples up to 14 days before they were first detected in the genomic clinical samples. Furthermore, wastewater as well as clinical genomic surveillance could effectively monitor changes occurring in the lineage abundance, while an increase in the frequency of lineage detection was found first in the wastewater samples.
Interestingly, the team found that the estimates of viral lineage abundance in wastewater samples facilitated the early detection of emerging variants and lineages, including lineages that were rarely detected by clinical surveillance. This was especially noted when the SARS-CoV-2 Mu variant was detected using wastewater surveillance on 27 July, while its first detection was reported in the clinical samples on 23 August. However, despite the consistent detection of the Mu variant in July and August in wastewater samples, the team did not detect the variant in either the clinical or the wastewater samples in September, indicating that the local community transmission of the Mu variant did not continue.
Overall, the study findings showed that enhanced viral detection in wastewater samples along with a novel approach to identifying multiple SARS-CoV-2 variants present in a mixed sample effectively detected the prevalent viral lineage in the community. Furthermore, the method also enabled the timely detection of novel viral lineages, which can subsequently improve the accuracy as well as the effectiveness of interventions.