Demonstrating a Comprehensive Wastewater-Based Surveillance Approach That Differentiates Globally Sourced Resistomes

Wastewater-based surveillance (WBS) for disease monitoring is highly promising but requires consistent methodologies that incorporate predetermined objectives, targets, and metrics. Herein, we describe a comprehensive metagenomics-based approach for global surveillance of antibiotic resistance in sewage that enables assessment of 1) which antibiotic resistance genes (ARGs) are shared across regions/communities; 2) which ARGs are discriminatory; and 3) factors associated with overall trends in ARGs, such as antibiotic concentrations. Across an internationally sourced transect of sewage samples collected using a centralized, standardized protocol, ARG relative abundances (16S rRNA gene-normalized) were highest in Hong Kong and India and lowest in Sweden and Switzerland, reflecting national policy, measured antibiotic concentrations, and metal resistance genes. Asian versus European/US resistomes were distinct, with macrolide-lincosamide-streptogramin, phenicol, quinolone, and tetracycline versus multidrug resistance ARGs being discriminatory, respectively. Regional trends in measured antibiotic concentrations differed from trends expected from public sales data. This could reflect unaccounted uses, captured only by the WBS approach. If properly benchmarked, antibiotic WBS might complement public sales and consumption statistics in the future. The WBS approach defined herein demonstrates multisite comparability and sensitivity to local/regional factors.

Triclosan has a robust, yet reversible impact on human gut microbial composition in vitro

The recent ban of the antimicrobial compound triclosan from use in consumer soaps followed research that showcased the risk it poses to the environment and to human health. Triclosan has been found in human plasma, urine and milk, demonstrating that it is present in human tissues. Previous work has also demonstrated that consumption of triclosan disrupts the gut microbial community of mice and zebrafish. Due to the widespread use of triclosan and ubiquity in the environment, it is imperative to understand the impact this chemical has on the human body and its symbiotic resident microbes. To that end, this study is the first to explore how triclosan impacts the human gut microbial community in vitro both during and after treatment. Through our in vitro system simulating three regions of the human gut; the ascending colon, transverse colon, and descending colon regions, we found that treatment with triclosan significantly impacted the community structure in terms of reduced population, diversity, and metabolite production, most notably in the ascending colon region. Given a 2 week recovery period, most of the population levels, community structure, and diversity levels were recovered for all colon regions. Our results demonstrate that the human gut microbial community diversity and population size is significantly impacted by triclosan at a high dose in vitro, and that the community is recoverable within this system.

Enhanced GII.4 human norovirus infection in gnotobiotic pigs transplanted with a human gut microbiota

The role of commensal microbiota in enteric viral infections has been explored extensively, but the interaction between human gut microbiota (HGM) and human norovirus (HuNoV) is poorly understood. In this study, we established an HGM-Transplanted gnotobiotic (Gn) pig model of HuNoV infection and disease, using an infant stool as HGM transplant and a HuNoV GII.4/2006b strain for virus inoculation. Compared to germ-free Gn pigs, HuNoV inoculation in HGMT Gn pigs resulted in increased HuNoV shedding, characterized by significantly higher shedding titres on post inoculation day (PID) 3, 4, 6, 8 and 9, and significantly longer mean duration of virus shedding. In addition, virus titres were significantly higher in duodenum and distal ileum of HGMT Gn pigs on PID10, while comparable and transient HuNoV viremia was detected in both groups. 16S rRNA gene sequencing demonstrated that HuNoV infection dramatically altered intestinal microbiota in HGMT Gn pigs at the phylum (Proteobacteria, Firmicutes and Bacteroidetes) and genus (Enterococcus, Bifidobacterium, Clostridium, Ruminococcus, Anaerococcus, Bacteroides and Lactobacillus) levels. In summary, enhanced GII.4 HuNoV infection was observed in the presence of HGM, and host microbiota was susceptible to disruption upon HuNoV infection.

