ARGminer: a web platform for the crowdsourcing-based curation of antibiotic resistance genes

The diversity of the antimicrobial resistome of lake Tanganyika increases with the water depth

The presence of antimicrobial resistance genes (ARGs) in the microbiome of freshwater communities is a consequence of thousands of years of evolution but also of the pressure exerted by anthropogenic activities, with potential negative impact on environmental and human health. In this study, we investigated the distribution of ARGs in Lake Tanganyika (LT)'s water column to define the resistome of this ancient lake. Additionally, we compared the resistome of LT with that of Lake Baikal (LB), the oldest known lake with different environmental characteristics and a lower anthropogenic pollution than LT. We found that richness and abundance of several antimicrobial resistance classes were higher in the deep water layers in both lakes. LT Kigoma region, known for its higher anthropogenic pollution, showed a greater richness and number of ARG positive MAGs compared to Mahale. Our results provide a comprehensive understanding of the antimicrobial resistome of LT and underscore its importance as reservoir of antimicrobial resistance. In particular, the deepest water layers of LT are the main repository of diverse ARGs, mirroring what was observed in LB and in other aquatic ecosystems. These findings suggest that the deep waters might play a crucial role in the preservation of ARGs in aquatic ecosystems.

Genomic surveillance for antimicrobial resistance - a One Health perspective

Antimicrobial resistance (AMR) - the ability of microorganisms to adapt and survive under diverse chemical selection pressures - is influenced by complex interactions between humans, companion and food-producing animals, wildlife, insects and the environment. To understand and manage the threat posed to health (human, animal, plant and environmental) and security (food and water security and biosecurity), a multifaceted 'One Health' approach to AMR surveillance is required. Genomic technologies have enabled monitoring of the mobilization, persistence and abundance of AMR genes and mutations within and between microbial populations. Their adoption has also allowed source-tracing of AMR pathogens and modelling of AMR evolution and transmission. Here, we highlight recent advances in genomic AMR surveillance and the relative strengths of different technologies for AMR surveillance and research. We showcase recent insights derived from One Health genomic surveillance and consider the challenges to broader adoption both in developed and in lower- and middle-income countries.

Metagenomic and Machine Learning Meta-Analyses Characterize Airborne Resistome Features and Their Hosts in China Megacities

Urban ambient air contains a cocktail of antibiotic resistance genes (ARGs) emitted from various anthropogenic sites. However, what is largely unknown is whether the airborne ARGs exhibit site-specificity or their pathogenic hosts persistently exist in the air. Here, by retrieving 1.2 Tb metagenomic sequences (n = 136), we examined the airborne ARGs from hospitals, municipal wastewater treatment plants (WWTPs) and landfills, public transit centers, and urban sites located in seven of China's megacities. As validated by the multiple machine learning-based classification and optimization, ARGs' site-specificity was found to be the most apparent in hospital air, with featured resistances to clinical-used rifamycin and (glyco)peptides, whereas the more environmentally prevalent ARGs (e.g., resistance to sulfonamide and tetracycline) were identified being more specific to the nonclinical ambient air settings. Nearly all metagenome-assembled genomes (MAGs) that possessed the site-featured resistances were identified as pathogenic taxa, which occupied the upper-representative niches in all the neutrally distributed airborne microbial community (P < 0.01, m = 0.22-0.50, R2 = 0.41-0.86). These niche-favored putative resistant pathogens highlighted the enduring antibiotic resistance hazards in the studied urban air. These findings are critical, albeit the least appreciated until our study, to gauge the airborne dimension of resistomes' features and fates in urban atmospheric environments.

Scalable and versatile container-based pipelines for de novo genome assembly and bacterial annotation

Background: Advancements in DNA sequencing technology have transformed the field of bacterial genomics, allowing for faster and more cost effective chromosome level assemblies compared to a decade ago. However, transforming raw reads into a complete genome model is a significant computational challenge due to the varying quality and quantity of data obtained from different sequencing instruments, as well as intrinsic characteristics of the genome and desired analyses. To address this issue, we have developed a set of container-based pipelines using Nextflow, offering both common workflows for inexperienced users and high levels of customization for experienced ones. Their processing strategies are adaptable based on the sequencing data type, and their modularity enables the incorporation of new components to address the community's evolving needs. Methods: These pipelines consist of three parts: quality control, de novo genome assembly, and bacterial genome annotation. In particular, the genome annotation pipeline provides a comprehensive overview of the genome, including standard gene prediction and functional inference, as well as predictions relevant to clinical applications such as virulence and resistance gene annotation, secondary metabolite detection, prophage and plasmid prediction, and more. Results: The annotation results are presented in reports, genome browsers, and a web-based application that enables users to explore and interact with the genome annotation results. Conclusions: Overall, our user-friendly pipelines offer a seamless integration of computational tools to facilitate routine bacterial genomics research. The effectiveness of these is illustrated by examining the sequencing data of a clinical sample of Klebsiella pneumoniae.

