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Advancing antimicrobial resistance monitoring in surface waters with metagenomic and quasimetagenomic methods [1]

['Andrea Ottesen', 'Center For Veterinary Medicine', 'Food', 'Drug Administration', 'Laurel', 'Maryland', 'United States Of America', 'National Antimicrobial Resistance Monitoring System', 'Narms', 'Brandon Kocurek']

Date: 2022-12 

Abstract The National Antimicrobial Resistance Monitoring System (NARMS) has monitored antimicrobial resistance (AMR) associated with pathogens of humans and animals since 1996. In alignment with One Health strategic planning, NARMS is currently exploring the inclusion of surface waters as an environmental modality for monitoring AMR. From a One Health perspective, surface waters function as key environmental integrators between humans, animals, agriculture, and the environment. Surface waters however, due to their dilute nature present a unique challenge for monitoring critically important antimicrobial resistance. Selective enrichments from water paired with genomic sequencing effectively describe AMR for single genomes but do not provide data to describe a broader environmental resistome. Metagenomic data effectively describe a broad range of AMR from certain matrices however, depth of coverage is usually insufficient to describe clinically significant AMR from aquatic matrices. Thus, the coupling of biological enrichments of surface water with shotgun NGS sequencing has been shown to greatly enhance the capacity to report an expansive profile of clinically significant antimicrobial resistance genes. Here we demonstrate, using water samples from distinct sites (a creek in close proximity to a hospital and a reservoir used for recreation and municipal water), that the AMR portfolio provided by enriched (quasimetagenomic) data is capable of describing almost 30% of NARMS surveillance targets contrasted to only 1% by metagenomic data. Additionally, the quasimetagenomic data supported reporting of statistically significant (P< 0.05) differential abundance of specific AMR genes between sites. A single time-point for two sites is a small pilot, but the robust results describing critically important AMR determinants from each water source, provide proof of concept that quasimetagenomics can be applied to aquatic AMR surveillance efforts for local, national, and global monitoring.

Citation: Ottesen A, Kocurek B, Ramachandran P, Reed E, Commichaux S, Engelbach G, et al. (2022) Advancing antimicrobial resistance monitoring in surface waters with metagenomic and quasimetagenomic methods. PLOS Water 1(12): e0000067. https://doi.org/10.1371/journal.pwat.0000067 Editor: Katrina J. Charles, University of Oxford, UNITED KINGDOM Received: April 8, 2022; Accepted: October 26, 2022; Published: December 14, 2022 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability: All sequences have been deposited in NARMS Water: NARMS Water Metagenomes under the BioProject ID: PRJNA794347 (http://www.ncbi.nlm.nih.gov/bioproject/794347). Funding: The work was funded by the National Antimicrobial Resistance Monitoring System (NARMS) and the Office of Research at the Center for Veterinary Medicine of the U.S. Food and Drug Administration. AO, BK, PM, ES, and CK received funding from NARMS to coordinate water collection, sequencing, and data analysis. Competing interests: The views expressed in this manuscript are those of the authors and do not necessarily reflect the official policy of the Department of Health and Human Services, the U.S. Food and Drug Administration, or the U.S. Government. Reference to any commercial materials, equipment, or process does not in any way constitute approval, endorsement, or recommendation by the Food and Drug Administration.