Comparison of Whole-Genome Sequences of Legionella pneumophila in Tap Water and in Clinical Strains, Flint, Michigan, USA, 2016

During the water crisis in Flint, Michigan, USA (2014-2015), 2 outbreaks of Legionnaires' disease occurred in Genesee County, Michigan. We compared whole-genome sequences of 10 clinical Legionella pneumophila isolates submitted to a laboratory in Genesee County during the second outbreak with 103 water isolates collected the following year. We documented a genetically diverse range of L. pneumophila strains across clinical and water isolates. Isolates belonging to 1 clade (3 clinical isolates, 3 water isolates from a Flint hospital, 1 water isolate from a Flint residence, and the reference Paris strain) had a high degree of similarity (2-1,062 single-nucleotide polymorphisms), all L. pneumophila sequence type 1, serogroup 1. Serogroup 6 isolates belonging to sequence type 2518 were widespread in Flint hospital water samples but bore no resemblance to available clinical isolates. L. pneumophila strains in Flint tap water after the outbreaks were diverse and similar to some disease-causing strains.

Identification of discriminatory antibiotic resistance genes among environmental resistomes using extremely randomized tree algorithm

BACKGROUND

The interconnectivities of built and natural environments can serve as conduits for the proliferation and dissemination of antibiotic resistance genes (ARGs). Several studies have compared the broad spectrum of ARGs (i.e., "resistomes") in various environmental compartments, but there is a need to identify unique ARG occurrence patterns (i.e., "discriminatory ARGs"), characteristic of each environment. Such an approach will help to identify factors influencing ARG proliferation, facilitate development of relative comparisons of the ARGs distinguishing various environments, and help pave the way towards ranking environments based on their likelihood of contributing to the spread of clinically relevant antibiotic resistance. Here we formulate and demonstrate an approach using an extremely randomized tree (ERT) algorithm combined with a Bayesian optimization technique to capture ARG variability in environmental samples and identify the discriminatory ARGs. The potential of ERT for identifying discriminatory ARGs was first evaluated using in silico metagenomic datasets (simulated metagenomic Illumina sequencing data) with known variability. The application of ERT was then demonstrated through analyses using publicly available and in-house metagenomic datasets associated with (1) different aquatic habitats (e.g., river, wastewater influent, hospital effluent, and dairy farm effluent) to compare resistomes between distinct environments and (2) different river samples (i.e., Amazon, Kalamas, and Cam Rivers) to compare resistome characteristics of similar environments.

RESULTS

The approach was found to readily identify discriminatory ARGs in the in silico datasets. Also, it was not found to be biased towards ARGs with high relative abundance, which is a common limitation of feature projection methods, and instead only captured those ARGs that elicited significant profiles. Analyses of publicly available metagenomic datasets further demonstrated that the ERT approach can effectively differentiate real-world environmental samples and identify discriminatory ARGs based on pre-defined categorizing schemes.

CONCLUSIONS

Here a new methodology was formulated to characterize and compare variances in ARG profiles between metagenomic data sets derived from similar/dissimilar environments. Specifically, identification of discriminatory ARGs among samples representing various environments can be identified based on factors of interest. The methodology could prove to be a particularly useful tool for ARG surveillance and the assessment of the effectiveness of strategies for mitigating the spread of antibiotic resistance. The python package is hosted in the Git repository: https://github.com/gaarangoa/ExtrARG.

Effects of Dairy Manure-Based Amendments and Soil Texture on Lettuce- and Radish-Associated Microbiota and Resistomes