Explaining the resistomes in a megacity's water supply catchment: Roles of microbial assembly-dominant taxa, niched environments and pathogenic bacteria

Antibiotic resistance genes (ARGs) in drinking water sources suggest the possible presence of resistant microorganisms that jeopardize human health. However, explanations for the presence of specific ARGs in situ are largely unknown, especially how their prevalence is affected by local microbial ecology, taxa assembly and community-wide gene transfer. Here, we characterized resistomes and bacterial communities in the Taipu River catchment, which feeds a key drinking water reservoir to a global megacity, Shanghai. Overall, ARG abundances decreased significantly as the river flowed downstream towards the reservoir (P < 0.01), whereas the waterborne bacteria assembled deterministically (|βNRI| > 2.0) as a function of temperature and dissolved oxygen conditions with the assembly-dominant taxa (e.g. Ilumatobacteraceae and Cyanobiaceae) defining local resistomes (P < 0.01, Cohen's D = 4.22). Bacterial hosts of intragenomic ARGs stayed at the same level across the catchment (60 ∼ 70 genome copies per million reads). Among them, the putative resistant pathogens (e.g. Burkholderiaceae) carried mixtures of ARGs that exhibited high transmission probability (transfer counts = 126, P < 0.001), especially with the microbial assembly-dominant taxa. These putative resistant pathogens had densities ranging form 3.0 to 4.0 × 10⁶ cell/L, which was more pronouncedly affected by resistome and microbial assembly structures than environmental factors (SEM, std-coeff β = 0.62 vs. 0.12). This work shows that microbial assembly and resistant pathogens play predominant roles in prevelance and dissemination of resistomes in receiving water, which deserves greater attention in devisng control strategies for reducing in-situ ARGs and resistant strains in a catchment.

Tracking Antibiotic Resistance from the Environment to Human Health

Antimicrobial resistance (AMR) is one of the threats to our world according to the World Health Organization (WHO). Resistance is an evolutionary dynamic process where host-associated microbes have to adapt to their stressful environments. AMR could be classified according to the mechanism of resistance or the biome where resistance takes place. Antibiotics are one of the stresses that lead to resistance through antibiotic resistance genes (ARGs). The resistome could be defined as the collection of all ARGs in an organism's genome or metagenome. Currently, there is a growing body of evidence supporting that the environment is the largest source of ARGs, but to what extent the environment does contribute to the antimicrobial resistance evolution is a matter of investigation. Monitoring the ARGs transfer route from the environment to humans and vice versa is a nature-to-nature feedback loop where you cannot set an accurate starting point of the evolutionary event. Thus, tracking resistome evolution and transfer to and from different biomes is crucial for the surveillance and prediction of the next resistance outbreak.Herein, we review the overlap between clinical and environmental resistomes and the available databases and computational analysis tools for resistome analysis through ARGs detection and characterization in bacterial genomes and metagenomes. Till this moment, there is no tool that can predict the resistance evolution and dynamics in a distinct biome. But, hopefully, by understanding the complicated relationship between the environmental and clinical resistome, we could develop tools that track the feedback loop from nature to nature in terms of evolution, mobilization, and transfer of ARGs.

Genome-resolved insight into the reservoir of antibiotic resistance genes in aquatic microbial community

Aquatic microbial communities are an important reservoir of antibiotic resistance genes (ARGs). However, distribution and diversity of different ARG categories in environmental microbes with different ecological strategies is not yet well studied. Despite the potential exposure of the southern part of the Caspian Sea to the release of antibiotics, little is known about its natural resistome profile. We used a combination of Hidden Markov model (HMM), homology alignment and a deep learning approach for comprehensive screening of the diversity and distribution of ARGs in the Caspian Sea metagenomes at genome resolution. Detected ARGs were classified into five antibiotic resistance categories including prevention of access to target (44%), modification/protection of targets (30%), direct modification of antibiotics (22%), stress resistance (3%), and metal resistance (1%). The 102 detected ARG containing metagenome-assembled genomes of the Caspian Sea were dominated by representatives of Acidimicrobiia, Gammaproteobacteria, and Actinobacteria classes. Comparative analysis revealed that the highly abundant, oligotrophic, and genome streamlined representatives of taxa Acidimicrobiia and Actinobacteria modify the antibiotic target via mutation to develop antibiotic resistance rather than carrying extra resistance genes. Our results help with understanding how the encoded resistance categories of each genome are aligned with its ecological strategies.

