Emily Garner

A human exposome framework for guiding risk management and holistic assessment of recycled water quality

Challenges associated with water scarcity and increasing water demand are leading many cities around the globe to consider water reuse as a step towards water sustainability. Recycled water may be used in a spectrum of applications, from irrigation or industrial use to direct potable reuse, and thus presents a challenge to regulators as not all applications require the same level of treatment. We propose that traditional drinking water standards identifying “safe” water quality are insufficient for recycled water and that using the “human exposome” as a framework to guide development of a risk management strategy offers a holistic means by which to base decisions impacting water quality. A successful and comprehensive plan for water reuse must consider 1) health impacts associated with both acute and chronic exposures, 2) all routes of exposure by which individuals may encounter recycled water, and 3) water quality at the true point of use after storage and transport through pipe networks, rather than at the point of treatment. Based on these principles we explore key chemical differences between recycled and traditional potable water, implications for distribution systems with respect to design and operation, occurrence of chronic contaminants, and the presence of emerging and often underappreciated microbial contaminants. The unique nature of recycled water has the potential to provide rapid regrowth conditions for certain microbial contaminants in these systems, which must be considered to achieve safe water quality at the point of use.

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data

BackgroundGrowing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the “best hits” of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively.ResultsEvaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories.ConclusionsThe deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at http://bench.cs.vt.edu/deeparg.

Metagenomic profiling of historic Colorado Front Range flood impact on distribution of riverine antibiotic resistance genes

Record-breaking floods in September 2013 caused massive damage to homes and infrastructure across the Colorado Front Range and heavily impacted the Cache La Poudre River watershed. Given the unique nature of this watershed as a test-bed for tracking environmental pathways of antibiotic resistance gene (ARG) dissemination, we sought to determine the impact of extreme flooding on ARG reservoirs in river water and sediment. We utilized high-throughput DNA sequencing to obtain metagenomic profiles of ARGs before and after flooding, and investigated 23 antibiotics and 14 metals as putative selective agents during post-flood recovery. With 277 ARG subtypes identified across samples, total bulk water ARGs decreased following the flood but recovered to near pre-flood abundances by ten months post-flood at both a pristine site and at a site historically heavily influenced by wastewater treatment plants and animal feeding operations. Network analysis of de novo assembled sequencing reads into 52,556 scaffolds identified ARGs likely located on mobile genetic elements, with up to 11 ARGs per plasmid-associated scaffold. Bulk water bacterial phylogeny correlated with ARG profiles while sediment phylogeny varied along the river’s anthropogenic gradient. This rare flood afforded the opportunity to gain deeper insight into factors influencing the spread of ARGs in watersheds.

Occurrence and transformation of veterinary antibiotics and antibiotic resistance genes in dairy manure treated by advanced anaerobic digestion and conventional treatment methods

Manure treatment technologies are rapidly developing to minimize eutrophication of surrounding environments and potentially decrease the introduction of antibiotics and antibiotic resistant genes (ARGs) into the environment. While laboratory and pilot-scale manure treatment systems boast promising results, antibiotic and ARG removals in full-scale systems receiving continuous manure input have not been evaluated. The effect of treatment on ARGs is similarly lacking. This study examines the occurrence and transformation of sulfonamides, tetracyclines, tetracycline degradation products, and related ARGs throughout a full-scale advanced anaerobic digester (AAD) receiving continuous manure and antibiotic input. Manure samples were collected throughout the AAD system to evaluate baseline antibiotic and ARG input (raw manure), the effect of hygenization (post-pasteurized manure) and anaerobic digestion (post-digestion manure) on antibiotic and ARG levels. Antibiotics were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the ARGs tet(O), tet(W), sul1 and sul2 were analyzed by quantitative polymerase chain reaction (Q-PCR). Significant reductions in the concentrations of chlortetracycline, oxytetracycline, tetracycline and their degradation products were observed in manure liquids following treatment (p < 0.001), concomitant to significant increases in manure solids (p < 0.001). These results suggest sorption is the major removal route for tetracyclines during AAD. Significant decreases in the epimer-to-total residue ratios for chlortetracycline and tetracycline in manure solids further indicate degradation is desorption-limited. Moreover, sul1 and sul2 copies decreased significantly (p < 0.001) following AAD in the absence of sulfonamide antibiotics, while tetracyclines-resistant genes remained unchanged. A cross-sectional study of dairy farms utilizing natural aeration and liquid-solid separation treatments was additionally performed to compare levels of antibiotics and ARGs found in AAD with the levels in common manure management systems. The concentration of antibiotics in raw manure varied greatly between farms while minimal differences in ARGs were observed. However, significant (p < 0.01) differences in the levels of antibiotics and ARGs (except tet(W)) were observed in the effluents from the three different manure management systems.

