Deep metatranscriptomic sequencing data of wastewater from Los Angeles, USA, 2023–2024

Infectious disease outbreaks are a major cause of mortality and morbidity worldwide, both through pandemics, such as COVID-19 or 1918 influenza¹, and continuous endemic spread of pathogens like seasonal influenza² or respiratory syncytial virus³. To mitigate the impact of infectious diseases, researchers are working on ways to identify new pathogens more quickly. One type of early warning method which has seen considerable development in recent years is wastewater monitoring¹.

Many pathogens have previously been identified in wastewater, including respiratory⁴, gastrointestinal⁵ and other viruses^6,7 as well as pathogenic protozoa⁸ and bacteria⁹. Spurred on by the COVID-19 pandemic, wastewater monitoring is now deployed across high-, middle-, and low-income countries¹⁰, identifying the spread of pathogens like H5N1 or mpox. Most of these programs use targeted assays like qPCR, or targeted sequencing methods such as Sanger or amplicon sequencing¹¹. Though useful, these methods will be of little help in detecting novel pathogens. Untargeted approaches such as wastewater metagenomic and metatranscriptomic sequencing (W-MGS/W-MTS) could, in principle, detect any pathogen, even those we have never seen before. W-MTS might be particularly promising, given its potential advantage in identifying novel RNA viruses, which pose the greatest pandemic risk¹².

While a number of valuable W-MTS datasets have been made public^13,14,15,16, the amount of available data is far less than that of W-MGS^{17,18,19,20,21,22}. Furthermore, many larger W-MTS datasets were generated during the COVID-19 pandemic, when public health measures suppressed the circulation of most other RNA viruses. New W-MTS data from the post-pandemic period is thus needed to properly evaluate this new approach to pathogen early detection.

Here we present a new W-MTS dataset comprising 43B read pairs (2 × 150 bp Illumina sequencing), 5–100 times larger than previously released W-MTS datasets^{13,14,16,23,24}. The 20 included influent wastewater samples were collected at the Hyperion Water Reclamation Plant, Los Angeles, California, USA, between December 2023 and April 2024, covering most of the 2023–2024 infectious disease season. Sequencing data was generated on a NovaSeq 6000 and a NovaSeq X (Table 1). Sequencing quality was assessed by quantifying adapter presence, QC content, duplication rates, and Phred scores.

Datasets cover a large taxonomic range, with median read fractions across libraries equaling 27.42% (min = 2.16%, max = 62.44%) for bacteria, 0.46% (0.08%, 1.31%) for eukaryotes, 0.05% (0.01%, 0.45%) for archaea, and 1.72% (0.47%, 4.36%) for viruses (Table S2). Deep sequencing and sampling during the peak of the 2023–2024 disease season revealed many human-infecting virus species, including influenza A, SARS-CoV-2, and norovirus. A varying share of reads 72.16% (34.43%, 96.60%) were unclassified when compared against the 2024 Kraken2 standard database, suggesting the extent to which reference databases do not yet capture the large taxonomic diversity of wastewater sequencing data (Table S2).

Twenty total 1 L 24-h composite influent wastewater samples were collected once weekly at the Hyperion Water Reclamation Plant in Southern California between December 2023 and April 2024 (Table S2). Samples were aliquoted into 250-mL bottles and stored at 4 °C until sample processing¹⁵. Sixty mL of sample was filtered through a sterile 0.22-μm vacuum filter (VWR, Radnor, PA) to remove solids. The filtrate was then ultrafiltered at 3,000 × g with 10-kDa Amicon filters (MilliporeSigma, Burlington, MA). Samples were centrifuged repeatedly, discarding flowthrough until the full 60-mL sample was processed. This resulted in final volumes <500 μL for each sample, which were then stored at −80 °C until RNA extraction. Wastewater concentrates were thawed on ice. Subsequently, an Invitrogen PureLink RNA minikit with on-column DNase (Invitrogen, Waltham, MA) was used to extract RNA following the manufacturer’s protocol. The RNA was subsequently stored at −80 °C.

Sample library preparation and next-generation sequencing were performed by the University of California Genomics Research and Technology Hub (GRT Hub). For library preparation, the GRT Hub used the Illumina RNA prep with enrichment kit (Illumina, San Diego, CA). The GRT Hub then sequenced paired-end libraries with 2 × 150 bp on an Illumina NovaSeq 6000 with an S4 300 cycle kit for samples collected before February 25, 2024, and a NovaSeq X with a 25B, 300 cycle flow cell for all samples collected thereafter. Per wastewater sample, 1.4B to 2.8B read pairs were generated, with an average of 2.2B.

Raw reads were screened for adapter contamination and trimmed of low-quality and low-complexity sequences with FASTP²⁵. Paired-end reads were then subset to 1 million read pairs per sample for high-level taxonomic classification using Kraken2 (v2.1.3)²⁶ using the Standard database²⁷ (2024-06-05 build). Across the entire dataset, human-infecting viruses were screened using BBDUK (v39.01)²⁸ and then identified using Kraken2 and Bowtie2²⁹, Fig. 1.

Read and base counts for different dates and sequencing platforms.

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Sequencing quality (per-base mean Phred score, GC content, and adapter presence) was measured with FastQC. Mean Phred scores were consistently above 30 for all sequencing runs, and mean GC content ranged between 44.5% and 48.94%, Fig. 2.

FastQC-measured per-base quality scores.

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Sequencing data quality and taxonomic composition was assessed using a comprehensive computational pipeline, available under https://github.com/naobservatory/mgs-workflow/tree/2.5.1. The analysis results can be accessed in the following figshare repository: https://doi.org/10.6084/m9.figshare.28454990.v1³². Code for figures and tables can be accessed under https://github.com/naobservatory/w-mgs-data-paper.

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Authors and Affiliations

1. Media Laboratory, Massachusetts Institute of Technology, Cambridge, USA

   Simon L. Grimm, William J. Bradshaw & Jeff T. Kaufman

2. SecureBio, Cambridge, USA

   Simon L. Grimm, William J. Bradshaw & Jeff T. Kaufman

3. Department of Microbiology and Plant Pathology, University of California, Riverside, Riverside, CA, USA

   Jason A. Rothman

4. Southern California Coastal Water Research Project, Costa Mesa, CA, USA

   Kylie Langlois, Joshua A. Steele & John F. Griffith

5. Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, USA

   Katrine L. Whiteson

Contributions

J.T.K. and J.A.R. conceived the study; K.L., J.A.S. and J.F.G. collected wastewater samples; J.A.R. ran sequencing experiments; J.T.K. imported sequencing data, with processing and analysis by S.L.G. and W.J.B. S.L.G. wrote the manuscript, with feedback from all authors. K.L.W. provided study design, project management, and oversight along with manuscript edits.

Corresponding authors

Correspondence to Jason A. Rothman or Jeff T. Kaufman.

Competing interests

The authors declare no competing interests.

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