Metagenomic-based surveillance systems for antibiotic resistance in non-clinical settings

The success of antibiotics as a therapeutic agent has led to their ineffectiveness. The continuous use and misuse in clinical and non-clinical areas have led to the emergence and spread of antibiotic-resistant bacteria and its genetic determinants. This is a multi-dimensional problem that has now become a global health crisis. Antibiotic resistance research has primarily focused on the clinical healthcare sectors while overlooking the non-clinical sectors. The increasing antibiotic usage in the environment - including animals, plants, soil, and water - are drivers of antibiotic resistance and function as a transmission route for antibiotic resistant pathogens and is a source for resistance genes. These natural compartments are interconnected with each other and humans, allowing the spread of antibiotic resistance via horizontal gene transfer between commensal and pathogenic bacteria. Identifying and understanding genetic exchange within and between natural compartments can provide insight into the transmission, dissemination, and emergence mechanisms. The development of high-throughput DNA sequencing technologies has made antibiotic resistance research more accessible and feasible. In particular, the combination of metagenomics and powerful bioinformatic tools and platforms have facilitated the identification of microbial communities and has allowed access to genomic data by bypassing the need for isolating and culturing microorganisms. This review aimed to reflect on the different sequencing techniques, metagenomic approaches, and bioinformatics tools and pipelines with their respective advantages and limitations for antibiotic resistance research. These approaches can provide insight into resistance mechanisms, the microbial population, emerging pathogens, resistance genes, and their dissemination. This information can influence policies, develop preventative measures and alleviate the burden caused by antibiotic resistance.

Plasmid Genomes Reveal the Distribution, Abundance, and Organization of Mercury-Related Genes and Their Co-Distribution with Antibiotic Resistant Genes in Gammaproteobacteria

Mercury (Hg) pollution poses human health and environmental risks worldwide, as it can have toxic effects and causes selective pressure that facilitates the spread of antibiotic resistant genes (ARGs) among microbes. More and more studies have revealed that numerous Hg-related genes (HRGs) can help to resist and transform Hg. In the present study, we systematically analyzed the HRG distribution, abundance, organization, and their co-distribution with ARGs, using 18,731 publicly available plasmid genomes isolated from a Gammaproteobacteria host. Our results revealed that there were many Hg-resistant (mer) operon genes but they were not extensively distributed across plasmids, with only 9.20% of plasmids harboring HRGs. Additionally, no hgcAB genes (which methylate Hg to create methylmercury) were identified in any of the analyzed plasmids. The host source significantly influenced the number of HRGs harbored by plasmids; plasmids isolated from humans and animals harbored a significantly smaller number of HRGs than plasmids isolated from the wastewater and sludge. HRG clusters displayed an extremely high organizational diversity (88 HRG cluster types), though incidences of more than half of the HRG cluster types was <5. This indicates the frequent rearrangement among HRGs in plasmids. The 1368 plasmids harboring both HRGs and ARGs, were dominated by Klebsiella, followed by Escherichia, Salmonella, and Enterobacter. The tightness of the HRG and ARG co-distribution in plasmids was affected by the host sources but not by pathogenicity. HRGs were more likely to co-occur with specific ARG classes (sulfonamide, macrolide-lincosamide-streptogramin, and aminoglycoside resistance genes). Collectively, our results reveal the distribution characteristics of HRGs in plasmids, and they have important implications for further understanding the environmental risks caused by the spread of ARGs through the plasmid-mediated co-transfer of ARGs and HRGs.

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.

