Using metagenomics to investigate human and environmental resistomes

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences

BACKGROUND

Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing.

RESULTS

In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG.

CONCLUSIONS

ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract.

Characterising global antimicrobial resistance research explains why One Health solutions are slow in development: An application of AI-based gap analysis

The global health crisis posed by increasing antimicrobial resistance (AMR) implicitly requires solutions based a One Health approach, yet multisectoral, multidisciplinary research on AMR is rare and huge knowledge gaps exist to guide integrated action. This is partly because a comprehensive survey of past research activity has never performed due to the massive scale and diversity of published information. Here we compiled 254,738 articles on AMR using Artificial Intelligence (AI; i.e., Natural Language Processing, NLP) methods to create a database and information retrieval system for knowledge extraction on research perfomed over the last 20 years. Global maps were created that describe regional, methodological, and sectoral AMR research activities that confirm limited intersectoral research has been performed, which is key to guiding science-informed policy solutions to AMR, especially in low-income countries (LICs). Further, we show greater harmonisation in research methods across sectors and regions is urgently needed. For example, differences in analytical methods used among sectors in AMR research, such as employing culture-based versus genomic methods, results in poor communication between sectors and partially explains why One Health-based solutions are not ensuing. Therefore, our analysis suggest that performing culture-based and genomic AMR analysis in tandem in all sectors is crucial for data integration and holistic One Health solutions. Finally, increased investment in capacity development in LICs should be prioritised as they are places where the AMR burden is often greatest. Our open-access database and AI methodology can be used to further develop, disseminate, and create new tools and practices for AMR knowledge and information sharing.

Rapid Detection of Antimicrobial Resistance Genes in Critically Ill Children Using a Custom TaqMan Array Card

Bacteria are identified in only 22% of critically ill children with respiratory infections treated with antimicrobial therapy. Once an organism is isolated, antimicrobial susceptibility results (phenotypic testing) can take another day. A rapid diagnostic test identifying antimicrobial resistance (AMR) genes could help clinicians make earlier, informed antimicrobial decisions. Here we aimed to validate a custom AMR gene TaqMan Array Card (AMR-TAC) for the first time and assess its feasibility as a screening tool in critically ill children. An AMR-TAC was developed using a combination of commercial and bespoke targets capable of detecting 23 AMR genes. This was validated using isolates with known phenotypic resistance. The card was then tested on lower respiratory tract and faecal samples obtained from mechanically ventilated children in a single-centre observational study of respiratory infection. There were 82 children with samples available, with a median age of 1.2 years. Major comorbidity was present in 29 (35%) children. A bacterial respiratory pathogen was identified in 13/82 (16%) of children, of which 4/13 (31%) had phenotypic AMR. One AMR gene was detected in 49/82 (60%), and multiple AMR genes were detected in 14/82 (17%) children. Most AMR gene detections were not associated with the identification of phenotypic AMR. AMR genes are commonly detected in samples collected from mechanically ventilated children with suspected respiratory infections. AMR-TAC may have a role as an adjunct test in selected children in whom there is a high suspicion of antimicrobial treatment failure.

Distinctive hospital and community resistomes in Scottish urban wastewater: Metagenomics of a paired wastewater sampling design

The wastewater microbiome contains a multitude of resistant bacteria of human origin, presenting an opportunity for surveillance of resistance in the general population. However, wastewater microbial communities are also influenced by clinical sources, such as hospitals. Identifying signatures of the community and hospital resistome in wastewater is needed for interpretation and risk analysis. In this study, we compare the resistome and microbiome of hospital, community, and mixed municipal wastewater to investigate how and why the composition of these different sites differ. We conducted shotgun metagenomic analysis on wastewater samples from eight wastewater treatment plants (WWTPs), four hospitals, and four community sites in Scotland, using a paired sampling design. Cluster analysis and source attribution random forest models demonstrated that the hospital resistome was distinct from community and WWTP resistomes. Hospital wastewater had a higher abundance and diversity of resistance genes, in keeping with evidence that hospitals act as a reservoir and enricher of resistance. However, this distinctive 'hospital' signature appeared to be weak in the resistome of downstream WWTPs, likely due to dilution. We conclude that hospital and community wastewater resistomes differ, with the hospital wastewater representing a reservoir of patient- and hospital environment-associated bacteria. However, this 'hospital' signature is transient and does not overwhelm the community signature in the resistome of the downstream WWTP influent.

NCRD: A non-redundant comprehensive database for detecting antibiotic resistance genes

Antibiotic resistance genes (ARGs) are emerging pollutants present in various environments. Identifying ARGs has become a growing concern in recent years. Several databases, including the Antibiotic Resistance Genes Database (ARDB), Comprehensive Antibiotic Resistance Database (CARD), and Structured Antibiotic Resistance Genes (SARG), have been applied to detect ARGs. However, these databases have limitations, which hinder the comprehensive profiling of ARGs in environmental samples. To address these issues, we constructed a non-redundant antibiotic resistance genes database (NRD) by consolidating sequences from ARDB, CARD, and SARG. We identified the homologous proteins of NRD from Non-redundant Protein Database (NR) and the Protein DataBank Database (PDB) and clustered them to establish a non-redundant comprehensive antibiotic resistance genes database (NCRD) with similarities of 100% (NCRD100) and 95% (NCRD95). To demonstrate the advantages of NCRD, we compared it with other databases by using metagenome datasets. Results revealed its strong ability in detecting potential ARGs.

Towards monitoring of antimicrobial resistance in the environment: For what reasons, how to implement it, and what are the data needs?

Antimicrobial resistance (AMR) is a global threat to human and animal health and well-being. To understand AMR dynamics, it is important to monitor resistant bacteria and resistance genes in all relevant settings. However, while monitoring of AMR has been implemented in clinical and veterinary settings, comprehensive monitoring of AMR in the environment is almost completely lacking. Yet, the environmental dimension of AMR is critical for understanding the dissemination routes and selection of resistant microorganisms, as well as the human health risks related to environmental AMR. Here, we outline important knowledge gaps that impede implementation of environmental AMR monitoring. These include lack of knowledge of the 'normal' background levels of environmental AMR, definition of high-risk environments for transmission, and a poor understanding of the concentrations of antibiotics and other chemical agents that promote resistance selection. Furthermore, there is a lack of methods to detect resistance genes that are not already circulating among pathogens. We conclude that these knowledge gaps need to be addressed before routine monitoring for AMR in the environment can be implemented on a large scale. Yet, AMR monitoring data bridging different sectors is needed in order to fill these knowledge gaps, which means that some level of national, regional and global AMR surveillance in the environment must happen even without all scientific questions answered. With the possibilities opened up by rapidly advancing technologies, it is time to fill these knowledge gaps. Doing so will allow for specific actions against environmental AMR development and spread to pathogens and thereby safeguard the health and wellbeing of humans and animals.

Targeted metagenomics using bait-capture to detect antibiotic resistance genes in retail meat and seafood

Metagenomics analysis of foods has the potential to provide comprehensive data on the presence and prevalence of antimicrobial resistance (AMR) genes in the microbiome of foods. However, AMR genes are generally present in low abundance compared to other bacterial genes in the food microbiome and consequently require multiple rounds of in-depth sequencing for detection. Here, a metagenomics approach, using bait-capture probes targeting antimicrobial resistance and plasmid genes, is used to characterize the resistome and plasmidome of retail beef, chicken, oyster, shrimp, and veal enrichment cultures (n = 15). Compared to total shotgun metagenomics, bait-capture required approximately 40-fold fewer sequence reads to detect twice the number of AMR gene classes, AMR gene families, and plasmid genes across all sample types. For the detection of critically important extended spectrum beta-lactamase (ESBL) genes the bait capture method had a higher overall positivity rate (44%) compared to shotgun metagenomics (26%), and a culture-based method (29%). Overall, the results support the use of bait-capture for the identification of low abundance genes such as AMR genes from food samples.