Effects of sample preservation and DNA extraction on enumeration of antibiotic resistance genes in wastewater

ABSTRACT With the growing application of high‐throughput sequencing‐based metagenomics for profiling antibiotic resistance genes (ARGs) in wastewater treatment plants (WWTPs), comparison of sample pretreatment and DNA extraction methods are needed to move toward standardized comparisons among laboratories. Three widely employed DNA extraction methods (FastDNA® Spin Kit for Soil, PowerSoil® DNA Isolation Kit and ZR Fecal DNA MiniPrep), with and without preservation in 50% ethanol and freezing, were applied to the influent, activated sludge and effluent of two WWTPs, in Hong Kong and in the USA. Annotated sequences obtained from the DNA extracted using the three kits shared similar taxonomy and ARG profiles. Overall, it was found that the DNA yield and purity, and diversity of ARGs captured were all highest when applying the FastDNA SPIN Kit for Soil for all three WWTP sample types investigated here (influent, activated sludge, effluent). Quantitative polymerase chain reaction of 16S rRNA genes confirmed the same trend as DNA extraction yields and similar recovery of a representative Gram‐negative bacterium (Escherichia coli). Moreover, sample fixation in ethanol, deep‐freezing and overseas shipment had no discernable effect on ARG profiles, as compared to fresh samples. This approach serves to inform future efforts toward global comparisons of ARG distributions in WWTPs.

ARG-miner: A web platform for crowdsourcing-based curation of antibiotic resistance genes

Curation of antibiotic resistance gene (ARG) databases is labor intensive and requires expert knowledge to manually collect, correct, and/or annotate individual genes. Consequently, most existing ARG databases contain only a small number of ARGs (~5k genes) and updates to these databases tend to be infrequent, commonly requiring years for completion and often containing inconsistencies. Thus a new approach is needed to achieve a truly comprehensive ARG database while also maintaining a high level of accuracy. Here we propose a new web-based curation system, ARGminer, that supports the annotation and inspection of several key attributes of potential ARGs, including gene name, antibiotic category, resistance mechanism, evidence for mobility and occurrence in clinically-important bacterial strains. Here we employ crowdsourcing as a novel strategy to overcome limitations of manual curation and expand curation capacity towards achieving a truly comprehensive and perpetually up-to-date database. Further, machine learning is employed as a powerful means to validate database curation, drawing from natural language processing to infer correct and consistent nomenclature for each potential ARG. We develop and validate the crowdsourcing approach by comparing performances of multiple cohorts of curators with varying levels of expertise, demonstrating that ARGminer is a time and cost efficient means of achieving accurate ARG curation. We further demonstrate the reliability of a trust validation filter for rejecting input generated by spammers. Crowdsourcing was found to be as accurate as expert annotation, with an accuracy >90% for the annotation of a diverse test set of ARGs. The ARGminer public search platform and database is available at http://bench.cs.vt.edu/argminer.

Microbial Ecology and Water Chemistry Impact Regrowth of Opportunistic Pathogens in Full-Scale Reclaimed Water Distribution Systems

Need for global water security has spurred growing interest in wastewater reuse to offset demand for municipal water. While reclaimed (i.e., nonpotable) microbial water quality regulations target fecal indicator bacteria, opportunistic pathogens (OPs), which are subject to regrowth in distribution systems and spread via aerosol inhalation and other noningestion routes, may be more relevant. This study compares the occurrences of five OP gene markers ( Acanthamoeba spp., Legionella spp., Mycobacterium spp., Naegleria fowleri, Pseudomonas aeruginosa) in reclaimed versus potable water distribution systems and characterizes factors potentially contributing to their regrowth. Samples were collected over four sampling events at the point of compliance for water exiting treatment plants and at five points of use at four U.S. utilities bearing both reclaimed and potable water distribution systems. Reclaimed water systems harbored unique water chemistry (e.g., elevated nutrients), microbial community composition, and OP occurrence patterns compared to potable systems examined here and reported in the literature. Legionella spp. genes, Mycobacterium spp. genes, and total bacteria, represented by 16S rRNA genes, were more abundant in reclaimed than potable water distribution system samples ( p ≤ 0.0001). This work suggests that further consideration should be given to managing reclaimed water distribution systems with respect to nonpotable exposures to OPs.