Comparison of functional classification systems

In microbiome analysis, functional profiling is based on assigning reads or contigs to terms or nodes in a functional classification system. There are a number of large, general-purpose functional classifications that are in use, such as eggNOG, KEGG, InterPro and SEED. Smaller, special-purpose classifications include CARD, EC, MetaCyc and VFDB. Here, we compare the different classifications in terms of their overlap, redundancy, structure and assignment rates. We also provide mappings between main concepts in different classifications. For the large classifications, we find that eggNOG performs the best with respect to sequence redundancy and structure, SEED has the cleanest hierarchy, whereas KEGG and InterPro:BP might be more informative for medical applications. We illustrate the practical assignment rates for different classifications using a number of metagenomic samples.

Microbial Dark Matter: from Discovery to Applications

With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment.

Antibiotic resistance genes are differentially mobilized according to resistance mechanism

BACKGROUND

Screening for antibiotic resistance genes (ARGs) in especially environmental samples with (meta)genomic sequencing is associated with false-positive predictions of phenotypic resistance. This stems from the fact that most acquired ARGs require being overexpressed before conferring resistance, which is often caused by decontextualization of putative ARGs by mobile genetic elements (MGEs). Consequent overexpression of ARGs can be caused by strong promoters often present in insertion sequence (IS) elements and integrons and the copy number effect of plasmids, which may contribute to high expression of accessory genes.

RESULTS

Here, we screen all complete bacterial RefSeq genomes for ARGs. The genetic contexts of detected ARGs are investigated for IS elements, integrons, plasmids, and phylogenetic dispersion. The ARG-MOB scale is proposed, which indicates how mobilized detected ARGs are in bacterial genomes. It is concluded that antibiotic efflux genes are rarely mobilized and even 80% of β-lactamases have never, or very rarely, been mobilized in the 15,790 studied genomes. However, some ARGs are indeed mobilized and co-occur with IS elements, plasmids, and integrons.

CONCLUSIONS

In this study, ARGs in all complete bacterial genomes are classified by their association with MGEs, using the proposed ARG-MOB scale. These results have consequences for the design and interpretation of studies screening for resistance determinants, as mobilized ARGs pose a more concrete risk to human health. An interactive table of all results is provided for future studies targeting highly mobilized ARGs.

Whole genome sequencing for the risk assessment of probiotic lactic acid bacteria

Probiotic bacteria exhibit beneficial effects on human and/or animal health, and have been widely used in foods and fermented products for decades. Most probiotics consist of lactic acid bacteria (LAB), which are used in the production of various food products but have also been shown to have the ability to prevent certain diseases. With the expansion of applications for probiotic LAB, there is an increasing concern with regard to safety, as cases with adverse effects, i.e., severe infections, transfer of antimicrobial resistance genes, etc., can occur. Currently, in vitro assays remain the primary way to assess the properties of LAB. However, such methodologies are not meeting the needs of strain risk assessment on a high-throughput scale, in the context of the evolving concept of food safety. Analyzing the complete genetic information, including potential virulence genes and other determinants with a negative impact on health, allows for assessing the safe use of the product, for which whole-genome sequencing (WGS) of individual LAB strains can be employed. Genomic data can also be used to understand subtle differences in the strain level important for beneficial effects, or protect patents. Here, we propose that WGS-based bioinformatics analyses are an ideal and cost-effective approach for the initial in silico microbial risk evaluation, while the technique may also increase our understanding of LAB strains for food safety and probiotic property evaluation.

A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling

The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.

Review and Comparison of Antimicrobial Resistance Gene Databases

As the prevalence of antimicrobial resistance genes is increasing in microbes, we are facing the return of the pre-antibiotic era. Consecutively, the number of studies concerning antibiotic resistance and its spread in the environment is rapidly growing. Next generation sequencing technologies are widespread used in many areas of biological research and antibiotic resistance is no exception. For the rapid annotation of whole genome sequencing and metagenomic results considering antibiotic resistance, several tools and data resources were developed. These databases, however, can differ fundamentally in the number and type of genes and resistance determinants they comprise. Furthermore, the annotation structure and metadata stored in these resources can also contribute to their differences. Several previous reviews were published on the tools and databases of resistance gene annotation; however, to our knowledge, no previous review focused solely and in depth on the differences in the databases. In this review, we compare the most well-known and widely used antibiotic resistance gene databases based on their structure and content. We believe that this knowledge is fundamental for selecting the most appropriate database for a research question and for the development of new tools and resources of resistance gene annotation.