New insight into the effect of microplastics on antibiotic resistance and bacterial community of biofilm

Microplastics (MPs) could serve as substrates for microbial colonization and biofilm formation. However, research on the effects of different types of microplastics and natural substrates on biofilm formation and community structure in the presence of antibiotic-resistant bacteria (ARB) is limited. In this study, we employed by means of microcosm experiments to analyze the situation of biofilms conditions, bacterial resistance patterns, antibiotic resistance genes (ARGs) distribution, and bacterial community on different substrates using microbial cultivation, high throughtput sequencing and PCR. The result showed that biofilms on different substrates markedly increased with time, with MPs surfaces formed more biofilm than stone. Analyses of antibiotic resistant showed negligible differences in the resistance rate to the same antibiotic at 30 d, but tetB would be selectively enriched on PP and PET. The microbial communities associated with biofilms on MPs and stones exhibited variations during different stages of formation. Notably, phylum WPS-2 and Epsilonbacteraeota were identified as the dominant microbiomes of biofilms on MPs and stones at 30 d, respectively. Correlation analysis suggested that WPS-2 could potentially be a tetracycline-resistant bacterium, while Epsilonbacteraeota did not correlate with any detected ARB. Our results emphasized the potential threat posed by MPs as attachment carriers for bacteria, particularly ARB, in aquatic environments.

Emerging Increase in Colistin Resistance Rates in Escherichia coli and Salmonella enterica from Türkiye

Foodborne infections caused by drug-resistant Salmonella spp. are a global health concern. Moreover, commensal Escherichia coli is considered risky due to the presence of antimicrobial resistance genes. Colistin is considered a last-resort antibiotic against Gram-negative bacterial infections. Colistin resistance can be transferred both vertically, and horizontally via conjugation between bacterial species. Plasmid-mediated resistance has been associated with mcr-1 to mcr-10 genes. In this study, we collected food samples (n = 238), and isolated E. coli (n = 36) and Salmonella (n = 16), representing recent isolates. We included previously collected Salmonella (n = 197) and E. coli (n = 56) from various sources from 2010 to 2015 in Türkiye as representing historical isolates to investigate colistin-resistance over time. In all isolates, colistin resistance was screened phenotypically by minimum inhibitory concentration (MIC), and then in resistant isolates, mcr-1 to mcr-5 genes were further screened. In addition, the antibiotic resistance of recent isolates was determined, and antibiotic resistance genes were investigated. We found that in total 20 Salmonella isolates (9.38%) and 23 of the E. coli isolates (25%) showed phenotypic colistin resistance. Interestingly, the majority of colistin-resistant isolates (N:32) had resistance levels above 128 mg/L. Furthermore 75% of commensal E. coli isolates recently isolated were resistant at least 3 antibiotics. Overall, we found that the colistin resistance has been increased from 8.12 to 25% in Salmonella isolates, and 7.14% to 52.8% in E. coli isolates over time. However, none of these resistant isolates carried mcr genes, most likely indicating emerging chromosomal colistin resistance.

Misunderstandings and misinterpretations: Antimicrobial use and resistance in salmon aquaculture

The exponential growth of aquaculture over the past 30 years has been accompanied by a parallel increase in the use of antimicrobials. This widespread use has had negative effects on animal, human and environmental health and affected the biodiversity of the environments where aquaculture takes place. Results showing these harmful effects have been resisted and made light of by the aquaculture industry and their scientific supporters through introduction of misunderstandings and misinterpretations of concepts developed in the evolution, genetics, and molecular epidemiology of antimicrobial resistance. We focus on a few of the most obvious scientific shortcomings and biases of two recent attempts to minimise the negative impacts of excessive antimicrobial use in Chilean salmon aquaculture on human and piscine health and on the environment. Such open debate is critical to timely implementation of effective regulation of antimicrobial usage in salmon aquaculture in Chile, if the negative local and worldwide impacts of this usage are to be avoided.

Interplay between strain fitness and transmission frequency determines prevalence of antimicrobial resistance

The steep rise of infections caused by bacteria that are resistant to antimicrobial agents threatens global health. However, the association between antimicrobial use and the prevalence of resistance is not straightforward. Therefore, it is necessary to quantify the importance of additional factors that affect this relationship. We theoretically explore how the prevalence of resistance is affected by the combination of three factors: antimicrobial use, bacterial transmission, and fitness cost of resistance. We present a model that combines within-host, between-hosts and between-populations dynamics, built upon the competitive Lotka-Volterra equations. We developed the model in a manner that allows future experimental validation of the findings with single isolates in the laboratory. Each host may carry two strains (susceptible and resistant) that represent the host’s commensal microbiome and are not the target of the antimicrobial treatment. The model simulates a population of hosts who are treated periodically with antibiotics and transmit bacteria to each other. We show that bacterial transmission results in strain co-existence. Transmission disseminates resistant bacteria in the population, increasing the levels of resistance. Counterintuitively, when the cost of resistance is low, high transmission frequencies reduce resistance prevalence. Transmission between host populations leads to more similar resistance levels, increasing the susceptibility of the population with higher antimicrobial use. Overall, our results indicate that the interplay between bacterial transmission and strain fitness affects the prevalence of resistance in a non-linear way. We then place our results within the context of ecological theory, particularly on temporal niche partitioning and metapopulation rescue, and we formulate testable experimental predictions for future research.

Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes

BACKGROUND

Bacterial communities in humans, animals, and the external environment maintain a large collection of antibiotic resistance genes (ARGs). However, few of these ARGs are well-characterized and thus established in existing resistance gene databases. In contrast, the remaining latent ARGs are typically unknown and overlooked in most sequencing-based studies. Our view of the resistome and its diversity is therefore incomplete, which hampers our ability to assess risk for promotion and spread of yet undiscovered resistance determinants.

RESULTS

A reference database consisting of both established and latent ARGs (ARGs not present in current resistance gene repositories) was created. By analyzing more than 10,000 metagenomic samples, we showed that latent ARGs were more abundant and diverse than established ARGs in all studied environments, including the human- and animal-associated microbiomes. The pan-resistomes, i.e., all ARGs present in an environment, were heavily dominated by latent ARGs. In comparison, the core-resistome, i.e., ARGs that were commonly encountered, comprised both latent and established ARGs. We identified several latent ARGs shared between environments and/or present in human pathogens. Context analysis of these genes showed that they were located on mobile genetic elements, including conjugative elements. We, furthermore, identified that wastewater microbiomes had a surprisingly large pan- and core-resistome, which makes it a potentially high-risk environment for the mobilization and promotion of latent ARGs.

CONCLUSIONS

Our results show that latent ARGs are ubiquitously present in all environments and constitute a diverse reservoir from which new resistance determinants can be recruited to pathogens. Several latent ARGs already had high mobile potential and were present in human pathogens, suggesting that they may constitute emerging threats to human health. We conclude that the full resistome-including both latent and established ARGs-needs to be considered to properly assess the risks associated with antibiotic selection pressures. Video Abstract.

Target-enriched long-read sequencing (TELSeq) contextualizes antimicrobial resistance genes in metagenomes

BACKGROUND

Metagenomic data can be used to profile high-importance genes within microbiomes. However, current metagenomic workflows produce data that suffer from low sensitivity and an inability to accurately reconstruct partial or full genomes, particularly those in low abundance. These limitations preclude colocalization analysis, i.e., characterizing the genomic context of genes and functions within a metagenomic sample. Genomic context is especially crucial for functions associated with horizontal gene transfer (HGT) via mobile genetic elements (MGEs), for example antimicrobial resistance (AMR). To overcome this current limitation of metagenomics, we present a method for comprehensive and accurate reconstruction of antimicrobial resistance genes (ARGs) and MGEs from metagenomic DNA, termed target-enriched long-read sequencing (TELSeq).

