RNA-Seq of untreated wastewater to assess COVID-19 and emerging and endemic viruses for public health surveillance

Abstract

Background: The COVID-19 pandemic showcased the power of genomic sequencing to tackle the emergence and spread of infectious diseases. However, metagenomic sequencing of total microbial RNAs in wastewater has the potential to assess multiple infectious diseases simultaneously and has yet to be explored.

Methods: A retrospective RNA-Seq epidemiological survey of 140 untreated composite wastewater samples was performed across urban (n = 112) and rural (n = 28) areas of Nagpur, Central India. Composite wastewater samples were prepared by pooling 422 individual grab samples collected prospectively from sewer lines of urban municipality zones and open drains of rural areas from 3rd February to 3rd April 2021, during the second COVID-19 wave in India. Samples were pre-processed and total RNA was extracted prior to genomic sequencing.

Findings: This is the first study that has utilised culture and/or probe-independent unbiased RNA-Seq to examine Indian wastewater samples. Our findings reveal the detection of zoonotic viruses including chikungunya, Jingmen tick and rabies viruses, which have not previously been reported in wastewater. SARS-CoV-2 was detectable in 83 locations (59%), with stark abundance variations observed between sampling sites. Hepatitis C virus was the most frequently detected infectious virus, identified in 113 locations and co-occurring 77 times with SARS-CoV-2; and both were more abundantly detected in rural areas than urban zones. Concurrent identification of segmented virus genomic fragments of influenza A virus, norovirus, and rotavirus was observed. Geographical differences were also observed for astrovirus, saffold virus, husavirus, and aichi virus that were more prevalent in urban samples, while the zoonotic viruses chikungunya and rabies, were more abundant in rural environments.

Interpretation: RNA-Seq can effectively detect multiple infectious diseases simultaneously, facilitating geographical and epidemiological surveys of endemic viruses that could help direct healthcare interventions against emergent and pre-existent infectious diseases as well as cost-effectively and qualitatively characterising the health status of the population over time.

Funding: UK Research and Innovation (UKRI) Global Challenges Research Fund (GCRF) grant number H54810, as supported by Research England.

Keywords: COVID-19; Endemic viruses; RNA-Seq; SARS-CoV-2; Sewage surveillance; Wastewater-based epidemiology.

Fig. 1

Schematic outlineof study methodology.

Fig. 2

RNA-Seq to assess emerging and pre-existing pathogenic viruses in untreated sewage samples. (a) The normalised abundance and best-approximation of geographical position of SARS-CoV-2 detected in untreated sewage samples from the Nagpur district of Central India. (b) The presence/absence co-occurrence of endemic viruses from the Nagpur district of Central India. RPKM, reads per kilobase per million reads.

Fig. 3

Abundance and distribution of Hepatitis C virus and Influenza A virus in untreated sewage samples. The normalised abundances of putative (a) Hepatitis C viral genotypes and (b) Influenza A viral genomic segments identified in untreated sewage samples. The number of samples with positive RPKM values that were accumulated per location are indicated at the base of each bar. RPKM, reads per kilobase per million reads.

Fig. 4

Abundance and distribution of the acute gastroenteritis-causing viruses Rotavirus and Norovirus. The normalised abundances of putative (a) Rotavirus species and its genomic segments and (b) Norovirus genogroups in untreated sewage samples. The number of samples with positive RPKM values that were accumulated per location are indicated at the base of each bar. RPKM, reads per kilobase per million reads.

Fig. 5

Abundance and distribution of additional noteworthy human pathogenic viruses. The normalised abundances of putative (a) Astrovirus genotypes, (b) Saffold virus, (c) Husavirus, (d) Measles virus, and (e) Aichi virus. The number of samples with positive RPKM values that were accumulated per location are indicated at the base of each bar. RPKM, reads per kilobase per million reads.

Fig. 6

Abundance and distribution of human disease-causing zoonotic viruses. The normalised abundances of putative (a) Chikungunya virus, (b) Jingmen tick virus, (c) Rabies virus, and (d) Porcine endogenous retrovirus E. The number of samples with positive RPKM values that were accumulated per location are indicated at the base of each bar. RPKM, reads per kilobase per million reads.