Dairy cattle are routinely treated with antibiotics, and the resulting manure or composted manure is commonly used as a soil amendment for crop production, raising questions regarding the potential for antibiotic resistance to propagate from "farm to fork." The objective of this study was to compare the microbiota and "resistomes" (i.e., carriage of antibiotic resistance genes [ARGs]) associated with lettuce leaf and radish taproot surfaces grown in different soils amended with dairy manure, compost, or chemical fertilizer only (control). Manure was collected from antibiotic-free dairy cattle (DC) or antibiotic-treated dairy cattle (DA), with a portion composted for parallel comparison. Amendments were applied to loamy sand or silty clay loam, and lettuce and radishes were cultivated to maturity in a greenhouse. Metagenomes were profiled via shotgun Illumina sequencing. Radishes carried a distinct ARG composition compared to that of lettuce, with greater relative abundance of total ARGs. Taxonomic species richness was also greater for radishes by 1.5-fold. The resistomes of lettuce grown with DC compost were distinct from those grown with DA compost, DC manure, or fertilizer only. Further, compost applied to loamy sand resulted in twofold-greater relative abundance of total ARGs on lettuce than when applied to silty clay loam. The resistomes of radishes grown with biological amendments were distinct from the corresponding fertilizer controls, but effects of composting or antibiotic use were not measureable. Cultivation in loamy sand resulted in higher species richness for both lettuce and radishes than when grown in silty clay loam by 2.2-fold and 1.2-fold, respectively, when amended with compost.IMPORTANCE A controlled, integrated, and replicated greenhouse study, along with comprehensive metagenomic analysis, revealed that multiple preharvest factors, including antibiotic use during manure collection, composting, biological soil amendment, and soil type, influence vegetable-borne resistomes. Here, radishes, a root vegetable, carried a greater load of ARGs and species richness than lettuce, a leafy vegetable. However, the lettuce resistome was more noticeably influenced by upstream antibiotic use and composting. Network analysis indicated that cooccurring ARGs and mobile genetic elements were almost exclusively associated with conditions receiving raw manure amendments, suggesting that composting could alleviate the mobility of manure-derived resistance traits. Effects of preharvest factors on associated microbiota and resistomes of vegetables eaten raw are worthy of further examination in terms of potential influence on human microbiomes and spread of antibiotic resistance. This research takes a step toward identifying on-farm management practices that can help mitigate the spread of agricultural sources of antibiotic resistance.

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

BACKGROUND

Growing 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.

RESULTS

Evaluation 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.

CONCLUSIONS

The 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 .

Apigenin Impacts the Growth of the Gut Microbiota and Alters the Gene Expression of Enterococcus

Apigenin is a major dietary flavonoid with many bioactivities, widely distributed in plants. Apigenin reaches the colon region intact and interacts there with the human gut microbiota, however there is little research on how apigenin affects the gut bacteria. This study investigated the effect of pure apigenin on human gut bacteria, at both the single strain and community levels. The effect of apigenin on the single gut bacteria strains Bacteroides galacturonicus, Bifidobacterium catenulatum, Lactobacillus rhamnosus GG, and Enterococcus caccae, was examined by measuring their anaerobic growth profiles. The effect of apigenin on a gut microbiota community was studied by culturing a fecal inoculum under in vitro conditions simulating the human ascending colon. 16S rRNA gene sequencing and GC-MS analysis quantified changes in the community structure. Single molecule RNA sequencing was used to reveal the response of Enterococcus caccae to apigenin. Enterococcus caccae was effectively inhibited by apigenin when cultured alone, however, the genus Enterococcus was enhanced when tested in a community setting. Single molecule RNA sequencing found that Enterococcus caccae responded to apigenin by up-regulating genes involved in DNA repair, stress response, cell wall synthesis, and protein folding. Taken together, these results demonstrate that apigenin affects both the growth and gene expression of Enterococcus caccae.

MetaStorm: A Public Resource for Customizable Metagenomics Annotation

Metagenomics is a trending research area, calling for the need to analyze large quantities of data generated from next generation DNA sequencing technologies. The need to store, retrieve, analyze, share, and visualize such data challenges current online computational systems. Interpretation and annotation of specific information is especially a challenge for metagenomic data sets derived from environmental samples, because current annotation systems only offer broad classification of microbial diversity and function. Moreover, existing resources are not configured to readily address common questions relevant to environmental systems. Here we developed a new online user-friendly metagenomic analysis server called MetaStorm (http://bench.cs.vt.edu/MetaStorm/), which facilitates customization of computational analysis for metagenomic data sets. Users can upload their own reference databases to tailor the metagenomics annotation to focus on various taxonomic and functional gene markers of interest. MetaStorm offers two major analysis pipelines: an assembly-based annotation pipeline and the standard read annotation pipeline used by existing web servers. These pipelines can be selected individually or together. Overall, MetaStorm provides enhanced interactive visualization to allow researchers to explore and manipulate taxonomy and functional annotation at various levels of resolution.