Introduction From a One Health perspective, surface waters function as key environmental integrators. They receive human, agricultural, and wildlife input and provide that same water for human, agricultural, and wildlife sustenance. Industrial and agricultural chemicals, metals, food additives, antibiotics, and even non-antibiotic drugs, have all been shown to play influential roles in the spread of AMR [1, 2]. Understanding the presence of pathogens and antimicrobial resistance (AMR) determinants in surface waters helps to inform risks across a wide range of applications and provides an integrative approach to public health. Recognizing the significant health impact that environmental water has on humans, animals, and the environment [3–5] the National Antimicrobial Resistance Monitoring System (NARMS) is investigating surface waters as a potential environmental modality for One Health AMR monitoring. This strategy requires methodological approaches capable of reporting AMR from an ecosystem as complex and dilute as water. Currently, NARMS monitoring efforts use standard in vitro antimicrobial susceptibility testing (AST) to generate minimum inhibitory concentrations (MICs) and whole genome sequencing (WGS) to predict resistant phenotypes directly from nucleotide data [6, 7]. These approaches rely on preliminary selective enrichments that produce high resolution data characterizing AMR phenotypes of pathovars. Metagenomic data is useful for providing ‘big pictures’ of certain environmental, human, and animal microbiomes; however, for the pathogens under active surveillance by NARMS, metagenomic data usually doesn’t provide sufficient depth of coverage to describe AMR phenotypes–especially for water. Quasimetagenomic data (QMGS), due to its inclusion of an enrichment step, provides coverage of critically important resistance determinants with a broader throughput than culture independent (CI) metagenomics or WGS. Critically important antimicrobials (CIA) are ranked by the World Health Organization (WHO) according to their importance in human medicine in efforts to develop risk management for control of AMR in humans and animals. CIAs from WHO comprise more than a dozen different classes of antibiotics [8]. Currently NARMS monitors a subset of genes conferring resistance to CIAs in Escherichia, Salmonella, Campylobacter and Enterococcus primarily isolated from human, food-producing animals, raw retail meats, and feed environments. NARMS seafood monitoring also tracks resistance in Aeromonas and Vibrio species. For NARMS, critically important determinants for monitoring efforts include genes conferring resistance to aminoglycosides, quinolones, ß-lactams, colistin, macrolides and ketolides, oxazolidinones, penicillins (Table 1) [9]. PPT PowerPoint slide

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TIFF original image Download: Table 1. Genes encoding resistance to critically important antimicrobial agents. https://doi.org/10.1371/journal.pwat.0000067.t001 For NARMS’s expanded One Health mission, new approaches that are capable of detecting and describing critically significant resistance determinants will be needed. In the present study dead end ultrafiltration [10] was used to collect 50L volumes from each of two sites: 1) an urban creek in close proximity to a hospital (Sligo), and 2) a recreational reservoir which is the source of drinking water for Prince George’s county, Maryland (Patuxent). DNA extracted from water was evaluated using both metagenomic and quasimetagenomic data to assess the ability of each data type to describe critically important NARMS resistance determinants. Quasimetagenomics, which uses shotgun sequencing of enriched microbiomes at strategic temporal increments during pathogen recovery protocols, has previously proven useful for expedited source tracking in outbreak investigations [11], identification of multi-serovar diversity associated with samples linked to outbreaks, and identification of co-competitors, co-enrichers, and recovery biases in state of the art FDA microbiological culturing methods [12, 13]. Here we demonstrate that the approach also greatly enhances capacity for resistome reporting and description of critically important resistance determinants, and taxa important to global AMR morbidity and mortality.