Antimicrobial Resistance in Rivers: A Review of the Genes Detected and New Challenges

River ecosystems are very important parts of the water cycle and an excellent habitat, food, and drinking water source for many organisms, including humans. Antibiotics are emerging contaminants which can enter rivers from various sources. Several antibiotics and their related antibiotic resistance genes (ARGs) have been detected in these ecosystems by various research programs and could constitute a substantial problem. The presence of antibiotics and other resistance cofactors can boost the development of ARGs in the chromosomes or mobile genetic elements of natural bacteria in rivers. The ARGs in environmental bacteria can also be transferred to clinically important pathogens. However, antibiotics and their resistance genes are both not currently monitored by national or international authorities responsible for controlling the quality of water bodies. For example, they are not included in the contaminant list in the European Water Framework Directive or in the US list of Water-Quality Benchmarks for Contaminants. Although ARGs are naturally present in the environment, very few studies have focused on non-impacted rivers to assess the background ARG levels in rivers, which could provide some useful indications for future environmental regulation and legislation. The present study reviews the antibiotics and associated ARGs most commonly measured and detected in rivers, including the primary analysis tools used for their assessment. In addition, other factors that could enhance antibiotic resistance, such as the effects of chemical mixtures, the effects of climate change, and the potential effects of the coronavirus disease 2019 pandemic, are discussed. Environ Toxicol Chem 2022;41:687-714. © 2022 SETAC.

Higher abundance of core antimicrobial resistant genes in effluent from wastewater treatment plants

Wastewater treatment plants (WWTPs) receive sewage water from a variety of sources, including livestock farms, hospitals, industries, and households, that contain antimicrobial resistant bacteria (ARB) and antimicrobial resistant genes (ARGs). Current treatment technologies are unable to completely remove ARB and ARGs, which are eventually released into the aquatic environment. This study focused on the core resistome of urban WWTPs that are persistent through wastewater treatment processes. We adopted the Hiseq-based metagenomic sequencing approach to identify the core resistome, their genetic context, and pathogenic potential of core ARGs in the influent (IN) and effluent (EF) samples of 12 urban WWTPs in South Korea. In this study, the abundance of ARGs ranged from 0.32 to 3.5 copies of ARGs per copy of the 16S rRNA gene, where the IN samples were relatively higher than the EF samples, especially for the macrolide-lincosamide-streptogramin (MLS)- and tetracycline- resistant genes. On the other hand, there were 43 core ARGs sharing up to 90% of the total, among which the relative abundance of sul1, APH(3'')-lb, and RbpA was higher in EF than in IN (p < 0.05). Moreover, tetracycline and sulfonamide-related core ARGs in both EF and IN were significantly more abundant on plasmids than on chromosomes (p < 0.05). We also found that the majority of core ARGs were carried by opportunistic pathogens such as Acinetobacter baumannii, Enterobacter cloacae, and Pseudomonas aeruginosa in both IN and EF. In addition, phages were the only mobile elements whose abundance correlated with that of core ARGs in EF, suggesting that transduction may play a major role in disseminating ARGs in the receiving water environment of the urban WWTP. The persistent release of core ARGs with pathogenic potential into environmental water is of immediate concern. The mobility of ARGs and ARBs in the environment is a major public health concern. These results should be taken into consideration when developing policy to mitigate environmental dissemination of ARG by WWTPs.

Functions predict horizontal gene transfer and the emergence of antibiotic resistance

Phylogenetic distance, shared ecology, and genomic constraints are often cited as key drivers governing horizontal gene transfer (HGT), although their relative contributions are unclear. Here, we apply machine learning algorithms to a curated set of diverse bacterial genomes to tease apart the importance of specific functional traits on recent HGT events. We find that functional content accurately predicts the HGT network [area under the receiver operating characteristic curve (AUROC) = 0.983], and performance improves further (AUROC = 0.990) for transfers involving antibiotic resistance genes (ARGs), highlighting the importance of HGT machinery, niche-specific, and metabolic functions. We find that high-probability not-yet detected ARG transfer events are almost exclusive to human-associated bacteria. Our approach is robust at predicting the HGT networks of pathogens, including Acinetobacter baumannii and Escherichia coli, as well as within localized environments, such as an individual’s gut microbiome.

Functional glyco-metagenomics elucidates the role of glycan-related genes in environments

BACKGROUND

Glycan-related genes play a fundamental role in various processes for energy acquisition and homeostasis maintenance while adapting to the environment in which the organism exists; however, their role in the microbiome in the environment is unclear.

