[Paper Review] SARS-CoV-2 Wastewater Genomic Surveillance: Approaches, Challenges, and Opportunities

Viorel Munteanu, Michael A. Saldana+30 more|arXiv (Cornell University)|Sep 23, 2023

SARS-CoV-2 detection and testingMedicine3 citations

TL;DR

This paper reviews wastewater genomic surveillance (WWGS) for SARS-CoV-2, outlining its role in detecting viral variants and asymptomatic transmission despite challenges like low viral RNA concentration, degradation, and suboptimal sequencing quality. It highlights the need for specialized bioinformatics tools and standardized methods to improve lineage detection and surveillance accuracy in resource-limited settings.

ABSTRACT

During the SARS-CoV-2 pandemic, wastewater-based genomic surveillance (WWGS) emerged as an efficient viral surveillance tool that takes into account asymptomatic cases and can identify known and novel mutations and offers the opportunity to assign known virus lineages based on the detected mutations profiles. WWGS can also hint towards novel or cryptic lineages, but it is difficult to clearly identify and define novel lineages from wastewater (WW) alone. While WWGS has significant advantages in monitoring SARS-CoV-2 viral spread, technical challenges remain, including poor sequencing coverage and quality due to viral RNA degradation. As a result, the viral RNAs in wastewater have low concentrations and are often fragmented, making sequencing difficult. WWGS analysis requires advanced computational tools that are yet to be developed and benchmarked. The existing bioinformatics tools used to analyze wastewater sequencing data are often based on previously developed methods for quantifying the expression of transcripts or viral diversity. Those methods were not developed for wastewater sequencing data specifically, and are not optimized to address unique challenges associated with wastewater. While specialized tools for analysis of wastewater sequencing data have also been developed recently, it remains to be seen how they will perform given the ongoing evolution of SARS-CoV-2 and the decline in testing and patient-based genomic surveillance. Here, we discuss opportunities and challenges associated with WWGS, including sample preparation, sequencing technology, and bioinformatics methods.

Motivation & Objective

• To evaluate the current state of wastewater-based genomic surveillance (WWGS) for SARS-CoV-2 in detecting viral lineages and mutations.
• To identify technical and computational challenges in WWGS, including low viral RNA concentration and RNA degradation.
• To assess the limitations of existing bioinformatics tools in handling wastewater-specific sequencing data.
• To highlight the need for specialized, benchmarked computational tools tailored to wastewater genomics.
• To outline future research directions for improving WWGS accuracy, scalability, and integration into public health surveillance systems.

Proposed method

• Systematic review of existing approaches in wastewater genomic surveillance, focusing on sample collection, viral RNA extraction, and sequencing technologies.
• Evaluation of current bioinformatics pipelines used for wastewater data, including read alignment, variant calling, and lineage assignment.
• Analysis of challenges such as low sequencing depth, fragmented RNA, and contamination risks in wastewater samples.
• Comparison of general-purpose genomic tools with newly developed wastewater-optimized tools for variant detection and lineage inference.
• Identification of gaps in data processing workflows and recommendations for standardized, scalable, and reproducible methods.
• Integration of insights from multi-omics and host-microbe interaction studies to improve viral detection in complex wastewater matrices.

Experimental results

Research questions

• RQ1 How effective is wastewater genomic surveillance in detecting SARS-CoV-2 variants and novel lineages without clinical testing data?
• RQ2 What are the primary technical barriers to reliable viral detection and mutation profiling in wastewater samples?
• RQ3 To what extent do existing bioinformatics tools perform on wastewater sequencing data, and how do they compare to tools designed for clinical or environmental genomics?
• RQ4 How can computational methods be optimized to handle low-coverage, degraded, and fragmented viral RNA in wastewater?
• RQ5 What are the key requirements for establishing a standardized, scalable, and publicly accessible wastewater surveillance framework?

Key findings

• WWGS effectively captures known SARS-CoV-2 lineages through mutation profile detection, even in the absence of clinical data.
• Viral RNA in wastewater is often degraded and present in low concentrations, leading to poor sequencing coverage and reduced detection sensitivity.
• Existing bioinformatics tools, originally designed for clinical or transcriptomic data, are suboptimal for wastewater genomics due to unique data characteristics.
• Specialized tools for wastewater are emerging but require further benchmarking and validation as SARS-CoV-2 continues to evolve.
• The lack of standardized protocols and computational pipelines hinders reproducibility and integration into national surveillance systems.
• WWGS holds promise for detecting cryptic or novel lineages, but definitive lineage assignment remains challenging without high-quality, deep-coverage sequencing.