Discussion Water plays perhaps the most important role in states of health and disease in humans and other animals, yet it is one of the most complex matrices to monitor due to its dilute nature. Metagenomics is often a valuable way to monitor a wide breadth of AMR from complex environmental samples but for certain matrices such as surface waters, there is simply not enough sequence coverage of AMR genes to describe critically important resistant determinants in water. Work by Gweon et al. used 200 million CI reads to describe AMR in pig caeca, effluent, and stream sediment. For pig caeca, the highest number of hits to AMR genes reached about 55,000, for effluent, about 22,000, but for stream sediment only 22 hits were observed [25]. This is consistent with numbers observed by metagenomic methods in the water samples examined here (stream and reservoir) with 70 to 150 million reads. CI data was insufficient for the monitoring aims of the NARMS program, but QMGS data met the challenge of reporting clinically important antimicrobial resistance. While QMGS produced robust data, there will inevitably be biases and limitations associated with this approach that will be important to evaluate in future work. The use of established AMR multiplex PCR panels side by side QMGS data will inform on just how much is missed by a QMGS approach and perhaps also, how much is gained. While PCR approaches may be more sensitive for a wide range of genes, they will never provide data beyond the panel. QMGS may have less sensitivity for certain genes, but it will also support discovery and provide complementary environmental genomic data to describe plasmids and co-occurring taxa to better understand community dynamics. Quasimetagenomic approaches have an established history of expediting WGS based source tracking and plasmid identification, as well as describing multi-serovar diversity associated with outbreaks [11, 12, 26]. Here we demonstrate that QMGS data also provides robust utility for AMR reporting from surface waters. The QMGS data presented here represents water incubated for 24 H at 37° in Buffered Peptone Water (BPW). There are many opportunities for advanced, precision QMGS targeting of important species and consortia of species by adjusting nutrients, oxygen tension, time, temperature, and addition of antibiotics. Even in enriched (QMGS) samples for Sligo and Patuxent, with an average of 70 million reads per replicate, the coverage of taxa responsible for the most AMR deaths worldwide (Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa [22]) comprised less than 6% of the total data. This, in contrast to less than 1% coverage in CI data, is a vast improvement but still leaves opportunity for further optimization. Bioinformatic methods will also require optimization, validation, and synchronization for harmonized reporting of taxonomy and resistance determinants. Important work has described AMR in water and effluent [3–5, 27–37], but lack of a consensus set of tools to harmonize methodologies and analyses across multiple groups’ efforts from water collection and laboratory processing to bioinformatic analysis and reporting, leaves us without a ready, common source of information to support local, national, and global AMR surveillance of key environmental integrators like surface waters. Metagenomic approaches provided valuable description of species such as Mycobacterium, Mycoplasma and Neisseria (as shown in Fig 11), but for monitoring of NARMS critically important gene targets, we recommend that metagenomics be coupled with quasimetagenomics. The Centers for Disease Control and Prevention (CDC) estimate that millions of people in the United States are impacted by waterborne diseases every year [38]. The WHO reports that 144 million people rely on untreated surface water [39], with projections that by 2025, more than half of the world’s population will live in water stressed areas [39]. Surface waters have the potential to serve as sentinels for global One Health monitoring of antimicrobial resistance. Murray et al. estimate there were 1.27 million deaths attributable to antimicrobial resistance in 2019 [22]. That number is close to the global number of HIV (680,000) and malaria (627,000) deaths combined [40–42]. Understanding the flow of antimicrobial resistance through ecosystems is a fundamental objective for One Health research and a new priority for the National Antimicrobial Resistance Monitoring System. As laboratory and informatic methods are harmonized in collaborative endeavors spanning microbiology, molecular biology, chemistry, hydrology, epidemiology, and interoperable metadata ontology, it will be possible to develop global resources to describe phenotype evolution, plasmid dynamics and an improved understanding of the flow of AMR through human, animal and environmental ecosystems. Our findings suggest that metagenomic and quasimetagenomic data used together, provide a valuable framework to support a new era of One Health AMR monitoring and identification of emerging resistance.