Noncoding RNAs that associate with YB-1 alter proliferation in prostate cancer cells

The highly conserved, multifunctional YB-1 is a powerful breast cancer prognostic indicator. We report on a pervasive role for YB-1 in which it associates with thousands of nonpolyadenylated short RNAs (shyRNAs) that are further processed into small RNAs (smyRNAs). Many of these RNAs have previously been identified as functional noncoding RNAs (http://www.johnlab.org/YB1). We identified a novel, abundant, 3'-modified short RNA antisense to Dicer1 (Shad1) that colocalizes with YB-1 to P-bodies and stress granules. The expression of Shad1 was shown to correlate with that of YB-1 and whose inhibition leads to an increase in cell proliferation. Additionally, Shad1 influences the expression of additional prognostic markers of cancer progression such as DLX2 and IGFBP2. We propose that the examination of these noncoding RNAs could lead to better understanding of prostate cancer progression.

Genome-wide transcript profiling reveals novel breast cancer-associated intronic sense RNAs

Non-coding RNAs (ncRNAs) play major roles in development and cancer progression. To identify novel ncRNAs that may identify key pathways in breast cancer development, we performed high-throughput transcript profiling of tumor and normal matched-pair tissue samples. Initial transcriptome profiling using high-density genome-wide tiling arrays revealed changes in over 200 novel candidate genomic regions that map to intronic regions. Sixteen genomic loci were identified that map to the long introns of five key protein-coding genes, CRIM1, EPAS1, ZEB2, RBMS1, and RFX2. Consistent with the known role of the tumor suppressor ZEB2 in the cancer-associated epithelial to mesenchymal transition (EMT), in situ hybridization reveals that the intronic regions deriving from ZEB2 as well as those from RFX2 and EPAS1 are down-regulated in cells of epithelial morphology, suggesting that these regions may be important for maintaining normal epithelial cell morphology. Paired-end deep sequencing analysis reveals a large number of distinct genomic clusters with no coding potential within the introns of these genes. These novel transcripts are only transcribed from the coding strand. A comprehensive search for breast cancer associated genes reveals enrichment for transcribed intronic regions from these loci, pointing to an underappreciated role of introns or mechanisms relating to their biology in EMT and breast cancer.

An in-depth map of polyadenylation sites in cancer

We present a comprehensive map of over 1 million polyadenylation sites and quantify their usage in major cancers and tumor cell lines using direct RNA sequencing. We built the Expression and Polyadenylation Database to enable the visualization of the polyadenylation maps in various cancers and to facilitate the discovery of novel genes and gene isoforms that are potentially important to tumorigenesis. Analyses of polyadenylation sites indicate that a large fraction (∼30%) of mRNAs contain alternative polyadenylation sites in their 3' untranslated regions, independent of the cell type. The shortest 3' untranslated region isoforms are preferentially upregulated in cancer tissues, genome-wide. Candidate targets of alternative polyadenylation-mediated upregulation of short isoforms include POLR2K, and signaling cascades of cell-cell and cell-extracellular matrix contact, particularly involving regulators of Rho GTPases. Polyadenylation maps also helped to improve 3' untranslated region annotations and identify candidate regulatory marks such as sequence motifs, H3K36Me3 and Pabpc1 that are isoform dependent and occur in a position-specific manner. In summary, these results highlight the need to go beyond monitoring only the cumulative transcript levels for a gene, to separately analysing the expression of its RNA isoforms.