METHODS

Sequence alignment was performed between known glycan-related genes and complete genomes of microorganisms, and optimal parameters for identifying glycan-related genes were determined based on the alignments. Using the constructed scheme (> 90% of identity and > 25 aa of alignment length), glycan-related genes in various environments were identified from 198 different metagenome data.

RESULTS

As a result, we identified 86.73 million glycan-related genes from the metagenome data. Among the 12 environments classified in this study, the percentage of glycan-related genes was high in the human-associated environment, suggesting that these environments utilize glycan metabolism better than other environments. On the other hand, the relative abundances of both glycoside hydrolases and glycosyltransferases surprisingly had a coverage of over 80% in all the environments. These glycoside hydrolases and glycosyltransferases were classified into two groups of (1) general enzyme families identified in various environments and (2) specific enzymes found only in certain environments. The general enzyme families were mostly from genes involved in monosaccharide metabolism, and most of the specific enzymes were polysaccharide degrading enzymes.

CONCLUSION

These findings suggest that environmental microorganisms could change the composition of their glycan-related genes to adapt the processes involved in acquiring energy from glycans in their environments. Our functional glyco-metagenomics approach has made it possible to clarify the relationship between the environment and genes from the perspective of carbohydrates, and the existence of glycan-related genes that exist specifically in the environment.

Probiotics impact the antibiotic resistance gene reservoir along the human GI tract in a person-specific and antibiotic-dependent manner

Antimicrobial resistance poses a substantial threat to human health. The gut microbiome is considered a reservoir for potential spread of resistance genes from commensals to pathogens, termed the gut resistome. The impact of probiotics, commonly consumed by many in health or in conjunction with the administration of antibiotics, on the gut resistome is elusive. Reanalysis of gut metagenomes from healthy antibiotics-naïve humans supplemented with an 11-probiotic-strain preparation, allowing direct assessment of the gut resistome in situ along the gastrointestinal (GI) tract, demonstrated that probiotics reduce the number of antibiotic resistance genes exclusively in the gut of colonization-permissive individuals. In mice and in a separate cohort of humans, a course of antibiotics resulted in expansion of the lower GI tract resistome, which was mitigated by autologous faecal microbiome transplantation or during spontaneous recovery. In contrast, probiotics further exacerbated resistome expansion in the GI mucosa by supporting the bloom of strains carrying vancomycin resistance genes but not resistance genes encoded by the probiotic strains. Importantly, the aforementioned effects were not reflected in stool samples, highlighting the importance of direct sampling to analyse the effect of probiotics and antibiotics on the gut resistome. Analysing antibiotic resistance gene content in additional published clinical trials with probiotics further highlighted the importance of person-specific metagenomics-based profiling of the gut resistome using direct sampling. Collectively, these findings suggest opposing person-specific and antibiotic-dependent effects of probiotics on the resistome, whose contribution to the spread of antimicrobial resistance genes along the human GI tract merit further studies.

Hidden Resistome: Enrichment Reveals the Presence of Clinically Relevant Antibiotic Resistance Determinants in Treated Wastewater-Irrigated Soils

Treated-wastewater (TW) irrigation transfers antibiotic-resistant bacteria (ARB) to soil, but persistence of these bacteria is generally low due to resilience of the soil microbiome. Nonetheless, wastewater-derived bacteria and associated antibiotic resistance genes (ARGs) may persist below detection levels and potentially proliferate under copiotrophic conditions. To test this hypothesis, we exposed soils from microcosm, lysimeter, and field experiments to short-term enrichment in copiotroph-stimulating media. In microcosms, enrichment stimulated growth of multidrug-resistant Escherichia coli up to 2 weeks after falling below detection limits. Lysimeter and orchard soils irrigated in-tandem with either freshwater or TW were subjected to culture-based, qPCR and shotgun metagenomic analyses prior, and subsequent, to enrichment. Although native TW- and freshwater-irrigated soil microbiomes and resistomes were similar to each other, enrichment resulted in higher abundances of cephalosporin- and carbapenem-resistant Enterobacteriaceae and in substantial differences in the composition of microbial communities and ARGs. Enrichment stimulated ARG-harboring Bacillaceae in the freshwater-irrigated soils, whereas in TWW-irrigated soils, ARG-harboring γ-proteobacterial families Enterobacteriaceae and Moraxellaceae were more profuse. We demonstrate that TW-derived ARB and associated ARGs can persist at below detection levels in irrigated soils and believe that similar short-term enrichment strategies can be applied for environmental antimicrobial risk assessment in the future.