RESULTS

Using technical replicates of diverse sample types, we compared TELSeq performance to that of non-enriched PacBio and short-read Illumina sequencing. TELSeq achieved much higher ARG recovery (>1,000-fold) and sensitivity than the other methods across diverse metagenomes, revealing an extensive resistome profile comprising many low-abundance ARGs, including some with public health importance. Using the long reads generated by TELSeq, we identified numerous MGEs and cargo genes flanking the low-abundance ARGs, indicating that these ARGs could be transferred across bacterial taxa via HGT.

CONCLUSIONS

TELSeq can provide a nuanced view of the genomic context of microbial resistomes and thus has wide-ranging applications in public, animal, and human health, as well as environmental surveillance and monitoring of AMR. Thus, this technique represents a fundamental advancement for microbiome research and application. Video abstract.

Antibiotic Resistance Genes in River Biofilms: A Metagenomic Approach toward the Identification of Sources and Candidate Hosts

Treated wastewater is a major pathway by which antibiotic resistance genes (ARG) enter aquatic ecosystems. However, knowledge gaps remain concerning the dissemination of specific ARG and their association with bacterial hosts. Here, we employed shotgun metagenomics to track ARG and taxonomic markers in river biofilms along a gradient of fecal pollution depicted by crAssphage signatures. We found strong evidence for an impact of wastewater effluents on both community composition and resistomes. In the light of such simultaneity, we employed a model comparison technique to identify ARG-host relationships from nonassembled metagenomic DNA. Hereby, a major cause of spurious associations otherwise encountered in correlation-based ARG-host analyses was suppressed. For several families of ARG, namely those conferring resistance to beta-lactams, particular bacterial orders were identified as candidate hosts. The found associations of blaFOX and cphA with Aeromonadales or blaPER with Chromatiales support the outcome of independent evolutionary analyses and thus confirm the potential of the methodology. For other ARG families including blaIMP or tet, clusters of bacterial orders were identified which potentially harbor a major proportion of host species. For yet other ARG, like, for example, ant or erm, no particular host candidates were identifiable, indicating their spread across various taxonomic groups.

Next-generation sequencing and PCR technologies in monitoring the hospital microbiome and its drug resistance

The hospital environment significantly contributes to the onset of healthcare-associated infections (HAIs), which represent one of the most frequent complications occurring in healthcare facilities worldwide. Moreover, the increased antimicrobial resistance (AMR) characterizing HAI-associated microbes is one of the human health’s main concerns, requiring the characterization of the contaminating microbial population in the hospital environment. The monitoring of surface microbiota in hospitals is generally addressed by microbial cultural isolation. However, this has some important limitations mainly relating to the inability to define the whole drug-resistance profile of the contaminating microbiota and to the long time period required to obtain the results. Hence, there is an urgent need to implement environmental surveillance systems using more effective methods. Molecular approaches, including next-generation sequencing and PCR assays, may be useful and effective tools to monitor microbial contamination, especially the growing AMR of HAI-associated pathogens. Herein, we summarize the results of our recent studies using culture-based and molecular analyses in 12 hospitals for adults and children over a 5-year period, highlighting the advantages and disadvantages of the techniques used.

Antibiotic resistance in chronic respiratory diseases: from susceptibility testing to the resistome

The development of resistome analysis, i.e. the comprehensive analysis of antibiotic-resistance genes (ARGs), is enabling a better understanding of the mechanisms of antibiotic-resistance emergence. The respiratory microbiome is a dynamic and interactive network of bacteria, with a set of ARGs that could influence the response to antibiotics. Viruses such as bacteriophages, potential carriers of ARGs, may also form part of this respiratory resistome. Chronic respiratory diseases (CRDs) such as cystic fibrosis, severe asthma, chronic obstructive pulmonary disease and bronchiectasis, managed with long-term antibiotic therapies, lead to multidrug resistance. Antibiotic susceptibility testing provides a partial view of the bacterial response to antibiotics in the complex lung environment. Assessing the ARG network would allow personalised, targeted therapeutic strategies and suitable antibiotic stewardship in CRDs, depending on individual resistome and microbiome signatures. This review summarises the influence of pulmonary antibiotic protocols on the respiratory microbiome, detailing the variable consequences according to antibiotic class and duration of treatment. The different resistome-profiling methods are explained to clarify their respective place in antibiotic-resistance analysis in the lungs. Finally, this review details current knowledge on the respiratory resistome related to therapeutic strategies and provides insight into the application of resistome analysis to counter the emergence of multidrug-resistant respiratory pathogens.

Monitoring antibiotic resistance genes in wastewater environments: The challenges of filling a gap in the One-Health cycle

Antibiotic resistance (AR) is a global problem requiring international cooperation and coordinated action. Global monitoring must rely on methods available and comparable across nations to quantify AR occurrence and identify sources and reservoirs, as well as paths of AR dissemination. Numerous analytical tools that are gaining relevance in microbiology, have the potential to be applied to AR research. This review summarizes the state of the art of AR monitoring methods, considering distinct needs, objectives and available resources. Based on the overview of distinct approaches that are used or can be adapted to monitor AR, it is discussed the potential to establish reliable and useful monitoring schemes that can be implemented in distinct contexts. This discussion places the environmental monitoring within the One-Health approach, where two types of risk, dissemination across distinct environmental compartments, and transmission to humans, must be considered. The plethora of methodological approaches to monitor AR and the variable features of the monitored sites challenge the capacity of the scientific community and policy makers to reach a common understanding. However, the dialogue between different methods and the production of action-oriented data is a priority. The review aims to warm up this discussion.

Antibiotic resistance in the environment

Antibiotic resistance is a global health challenge, involving the transfer of bacteria and genes between humans, animals and the environment. Although multiple barriers restrict the flow of both bacteria and genes, pathogens recurrently acquire new resistance factors from other species, thereby reducing our ability to prevent and treat bacterial infections. Evolutionary events that lead to the emergence of new resistance factors in pathogens are rare and challenging to predict, but may be associated with vast ramifications. Transmission events of already widespread resistant strains are, on the other hand, common, quantifiable and more predictable, but the consequences of each event are limited. Quantifying the pathways and identifying the drivers of and bottlenecks for environmental evolution and transmission of antibiotic resistance are key components to understand and manage the resistance crisis as a whole. In this Review, we present our current understanding of the roles of the environment, including antibiotic pollution, in resistance evolution, in transmission and as a mere reflection of the regional antibiotic resistance situation in the clinic. We provide a perspective on current evidence, describe risk scenarios, discuss methods for surveillance and the assessment of potential drivers, and finally identify some actions to mitigate risks.

Early-life formula feeding is associated with infant gut microbiota alterations and an increased antibiotic resistance load

BACKGROUND

Infants are at a high risk of acquiring fatal infections, and their treatment relies on functioning antibiotics. Antibiotic resistance genes (ARGs) are present in high numbers in antibiotic-naive infants' gut microbiomes, and infant mortality caused by resistant infections is high. The role of antibiotics in shaping the infant resistome has been studied, but there is limited knowledge on other factors that affect the antibiotic resistance burden of the infant gut.

OBJECTIVES

Our objectives were to determine the impact of early exposure to formula on the ARG load in neonates and infants born either preterm or full term. Our hypotheses were that diet causes a selective pressure that influences the microbial community of the infant gut, and formula exposure would increase the abundance of taxa that carry ARGs.

