Evidence map›Paper›PMID 36871720›Full record

ArticleThe Science of the total environment2023

Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes.

Shuchen Feng, Sarah M Owens, Abhilasha Shrestha, Rachel Poretsky, Erica M Hartmann, George Wells

Open access · hybridAbstract read
In one paragraph

Article in The Science of the total environment, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.7field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 19 citations in OpenAlex.

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  9. Article
  10. 20-Month monitoring of SARS-CoV-2 in wastewater of Curitiba, in Southern Brazil.Environmental science and pollution research international · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 1 country.

Shuchen FengDepartment of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA.
Sarah M OwensBiosciences Division, Argonne National Laboratory, Lemont, IL, USA.
Abhilasha ShresthaDepartment of Environmental and Occupation Health Sciences, School of Public Health, University of Illinois Chicago, Chicago, IL, USA.
Rachel PoretskyDepartment of Biological Sciences, University of Illinois Chicago, Chicago, IL, USA.
Erica M HartmannDepartment of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA.
George WellsDepartment of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA. Electronic address: george.wells@northwestern.edu.
Northwestern University · USArgonne National Laboratory · USUniversity of Illinois Chicago · USUniversity of Illinois Urbana-Champaign · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wastewater SARS-CoV-2 surveillance has been deployed since the beginning of the COVID-19 pandemic to monitor the dynamics in virus burden in local communities. Genomic surveillance of SARS-CoV-2 in wastewater, particularly efforts aimed at whole genome sequencing for variant tracking and identification, are still challenging due to low target concentration, complex microbial and chemical background, and lack of robust nucleic acid recovery experimental procedures. The intrinsic sample limitations are inherent to wastewater and are thus unavoidable. Here, we use a statistical approach that couples correlation analyses to a random forest-based machine learning algorithm to evaluate potentially important factors associated with wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes, with a specific focus on the breadth of genome coverage. We collected 182 composite and grab wastewater samples from the Chicago area between November 2020 to October 2021. Samples were processed using a mixture of processing methods reflecting different homogenization intensities (HA + Zymo beads, HA + glass beads, and Nanotrap), and were sequenced using one of the two library preparation kits (the Illumina COVIDseq kit and the QIAseq DIRECT kit). Technical factors evaluated using statistical and machine learning approaches include sample types, certain sample intrinsic features, and processing and sequencing methods. The results suggested that sample processing methods could be a predominant factor affecting sequencing outcomes, and library preparation kits was considered a minor factor. A synthetic SARS-CoV-2 RNA spike-in experiment was performed to validate the impact from processing methods and suggested that the intensity of the processing methods could lead to different RNA fragmentation patterns, which could also explain the observed inconsistency between qPCR quantification and sequencing outcomes. Overall, extra attention should be paid to wastewater sample processing (i.e., concentration and homogenization) for sufficient and good quality SARS-CoV-2 RNA for downstream sequencing.

Indexed as

COVID-19SARS-CoV-2HumansPandemicsRNA, ViralSpecimen HandlingWastewaterRNA, ViralWastewaterAmplicon sequencingIllumina COVIDseqQIAseq DIRECTRNA fragmentationSample processing methodsWastewater SARS-CoV-2

Identifiers

PMID36871720
PMCPMC9984232
OpenAlexW4323030842

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.