Computational resources in the management of antibiotic resistance: Speeding up drug discovery

This article reviews more than 50 computational resources developed in past two decades for forecasting of antibiotic resistance (AR)-associated mutations, genes and genomes. More than 30 databases have been developed for AR-associated information, but only a fraction of them are updated regularly. A large number of methods have been developed to find AR genes, mutations and genomes, with most of them based on similarity-search tools such as BLAST and HMMER. In addition, methods have been developed to predict the inhibition potential of antibiotics against a bacterial strain from the whole-genome data of bacteria. This review also discuss computational resources that can be used to manage the treatment of AR-associated diseases.

Next generation sequencing approaches to evaluate water and wastewater quality

The emergence of next generation sequencing (NGS) is revolutionizing the potential to address complex microbiological challenges in the water industry. NGS technologies can provide holistic insight into microbial communities and their functional capacities in water and wastewater systems, thus eliminating the need to develop a new assay for each target organism or gene. However, several barriers have hampered wide-scale adoption of NGS by the water industry, including cost, need for specialized expertise and equipment, challenges with data analysis and interpretation, lack of standardized methods, and the rapid pace of development of new technologies. In this critical review, we provide an overview of the current state of the science of NGS technologies as they apply to water, wastewater, and recycled water. In addition, a systematic literature review was conducted in which we identified over 600 peer-reviewed journal articles on this topic and summarized their contributions to six key areas relevant to the water and wastewater fields: taxonomic classification and pathogen detection, functional and catabolic gene characterization, antimicrobial resistance (AMR) profiling, bacterial toxicity characterization, Cyanobacteria and harmful algal bloom identification, and virus characterization. For each application, we have presented key trends, noteworthy advancements, and proposed future directions. Finally, key needs to advance NGS technologies for broader application in water and wastewater fields are assessed.

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.

Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance

A multitude of factors, such as drug misuse, lack of strong regulatory measures, improper sewage disposal, and low-quality medicine and medications, have been attributed to the emergence of drug resistant microbes. The emergence and outbreaks of multidrug resistance to last-line antibiotics has become quite common. This is further fueled by the slow rate of drug development and the lack of effective resistome surveillance systems. In this review, we provide insights into the recent advances made in computational approaches for the surveillance of antibiotic resistomes, as well as experimental formulation of combinatorial drugs. We explore the multiple roles of antibiotics in nature and the current status of combinatorial and adjuvant-based antibiotic treatments with nanoparticles, phytochemical, and other non-antibiotics based on synergetic effects. Furthermore, advancements in machine learning algorithms could also be applied to combat the spread of antibiotic resistance. Development of resistance to new antibiotics is quite rapid. Hence, we review the recent literature on discoveries of novel antibiotic resistant genes though shotgun and expression-based metagenomics. To decelerate the spread of antibiotic resistant genes, surveillance of the resistome is of utmost importance. Therefore, we discuss integrative applications of whole-genome sequencing and metagenomics together with machine learning models as a means for state-of-the-art surveillance of the antibiotic resistome. We further explore the interactions and negative effects between antibiotics and microbiomes upon drug administration.

sraX: A Novel Comprehensive Resistome Analysis Tool

The accurate identification of the assortment of antibiotic resistance genes within a collection of genomes enables the discernment of intricate antimicrobial resistance (AMR) patterns while depicting the diversity of resistome profiles of the analyzed samples. The availability of large amount of sequence data, owing to the advancement of novel sequencing technologies, have conceded exciting possibilities for developing suitable AMR exploration tools. However, the level of complexity of bioinformatic analyses has raised as well, since the achievement of desired results involves executing several challenging steps. Here, sraX is proposed as a fully automated analytical pipeline for performing a precise resistome analysis. Our nominated tool is capable of scrutinizing hundreds of bacterial genomes in-parallel for detecting and annotating putative resistant determinants. Particularly, sraX presents unique features: genomic context analysis, validation of known mutations conferring resistance, illustration of drug classes and type of mutated loci proportions and integration of results into a single hyperlinked navigable HTML-formatted file. Furthermore, sraX also exhibits relevant operational features since the complete analysis is accomplished by executing a single-command step. The capacity and efficacy of sraX was demonstrated by re-analyzing 197 strains belonging to Enterococcus spp., from which we confirmed 99.15% of all detection events that were reported in the original study. sraX can be downloaded from https://github.com/lgpdevtools/srax.

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.