METHODS

Cross-sectionally sampled gut metagenomes of 46 neonates were used to build a generalized linear model to determine the impact of diet on ARG loads in neonates. The model was cross-validated using neonate metagenomes gathered from public databases using our custom statistical pipeline for cross-validation.

RESULTS

Formula-fed neonates had higher relative abundances of opportunistic pathogens such as Staphylococcus aureus, Staphylococcus epidermidis, Klebsiella pneumoniae, Klebsiella oxytoca, and Clostridioides difficile. The relative abundance of ARGs carried by gut bacteria was 69% higher in the formula-receiving group (fold change, 1.69; 95% CI: 1.12-2.55; P = 0.013; n = 180) compared to exclusively human milk-fed infants. The formula-fed infants also had significantly less typical infant bacteria, such as Bifidobacteria, that have potential health benefits.

CONCLUSIONS

The novel finding that formula exposure is correlated with a higher neonatal ARG burden lays the foundation that clinicians should consider feeding mode in addition to antibiotic use during the first months of life to minimize the proliferation of antibiotic-resistant gut bacteria in infants.

MetaMLP: A Fast Word Embedding Based Classifier to Profile Target Gene Databases in Metagenomic Samples

The functional profile of metagenomic samples enables improved understanding of microbial populations in the environment. Such analysis consists of assigning short sequencing reads to a particular functional category. Normally, manually curated databases are used for functional assignment, and genes are arranged into different classes. Sequence alignment has been widely used to profile metagenomic samples against curated databases. However, this method is time consuming and requires high computational resources. While several alignment-free methods based on k-mer composition have been developed in recent years, they still require large amounts of computer main memory. In this article, MetaMLP (Metagenomics Machine Learning Profiler), a machine learning method that represents sequences as numerical vectors (embeddings) and uses a simple one hidden layer neural network to profile functional categories, is developed. Unlike other methods, MetaMLP enables partial matching by using a reduced alphabet to build sequence embeddings from full and partial k-mers. MetaMLP is able to identify a slightly larger number of reads compared with DIAMOND (one of the fastest sequence alignment methods), as well as to perform accurate predictions with 0.99 precision and 0.99 recall. MetaMLP can process 100M reads in ∼10 minutes on a laptop computer, which is 50 times faster than DIAMOND.

Metagenomic Analysis Reveals the Fate of Antibiotic Resistance Genes in a Full-Scale Wastewater Treatment Plant in Egypt

Wastewater treatment plants (WWTPs) are recognized as hotspots for the dissemination of antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARBs) in the environment. Our study utilized a high-throughput sequencing-based metagenomic analysis approach to compare the ARG abundance profiles of the raw sewage, treated effluent and activated sludge samples from a full-scale WWTP in Egypt. In addition, the difference in microbial community composition due to the treatment process was assessed. As a result, 578 ARG subtypes (resistance genes) belonging to 18 ARG types (antibiotic resistance classes) were identified. ARGs encoding for resistance against multidrug, aminoglycoside, bacitracin, beta-lactam, sulfonamide, and tetracycline antibiotics were the most abundant types. The total removal efficiency percentage of ARGs in the WWTP was found to be 98% however, the ARG persistence results indicated that around 68% of the ARGs in the influent could be found in the treated effluent. This finding suggests that the treated wastewater poses a potential risk for the ARG dissemination in bacterial communities of the receiving water bodies via horizontal gene transfer (HGT). The community composition at phylum level showed that Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria were the most abundant phyla in all datasets. Although the relative abundance of several pathogenic bacteria in the influent declined to less than 1% in the effluent, the taxonomic assignments at species level for the effluent and sludge metagenomes demonstrated that clinically important pathogens such as Escherichia coli, Klebsiella pneumonia, and Aeromonas caviae were present. Overall, the results of this study would hopefully enhance our knowledge about the abundance profiles of ARGs and their fate in different wastewater treatment compartments that have never been examined before.

Distribution of antibiotic resistance genes in the environment

The prevalence of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in the microbiome is a major public health concern globally. Many habitats in the environment are under threat due to excessive use of antibiotics and evolutionary changes occurring in the resistome. ARB and ARGs from farms, cities and hospitals, wastewater treatment plants (WWTPs) or as water runoffs, may accumulate in water, soil, and air. We present a global picture of the resistome by examining ARG-related papers retrieved from PubMed and published in the last 30 years (1990-2020). Natural Language Processing (NLP) was used to retrieve 496,640 papers, out of which 9374 passed the filtering test and were further analyzed to determine the distribution and diversity of ARG subtypes. The papers revealed seven major antibiotic families together with their respective ARG subtypes in different habitats on six continents. Asia, especially China, had the highest number of ARGs related papers compared to other countries/regions/continents. ARGs belonging to multidrug, glycopeptide, and β-lactam families were the most common in reports from hospitals and sulfonamide and tetracycline families were common in reports from farms, WWTPs, water and soil. We also highlight the 'omics' tools used in resistome research, describe some factors that shape the development of resistome, and suggest future work needed to better understand the resistome. The goal was to show the global nature of ARB and ARGs in order to encourage collaborate research efforts aimed at reducing the negative impacts of antibiotic resistance on the One Health concept.

EpicPCR 2.0: Technical and Methodological Improvement of a Cutting-Edge Single-Cell Genomic Approach

EpicPCR (Emulsion, Paired Isolation and Concatenation PCR) is a recent single-cell genomic method based on a fusion-PCR allowing us to link a functional sequence of interest to a 16S rRNA gene fragment and use the mass sequencing of the resulting amplicons for taxonomic assignment of the functional sequence-carrying bacteria. Although it is interesting because it presents the highest efficiency for assigning a bacterial host to a marker, epicPCR remains a complex multistage procedure with technical difficulties that may easily impair the approach depth and quality. Here, we described how to adapt epicPCR to new gene targets and environmental matrices while identifying the natural host range of SXT/R391 integrative and conjugative elements in water microbial communities from the Meurthe River (France). We notably show that adding a supplementary PCR step allowed us to increase the amplicon yield and thus the number of reads obtained after sequencing. A comparison of operational taxonomic unit (OTU) identification approaches when using biological and technical replicates demonstrated that, although OTUs can be validated when obtained from three out of three technical replicates, up to now, results obtained from two or three biological replicates give a similar and even a better confidence level in OTU identification, while allowing us to detect poorly represented SXT/R391 hosts in microbial communities.

More diversified antibiotic resistance genes in chickens and workers of the live poultry markets

BACKGROUND

Poultry farms and LPMs are a reservoir of antimicrobial resistant bacteria and resistance genes from feces. The LPM is an important interface between humans, farm animals, and environments in a typical urban environment, and it is considered a reservoir for ARGs and viruses. However, the antibiotic resistomes shared between chicken farms and LPMs, and that of LPM workers and people who have no contact with the LPMs remains unknown.

METHODS

We characterized the resistome and bacterial microbiome of farm chickens and LPMs and LPM workers and control subjects. The mobile ARGs identified in chickens and the distribution of the mcr-family genes in publicly bacterial genomes and chicken gut metagenomes was analyzed, respectively. In addition, the prevalence of mcr-1 in LPMs following the ban on colistin-positive additives in China was explored.

