Evidence map›Paper›PMID 34054755›Full record

ArticleFrontiers in microbiology2021

Evaluation of Metagenomic-Enabled Antibiotic Resistance Surveillance at a Conventional Wastewater Treatment Plant.

Haniyyah J Majeed, Maria V Riquelme, Benjamin C Davis, Suraj Gupta, Luisa Angeles, Diana S Aga, Emily Garner, Amy Pruden, Peter J Vikesland

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
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

31 citing papers in PubMed.

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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

9 authors.

Haniyyah J MajeedDepartment of Civil & Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
Maria V RiquelmeDepartment of Civil & Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
Benjamin C DavisDepartment of Civil & Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
Suraj GuptaInterdisciplinary Ph.D Program in Genetics, Bioinformatics, and Computational Biology, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
Luisa AngelesDepartment of Chemistry, University at Buffalo, Buffalo, NY, United States.
Diana S AgaDepartment of Chemistry, University at Buffalo, Buffalo, NY, United States.
Emily GarnerDepartment of Civil & Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
Amy PrudenDepartment of Civil & Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
Peter J VikeslandDepartment of Civil & Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wastewater treatment plants (WWTPs) receive a confluence of sewage containing antimicrobials, antibiotic resistant bacteria, antibiotic resistance genes (ARGs), and pathogens and thus are a key point of interest for antibiotic resistance surveillance. WWTP monitoring has the potential to inform with respect to the antibiotic resistance status of the community served as well as the potential for ARGs to escape treatment. However, there is lack of agreement regarding suitable sampling frequencies and monitoring targets to facilitate comparison within and among individual WWTPs. The objective of this study was to comprehensively evaluate patterns in metagenomic-derived indicators of antibiotic resistance through various stages of treatment at a conventional WWTP for the purpose of informing local monitoring approaches that are also informative for global comparison. Relative abundance of total ARGs decreased by ∼50% from the influent to the effluent, with each sampling location defined by a unique resistome (i.e., total ARG) composition. However, 90% of the ARGs found in the effluent were also detected in the influent, while the effluent ARG-pathogen taxonomic linkage patterns identified in assembled metagenomes were more similar to patterns in regional clinical surveillance data than the patterns identified in the influent. Analysis of core and discriminatory resistomes and general ARG trends across the eight sampling events (i.e., tendency to be removed, increase, decrease, or be found in the effluent only), along with quantification of ARGs of clinical concern, aided in identifying candidate ARGs for surveillance. Relative resistome risk characterization further provided a comprehensive metric for predicting the relative mobility of ARGs and likelihood of being carried in pathogens and can help to prioritize where to focus future monitoring and mitigation. Most antibiotics that were subject to regional resistance testing were also found in the WWTP, with the total antibiotic load decreasing by ∼40-50%, but no strong correlations were found between antibiotics and corresponding ARGs. Overall, this study provides insight into how metagenomic data can be collected and analyzed for surveillance of antibiotic resistance at WWTPs, suggesting that effluent is a beneficial monitoring point with relevance both to the local clinical condition and for assessing efficacy of wastewater treatment in reducing risk of disseminating antibiotic resistance.

Indexed as

antibiogramsantibioticsmicrobiomeresistomeresistome (ARGs and MGEs)shotgun sequencing

Identifiers

PMID34054755
PMCPMC8155483

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Registered trials

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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.