Materials and methods Water collection Dead end ultrafiltration (DEUF) was used to collect 50 L of water from Sligo and Patuxent water sources [28]. DEUF was done using a Hemodialyzer Rexeed 25S filters (AsahiKasei, Chiyoda, Tokyo, Japan) and a Geopump peristaltic pump (Geotech, Denver, CO). Cells and particles are caught in the hollow fiber membranes within the ultrafilter filter, while filtrate passes through. Ultrafilter membrane separation collects particles between nano and micro (pore size range of 0.001–0.05 μm) and all manner of larger organisms and debris. The size range captured by ultrafiltration is ideal for examination of viruses, bacteria, fungi, and protists in water. After collection, filters are capped, bagged and stored at 4°C, until backflushing. Laboratory processing Backflushing. First step for laboratory processing is the ‘backflushing’ of the filter. Backflush concentrate was used for culture independent (CI) metagenomics by direct DNA extraction of water filters passed through 0.2 micron filters. Nucleic acid extraction was conducted directly on the filters as part of the Qiagen DNeasy PowerWater DNA extraction protocol (Qiagen, Germantown, MD, United States) according to the manufacturer’s specifications (Qiagen PowerWater Kit Handbook). Enrichment Enrichment for quasimetagenomes was achieved by adding BPW at a 1 to 1 ratio (25 ml to 25 ml) (4 replicates of backflushed filtrate from each 50L ultrafilter collections with incubation at 37° for 24 H). After 24 H of incubation at 37°, 2 ml aliquots were removed, centrifuged and DNA was extracted from the pellet using the Zymo High Molecular Weight DNA Extraction kit according to the manufacturers specifications (Zymo Quick-DNA Magbead Handbook). Library preparation and sequencing DNA libraries from both CI and QMGS samples was prepared using the Illumina DNA Library Prep according to the manufacturers specifications (Illumina). https://www.protocols.io/edit/illumina-dna-prep-sop-bzstp6en. Sequencing was performed on a NextSeq 500 according to the manufacturer’s specifications. All sequencing runs were performed in paired end mode with 2 x 150 cycles using the NextSeq 500/550 v2.5 High Output Kit (150 Cycles). Libraries were diluted to 1.8 pM according to the manufacturer’s specifications (NextSeq Denature and Dilute Libraries Guide). Bioinformatic analyses Files were demultiplexed (bcl to fastq) and screened/trimmed using Trimmomatic [43]. Four replicates of each treatment with reads per sample spanning 20 million to 150 million reads were used for further downstream analyses. Fastqs were run on the AMR++ pipeline [7] with the Megares database v2 using the CFSAN High Performance Cluster (HPC) with default parameters. All fluoroquinolones requiring SNP confirmation were verified by identifying the SNPs conferring resistance in Tablet [44] using the AMR++ output. For annotation by AMR FinderPlus, fastq files were aligned against the AMRFinder Plus database using SAUTE [45] on the CFSAN HPC. https://github.com/ncbi/amr/wiki/Methods. Blast was used with the CARD [15] database to annotate sequence data according to default parameters. Reads were also evaluated using the COSMOS ID analytical pipeline (AMR database update July 2021 https://www.cosmosid.com). Counts and abundances from AMR annotation outputs were ‘normalized’ using scripts to assess ‘reads per kilobase of transcript’ (RPKM) to normalize gene reporting between different sites by accommodating for variation in number of sequencing reads per sample and gene length. Total reads in the sample were divided by 1,000,000 “per million” scaling factor to normalize for sequencing depth and provide ‘reads per million’ (RPM). RPM values were then divided by length of each gene in kilobases to report ‘RPKM. https://github.com/SethCommichaux/AMRplusplus Annotation of metagenomic outputs of antimicrobial resistance Each pipeline used to examine Sligo and Patuxent data generated annotations by different algorithmic approaches and slightly different databases and output styles. AMRFinderPlus uses Hidden Markov Models (HMMs) with customized algorithms to identify AMR genes, point mutations, stress responses, and virulence genes. AMRFinderPlus can use both protein and nucleotide sequences. Outputs include gene length and contig position information for users to further their own evaluations of the annotations [16]. The pipeline was primarily designed for use with genomes. AMRPlusPlus was designed for use with large datasets of short read metagenomic data. It handles terabyte sized data fast and accurately for count-based data. The latest update in AMRPlusPlus’s associated database, MEGARes 2.0 incorporates published resistance sequences (~8,000 hand curated) for antimicrobial drugs, metal, and biocide resistance determinants [14]. CosmosID uses kmers which are excellent for detection (sensitivity) but sometimes lacking in specificity. While Cosmos’s approach and database is proprietary, there is very clear overlap with AMRFinderPlus annotation which uses the NCBI databases. The Comprehensive Antibiotic Resistance Database (CARD) (https://card.mcmaster.ca) provides highly curated reference sequences of both nucleotides and proteins with a highly structured ontology to support analysis and prediction [15]. Plasmids were annotated using ‘Platon’ according to the default parameters [46] (https://github.com/oschwengers/platon). Taxonomy All taxonomic annotation was accomplished using an in-house FDA kmer pipeline developed at CFSAN, FDA, hand curated since 2014 to address pathogens monitored by FDA, available on GalaxyTRAKR http://galaxytrakr.org. Data reporting and sharing Reporting. Pipeline annotation outputs were visualized using R and Graphlan [47]. Replicates of water samples were merged for certain visualizations to simplify reporting. Data sharing. All sequences have been deposited in NARMS Water: NARMS Water Metagenomes in the BioProject ID: PRJNA794347 http://www.ncbi.nlm.nih.gov/bioproject/794347.

Acknowledgments We would like to acknowledge and profoundly thank the architects and scientists that support the CFSAN High Performance Cluster.

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