RESULTS

By profiling the microbiomes and resistomes in chicken farms, LPMs, LPM workers, and LPM environments, we found that the bacterial community composition and resistomes were significantly different between the farms and the LPMs, and the LPM samples possessed more diversified ARGs (59 types) than the farms. Some mobile ARGs, such as mcr-1 and tet(X3), identified in chicken farms, LPMs, LPM workers, and LPM environments were also harbored by human clinical pathogens. Moreover, we found that the resistomes were significantly different between the LPM workers and those who have no contact with the LPMs, and more diversified ARGs (188 types) were observed in the LPM workers. It is also worth noting that mcr-10 was identified in both human (5.2%, 96/1,859) and chicken (1.5%, 14/910) gut microbiomes. Although mcr-1 prevalence decreased significantly in the LPMs across the eight provinces in China, from 190/333 (57.1%) samples in September 2016-March 2017 to 208/544 (38.2%) samples in August 2018-May 2019, it is widespread and continuous in the LPMs.

CONCLUSION

Live poultry trade has a significant effect on the diversity of ARGs in LPM workers, chickens, and environments in China, driven by human selection with the live poultry trade. Our findings highlight the live poultry trade as ARG disseminators into LPMs, which serve as an interface of LPM environments even LPM workers, and that could urge Government to have better control of LPMs in China. Further studies on the factors that promote antibiotic resistance exchange between LPM environments, human commensals, and pathogens, are warranted.

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

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.

Selection of antibiotic resistance genes on biodegradable and non-biodegradable microplastics

Growing evidence have demonstrated that microplastics in the marine ecosystem can provide novel substrates for biofilm formation, potentially facilitating the spread of antibiotic resistance. However, the occurrence of antibiotic resistance genes (ARGs) in the biofilm on microplastics has not been fully explored. This study used the metagenomic data of biodegradable and non-biodegradable microplastics staged at a coastal lagoon in the northern Gulf of Mexico to profile the ARGs and their bacterial hosts. The abundance and Shannon diversity of ARGs on biodegradable poly hydroxy alkanoate (PHA) and non-biodegradable polyethylene terephthalate (PET) have no significant differences. Nevertheless, the abundance of multidrug resistance genes on PET (3.05 copies per 16S rRNA) was statistically higher than that on PHA (2.05). Beta diversity showed that the overall pattern of resistome on PHA was significantly distinct with that on PET. Procrustes analysis suggested a good-fit correlation between ARG profiles and bacterial community composition. The host-tracking analysis identified that Pseudomonas was always the major host for glycopeptide and multidrug resistance genes in PET and PHA biofilms, whereas the primary host for macrolide-lincosamide-streptogramin (MLS) changed to Desulfovibrio on PET. This study provided the first metagenomic insights into the ARGs and their hosts on biodegradable and non-biodegradable microplastics, suggesting that both two types of plastics harbor ARGs with preferences.

ResistoXplorer: a web-based tool for visual, statistical and exploratory data analysis of resistome data

The study of resistomes using whole metagenomic sequencing enables high-throughput identification of resistance genes in complex microbial communities, such as the human microbiome. Over recent years, sophisticated and diverse pipelines have been established to facilitate raw data processing and annotation. Despite the progress, there are no easy-to-use tools for comprehensive visual, statistical and functional analysis of resistome data. Thus, exploration of the resulting large complex datasets remains a key bottleneck requiring robust computational resources and technical expertise, which creates a significant hurdle for advancements in the field. Here, we introduce ResistoXplorer, a user-friendly tool that integrates recent advancements in statistics and visualization, coupled with extensive functional annotations and phenotype collection, to enable high-throughput analysis of common outputs generated from metagenomic resistome studies. ResistoXplorer contains three modules—the ‘Antimicrobial Resistance Gene Table’ module offers various options for composition profiling, functional profiling and comparative analysis of resistome data; the ‘Integration’ module supports integrative exploratory analysis of resistome and microbiome abundance profiles derived from metagenomic samples; finally, the ‘Antimicrobial Resistance Gene List’ module enables users to intuitively explore the associations between antimicrobial resistance genes and the microbial hosts using network visual analytics to gain biological insights. ResistoXplorer is publicly available at http://www.resistoxplorer.no.

AgroSeek: a system for computational analysis of environmental metagenomic data and associated metadata

BACKGROUND

Metagenomics is gaining attention as a powerful tool for identifying how agricultural management practices influence human and animal health, especially in terms of potential to contribute to the spread of antibiotic resistance. However, the ability to compare the distribution and prevalence of antibiotic resistance genes (ARGs) across multiple studies and environments is currently impossible without a complete re-analysis of published datasets. This challenge must be addressed for metagenomics to realize its potential for helping guide effective policy and practice measures relevant to agricultural ecosystems, for example, identifying critical control points for mitigating the spread of antibiotic resistance.

RESULTS

Here we introduce AgroSeek, a centralized web-based system that provides computational tools for analysis and comparison of metagenomic data sets tailored specifically to researchers and other users in the agricultural sector interested in tracking and mitigating the spread of ARGs. AgroSeek draws from rich, user-provided metagenomic data and metadata to facilitate analysis, comparison, and prediction in a user-friendly fashion. Further, AgroSeek draws from publicly-contributed data sets to provide a point of comparison and context for data analysis. To incorporate metadata into our analysis and comparison procedures, we provide flexible metadata templates, including user-customized metadata attributes to facilitate data sharing, while maintaining the metadata in a comparable fashion for the broader user community and to support large-scale comparative and predictive analysis.

CONCLUSION

AgroSeek provides an easy-to-use tool for environmental metagenomic analysis and comparison, based on both gene annotations and associated metadata, with this initial demonstration focusing on control of antibiotic resistance in agricultural ecosystems. Agroseek creates a space for metagenomic data sharing and collaboration to assist policy makers, stakeholders, and the public in decision-making. AgroSeek is publicly-available at https://agroseek.cs.vt.edu/ .

Metagenomic Approaches to Analyze Antimicrobial Resistance: An Overview

Antimicrobial resistance is a major global public health problem, which develops when pathogens acquire antimicrobial resistance genes (ARGs), primarily through genetic recombination between commensal and pathogenic microbes. The resistome is a collection of all ARGs. In microorganisms, the primary method of ARG acquisition is horizontal gene transfer (HGT). Thus, understanding and identifying HGTs, can provide insight into the mechanisms of antimicrobial resistance transmission and dissemination. The use of high-throughput sequencing technologies has made the analysis of ARG sequences feasible and accessible. In particular, the metagenomic approach has facilitated the identification of community-based antimicrobial resistance. This approach is useful, as it allows access to the genomic data in an environmental sample without the need to isolate and culture microorganisms prior to analysis. Here, we aimed to reflect on the challenges of analyzing metagenomic data in the three main approaches for studying antimicrobial resistance: (i) analysis of microbial diversity, (ii) functional gene analysis, and (iii) searching the most complete and pertinent resistome databases.

Identification of a Novel Plasmid-Borne Gentamicin Resistance Gene in Nontyphoidal Salmonella Isolated from Retail Turkey

The spread of antibiotic-resistant bacteria presents a global health challenge. Efficient surveillance of bacteria harboring antibiotic resistance genes (ARGs) is a critical aspect to controlling the spread. Increased access to microbial genomic data from many diverse populations informs this surveillance but only when functional ARGs are identifiable within the data set. Current, homology-based approaches are effective at identifying the majority of ARGs within given clinical and nonclinical data sets for several pathogens, yet there are still some whose identities remain elusive. By coupling phenotypic profiling with genotypic data, these unknown ARGs can be identified to strengthen homology-based searches. To prove the efficacy and feasibility of this approach, a published data set from the U.S. National Antimicrobial Resistance Monitoring System (NARMS), for which the phenotypic and genotypic data of 640 Salmonella isolates are available, was subjected to this analysis. Six isolates recovered from the NARMS retail meat program between 2011 and 2013 were identified previously as phenotypically resistant to gentamicin but contained no known gentamicin resistance gene. Using the phenotypic and genotypic data, a comparative genomics approach was employed to identify the gene responsible for the observed resistance in all six of the isolates. This gene, grdA, is harbored on a 9,016-bp plasmid that is transferrable to Escherichia coli, confers gentamicin resistance to E. coli, and has never before been reported to confer gentamicin resistance. Bioinformatic analysis of the encoded protein suggests an ATP binding motif. This work demonstrates the advantages associated with coupling genomics technologies with phenotypic data for novel ARG identification.

The Association between Insertion Sequences and Antibiotic Resistance Genes

The emergence and spread of antibiotic resistance genes (ARGs) among pathogens threaten the prevention and treatment of bacterial infections as well as our food production chains. Early knowledge about mobile ARGs that are present in pathogens or that have the potential to become clinically relevant could help mitigate potential negative consequences. Recently, exploring integron gene cassettes was shown to be successful for identifying novel mobilized ARGs, some of which were already circulating in pathogens. Still, only a subset of ARGs is mobilized by integrons, and the contexts of other mobile genetic elements associated with ARGs remain unexplored. This includes insertion sequences (ISs) responsible for the mobilization of many ARGs. Our analyses identified ISs, species, and environments where ARG-IS relationships are particularly strong. This could be a first step to guide the discovery of novel ARGs, while also providing insights into mechanisms involved in the mobilization and transfer of ARGs.

Insertion sequences (ISs) are abundant mobile genetic elements on bacterial genomes, responsible for mobilization of many genes, including antibiotic resistance genes (ARGs). As ARGs often occur in similar genetic contexts, understanding which ISs tend to be associated with known ARGs could be a first step toward discovering novel ARGs through predictive or experimental strategies. This could be valuable, as early identification of ARGs in pathogens could facilitate surveillance, confinement actions, molecular diagnostics, and drug development. Here, we present a comprehensive analysis of the association of specific ISs with known ARGs. A large collection of bacterial genomes was used to characterize the immediate context of 2,437 known ARGs and 3,768 ISs. While many ARGs were consistently found close to specific ISs, the contexts around all ISs were more variable. Nevertheless, a subset of individual ISs, as well as tentative composite transposons, showed significant associations with ARGs. These included, e.g., insertion sequences classified as IS6, Tn3, IS4, and IS1 that were not only strongly associated with diverse ARGs but also highly abundant in pathogens. Therefore, we conclude that the context of this subset of ISs and tentative composite transposons would be particularly valuable to discover novel ARGs through modeling or empirical approaches. A set of 1,891 metagenomes were analyzed to identify environments where those ISs commonly associated with ARGs were particularly abundant. The associations found in metagenomes were similar to those found in genomes.

IMPORTANCE The emergence and spread of antibiotic resistance genes (ARGs) among pathogens threaten the prevention and treatment of bacterial infections as well as our food production chains. Early knowledge about mobile ARGs that are present in pathogens or that have the potential to become clinically relevant could help mitigate potential negative consequences. Recently, exploring integron gene cassettes was shown to be successful for identifying novel mobilized ARGs, some of which were already circulating in pathogens. Still, only a subset of ARGs is mobilized by integrons, and the contexts of other mobile genetic elements associated with ARGs remain unexplored. This includes insertion sequences (ISs) responsible for the mobilization of many ARGs. Our analyses identified ISs, species, and environments where ARG-IS relationships are particularly strong. This could be a first step to guide the discovery of novel ARGs, while also providing insights into mechanisms involved in the mobilization and transfer of ARGs.

ARGDIT: a validation and integration toolkit for Antimicrobial Resistance Gene Databases

MOTIVATION

Antimicrobial resistance is currently one of the main challenges in public health due to the excessive use of antimicrobials in medical treatments and agriculture. The advancements in high-throughput next-generation sequencing and development of bioinformatics tools allow simultaneous detection and identification of antimicrobial resistance genes (ARGs) from clinical, food and environment samples, to monitor the prevalence and track the dissemination of these ARGs. Such analyses are however reliant on a comprehensive database of ARGs with accurate sequence content and annotation. Most of the current ARG databases are therefore manually curated, but this is a time-consuming process and the resulting curation errors could be hard to detect. Several secondary ARG databases consolidate contents from different source ARG databases, and hence modifications in the primary databases might not be propagated and updated promptly in the secondary ARG databases.

RESULTS

To address these problems, a validation and integration toolkit called ARGDIT was developed to validate ARG database fidelity, and merge multiple primary ARG databases into a single consolidated secondary ARG database with optional automated sequence re-annotation. Experimental results demonstrated the effectiveness of this toolkit in identifying errors such as sequence annotation typos in current ARG databases and generating an integrated non-redundant ARG database with structured annotation. A toolkit-oriented workflow is also proposed to minimize the efforts in validating, curating and merging multiple ARG protein or coding sequence databases. Database developers therefore benefit from faster update cycles and lower costs for database maintenance, while ARG pipeline users can easily evaluate the reference ARG database quality.

AVAILABILITY AND IMPLEMENTATION

ARGDIT is available at https://github.com/phglab/ARGDIT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A clinically important, plasmid-borne antibiotic resistance gene (β-lactamase TEM-116) present in desert soils

The exhaustive use of antibiotics in humans, animal farming and other agricultural practices has resulted in the frequent appearance of antibiotic resistant bacteria in human-impacted habitats. However, antibiotic resistance in natural (less-impacted) habitats is less understood. Using shotgun metagenomics we analysed soils from relatively low anthropogenic impact sites across the Namib Desert. We report the presence of a clinically significant extended spectrum β-lactamase (TEM-116), on a ColE1-like plasmid also carrying a metal resistance gene (arsC). The co-occurrence of resistance to antimicrobial drugs and metals encoded on a single mobile genetic element increases the probability of dissemination of these resistance determinants and the potential selection of multiple resistance mechanisms. In addition, the presence of a P7 entero-bacteriophage on the same plasmid, may represent a new vehicle for the propagation of TEM-116 in these soil communities. These findings highlight the role of the environment in the One Health initiative.

Platforms for elucidating antibiotic resistance in single genomes and complex metagenomes

Antibiotic or antimicrobial resistance (AR) facilitated by the vertical and/or horizontal transfer of antibiotic resistance genes (ARGs), is a serious global health challenge. While traditionally associated with pathogens in clinical environments, it is becoming increasingly clear that non-clinical environments may also be reservoirs of ARGs. The recent improvements in rapid and affordable next generation sequencing technologies along with sophisticated bioinformatics platforms has the potential to revolutionize diagnostic microbiology and microbial surveillance. Through the study and characterization of ARGs in bacterial genomes and complex metagenomes, we are now able to reveal the genetic scope of AR in single bacteria and complex communities, and obtain important insights into AR dynamics at species, population and community levels, providing novel epidemiological and ecological perspectives. A suite of bioinformatics pipelines and ARG databases are currently available for genomic and metagenomic data analyses. However, different platforms may significantly vary and therefore, it is crucial to choose the tools that are most suitable for the specific analysis being conducted. This review provides a detailed account of available bioinformatics platforms for identification and characterization of ARGs and associated genetic elements within single bacterial isolates and complex environmental samples. It focuses primarily on currently available ARG databases, employing a comprehensive benchmarking pipeline to identify ARGs in four bacterial genomes (Aeromonas salmonicida, Bacillus cereus, Burkholderia sp. and Escherichia coli) and three shotgun metagenomes (human gut, poultry litter and soil) providing insight into which databases should be used for different analytical scenarios.

Abundance and diversity of resistomes differ between healthy human oral cavities and gut

The global threat of antimicrobial resistance has driven the use of high-throughput sequencing techniques to monitor the profile of resistance genes, known as the resistome, in microbial populations. The human oral cavity contains a poorly explored reservoir of these genes. Here we analyse and compare the resistome profiles of 788 oral cavities worldwide with paired stool metagenomes. We find country and body site-specific differences in the prevalence of antimicrobial resistance genes, classes and mechanisms in oral and stool samples. Within individuals, the highest abundances of antimicrobial resistance genes are found in the oral cavity, but the oral cavity contains a lower diversity of resistance genes compared to the gut. Additionally, co-occurrence analysis shows contrasting ARG-species associations between saliva and stool samples. Maintenance and persistence of antimicrobial resistance is likely to vary across different body sites. Thus, we highlight the importance of characterising the resistome across body sites to uncover the antimicrobial resistance potential in the human body.

Mobilizable antibiotic resistance genes are present in dust microbial communities

The decades-long global trend of urbanization has led to a population that spends increasing amounts of time indoors. Exposure to microbes in buildings, and specifically in dust, is thus also increasing, and has been linked to various health outcomes and to antibiotic resistance genes (ARGs). These are most efficiently screened using DNA sequencing, but this method does not determine which microbes are viable, nor does it reveal whether their ARGs can actually disseminate to other microbes. We have thus performed the first study to: 1) examine the potential for ARG dissemination in indoor dust microbial communities, and 2) validate the presence of detected mobile ARGs in viable dust bacteria. Specifically, we integrated 166 dust metagenomes from 43 different buildings. Sequences were assembled, annotated, and screened for potential integrons, transposons, plasmids, and associated ARGs. The same dust samples were further investigated using cultivation and isolate genome and plasmid sequencing. Potential ARGs were detected in dust isolate genomes, and we confirmed their placement on mobile genetic elements using long-read sequencing. We found 183 ARGs, of which 52 were potentially mobile (associated with a putative plasmid, transposon or integron). One dust isolate related to Staphylococcus equorum proved to contain a plasmid carrying an ARG that was detected metagenomically and confirmed through whole genome and plasmid sequencing. This study thus highlights the power of combining cultivation with metagenomics to assess the risk of potentially mobile ARGs for public health.

Introduction of NGS in Environmental Surveillance for Healthcare-Associated Infection Control

The hospital environment significantly contributes to the onset of healthcare associated infections (HAIs), representing the most frequent and severe complications related to health care. The monitoring of hospital surfaces is generally addressed by microbial cultural isolation, with some performance limitations. Hence there is need to implement environmental surveillance systems using more effective methods. This study aimed to evaluate next-generation sequencing (NGS) technologies for hospital environment microbiome characterization, in comparison with conventional and molecular methods, in an Italian pediatric hospital. Environmental samples included critical surfaces of randomized rooms, surgical rooms, intensive care units and delivery rooms. The resistome of the contaminating population was also evaluated. NGS, compared to other methods, detected with higher sensitivity the environmental bacteria, and was the only method able to detect even unsearched bacteria. By contrast, however, it did not detect mycetes, nor it could distinguish viable from dead bacteria. Microbiological and PCR methods could identify and quantify mycetes, in addition to bacteria, and PCR could define the population resistome. These data suggest that NGS could be an effective method for hospital environment monitoring, especially if flanked by PCR for species identification and resistome characterization, providing a potential tool for the control of HAI transmission.

Source identification of antibiotic resistance genes in a peri-urban river using novel crAssphage marker genes and metagenomic signatures

Antimicrobial resistance is a growing public health concern, and environment is regarded as an important reservoir and dissemination route for antibiotic resistance genes (ARGs). To prevent and control ARG pollution, it is essential to correctly disentangle source-sink relationship of ARGs in the environment. However, accurately apportioning sources of ARGs is still a big challenge due to the complex interaction of multiple sources and contaminants in the environment with changing dynamics. In this study, we addressed this problem and focused on identifying the potential sources of ARGs in a peri-urban river by jointly utilizing two novel microbial source tracking methods. To attain the objective, sediment/water samples were collected from the peri-urban river and four ARG-associated ecotypes including effluents of sewage treatment plants (STPs), STP influent, chicken manures and pig manures. The high-throughput profilings of ARGs and microbial taxa in the river sediments and the four ecotypes were comprehensively characterized in combination of shotgun sequencing and metagenomic assembly analysis. CrAssphage, a recently-discovered DNA bacteriophage, was employed to track the impact of human fecal pollution on ARGs in the river sediments. Further, SourceTracker, a machine-learning classification tool, was used for quantifying the contributions of potential sources to ARGs in the river sediments based on the metagenomic signatures of ARGs and microbial taxa. In total, 888 ARG subtypes belonging to 29 ARG types were detected across all samples, including mcr-1 and a range of carbapenemases types. Statistical analyses suggested different ecotypes generally had distinct profiles of both ARGs and microbial taxa, while the ARG compositions were significantly correlated with the microbial community. Source tracking with crAssphage showed the presence of ARGs in the river sediments might be largely impacted by the extent of human fecal pollution, which was also confirmed by the analyses of SourceTracker that the discharge from STPs was the largest contributor of ARGs (81.6-92.1%) and microbes (49.3-68.1%) in the river sediments. Results of the study can help us to better understand the characterization of ARGs in the peri-urban ecosystem and to design effective prevention and control strategies for reducing ARG dissemination.

Resistome analyses of sputum from COPD and healthy subjects reveals bacterial load-related prevalence of target genes

BACKGROUND

Antibiotic resistance is a major global threat. We hypothesised that the chronic obstructive pulmonary disease (COPD) airway is a reservoir of antimicrobial resistance genes (ARGs) that associate with microbiome-specific COPD subgroups.

OBJECTIVE

To determine the resistance gene profiles in respiratory samples from COPD patients and healthy volunteers.

METHODS

Quantitative PCR targeting 279 specific ARGs was used to profile the resistomes in sputum from subjects with COPD at stable, exacerbation and recovery visits (n=55; COPD-BEAT study), healthy controls with (n=7) or without (n=22) exposure to antibiotics in the preceding 12 months (EXCEED study) and in bronchial brush samples from COPD (n=8) and healthy controls (n=7) (EvA study).

RESULTS

ARG mean (SEM) prevalence was greater in stable COPD samples (35.2 (1.6)) than in healthy controls (27.6 (1.7); p=0.004) and correlated with total bacterial abundance (r²=0.23; p<0.001). Prevalence of ARG positive signals in individuals was not related to COPD symptoms, lung function or their changes at exacerbation. In the COPD subgroups designated High γProteobacteria and High Firmicutes, ARG prevalence was not different at stable state but significantly declined from stable through exacerbation to recovery in the former (p=0.011) without changes in total bacterial abundance. The ARG patterns were similar in COPD versus health, COPD microbiome-subgroups and between sputum and bronchoscopic samples independent of antibiotic exposure in the last 12 months.

CONCLUSIONS

ARGs are highly prevalent in sputum, broadly in proportion to bacterial abundance in both healthy and COPD subjects. Thus, COPD appears to be an ARG reservoir due to high levels of bacterial colonisation.

The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples

BACKGROUND

Shotgun metagenomics is increasingly used to characterise microbial communities, particularly for the investigation of antimicrobial resistance (AMR) in different animal and environmental contexts. There are many different approaches for inferring the taxonomic composition and AMR gene content of complex community samples from shotgun metagenomic data, but there has been little work establishing the optimum sequencing depth, data processing and analysis methods for these samples. In this study we used shotgun metagenomics and sequencing of cultured isolates from the same samples to address these issues. We sampled three potential environmental AMR gene reservoirs (pig caeca, river sediment, effluent) and sequenced samples with shotgun metagenomics at high depth (~ 200 million reads per sample). Alongside this, we cultured single-colony isolates of Enterobacteriaceae from the same samples and used hybrid sequencing (short- and long-reads) to create high-quality assemblies for comparison to the metagenomic data. To automate data processing, we developed an open-source software pipeline, 'ResPipe'.

RESULTS

Taxonomic profiling was much more stable to sequencing depth than AMR gene content. 1 million reads per sample was sufficient to achieve < 1% dissimilarity to the full taxonomic composition. However, at least 80 million reads per sample were required to recover the full richness of different AMR gene families present in the sample, and additional allelic diversity of AMR genes was still being discovered in effluent at 200 million reads per sample. Normalising the number of reads mapping to AMR genes using gene length and an exogenous spike of Thermus thermophilus DNA substantially changed the estimated gene abundance distributions. While the majority of genomic content from cultured isolates from effluent was recoverable using shotgun metagenomics, this was not the case for pig caeca or river sediment.

CONCLUSIONS

Sequencing depth and profiling method can critically affect the profiling of polymicrobial animal and environmental samples with shotgun metagenomics. Both sequencing of cultured isolates and shotgun metagenomics can recover substantial diversity that is not identified using the other methods. Particular consideration is required when inferring AMR gene content or presence by mapping metagenomic reads to a database. ResPipe, the open-source software pipeline we have developed, is freely available ( https://gitlab.com/hsgweon/ResPipe ).

Industrial wastewater treatment plant enriches antibiotic resistance genes and alters the structure of microbial communities

Antibiotic resistance is an emerging global health crisis, driven largely by overuse and misuse of antibiotics. However, there are examples in which the production of these antimicrobial agents has polluted the environment with active antibiotic residues, selecting for antibiotic resistant bacteria and the genes they carry. In this work, we have used shotgun metagenomics to investigate the taxonomic structure and resistance gene composition of sludge communities in a treatment plant in Croatia receiving wastewater from production of the macrolide antibiotic azithromycin. We found that the total abundance of antibiotic resistance genes was three times higher in sludge from the treatment plant receiving wastewater from pharmaceutical production than in municipal sludge from a sewage treatment plant in Zagreb. Surprisingly, macrolide resistance genes did not have higher abundances in the industrial sludge, but genes associated with mobile genetic elements such as integrons had. We conclude that at high concentrations of antibiotics, selection may favor taxonomic shifts towards intrinsically resistant species or strains harboring chromosomal resistance mutations rather than acquisition of mobile resistance determinants. Our results underscore the need for regulatory action also within Europe to avoid release of antibiotics into the environment.

Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available online (http://microbiology.se/software/mumame).

Associations between antimicrobial use and the faecal resistome on broiler farms from nine European countries

OBJECTIVES

To determine associations between farm- and flock-level antimicrobial usage (AMU), farm biosecurity status and the abundance of faecal antimicrobial resistance genes (ARGs) on broiler farms.

METHODS

In the cross-sectional pan-European EFFORT study, conventional broiler farms were visited and faeces, AMU information and biosecurity records were collected. The resistomes of pooled faecal samples were determined by metagenomic analysis for 176 farms. A meta-analysis approach was used to relate total and class-specific ARGs (expressed as fragments per kb reference per million bacterial fragments, FPKM) to AMU (treatment incidence per DDD, TIDDDvet) per country and subsequently across all countries. In a similar way, the association between biosecurity status (Biocheck.UGent) and the resistome was explored.

RESULTS

Sixty-six (38%) flocks did not report group treatments but showed a similar resistome composition and roughly similar ARG levels to antimicrobial-treated flocks. Nevertheless, we found significant positive associations between β-lactam, tetracycline, macrolide and lincosamide, trimethoprim and aminoglycoside antimicrobial flock treatments and ARG clusters conferring resistance to the same class. Similar associations were found with purchased products. In gene-level analysis for β-lactams and macrolides, lincosamides and streptogramins, a significant positive association was found with the most abundant gene clusters blaTEM and erm(B). Little evidence was found for associations with biosecurity.

CONCLUSIONS

The faecal microbiome in European broilers contains a high diversity of ARGs, even in the absence of current antimicrobial selection pressure. Despite this, the relative abundance of genes and the composition of the resistome is positively related to AMU in European broiler farms for several antimicrobial classes.

A conceptual framework for the environmental surveillance of antibiotics and antibiotic resistance

Environmental surveillance of antibiotics and antibiotic resistance could contribute toward the protection of human, animal and ecosystem health. However, justification for the choice of markers and sampling sites that informs about different risk scenarios is often lacking. Here, we define five fundamentally different objectives for surveillance of antibiotics and antibiotic resistance in the environment. The first objective is (1) to address the risk of transmission of already antibiotic-resistant bacteria to humans via environmental routes. The second is (2) to address the risk for accelerating the evolution of antibiotic resistance in pathogens through pollution with selective agents and bacteria of human or animal origin. The third objective is (3) to address the risks antibiotics pose for aquatic and terrestrial ecosystem health, including the effects on ecosystem functions and services. The two final objectives overlap with those of traditional clinical surveillance, namely, to identify (4) the population-level resistance prevalence and (5) population-level antibiotic use. The latter two environmental surveillance objectives have particular potential in countries where traditional clinical surveillance data and antibiotic consumption data are scarce or absent. For each objective, the levels of evidence provided by different phenotypic and genotypic microbial surveillance markers, as well as antibiotic residues, are discussed and evaluated on a conceptual level. Furthermore, sites where monitoring would be particularly informative are identified. The proposed framework could be one of the starting points for guiding environmental monitoring and surveillance of antibiotics and antibiotic resistance on various spatiotemporal scales, as well as for harmonizing such activities with existing human and animal surveillance systems.

Environmental dimensions of antibiotic resistance: assessment of basic science gaps

Antibiotic resistance is one of the major problems facing medical practice in the 21st century. Historical approaches to managing antibiotic resistance have often focused on individual patients, specific pathogens and particular resistance phenotypes. However, it is increasingly recognized that antibiotic resistance is a complex ecological and evolutionary problem. As such, understanding the dynamics of antibiotic resistance requires integration of data on the diverse mobile genetic elements often associated with antibiotic resistance genes, and their dissemination by various mechanisms of horizontal gene transfer between bacterial cells and environments. Most important is understanding the fate and effects of antibiotics at sub-inhibitory concentrations, and co-selection. This opinion paper identifies key knowledge gaps in our understanding of resistance phenomena, and outlines research needs that should be addressed to help us manage resistance into the future.

Viruses as key reservoirs of antibiotic resistance genes in the environment

Antibiotic resistance is a rapidly growing health care problem globally and causes many illnesses and deaths. Bacteria can acquire antibiotic resistance genes (ARGs) by horizontal transfer mediated by mobile genetic elements, where the role of phages in their dissemination in natural environments has not yet been clearly resolved. From metagenomic studies, we showed that the mean proportion of predicted ARGs found in prophages (0-0.0028%) was lower than those present in the free viruses (0.001-0.1%). Beta-lactamase, from viruses in the swine gut, represented 0.10 % of the predicted genes. Overall, in the environment, the ARG distribution associated with viruses was strongly linked to human activity, and the low dN/dS ratio observed advocated for a negative selection of the ARGs harbored by the viruses. Our network approach showed that viruses were linked to putative pathogens (Enterobacterales and vibrionaceae) and were considered key vehicles in ARG transfer, similar to plasmids. Therefore, these ARGs could then be disseminated at larger temporal and spatial scales than those included in the bacterial genomes, allowing for time-delayed genetic exchanges.