FARME DB: a functional antibiotic resistance element database

DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data

BACKGROUND

Antibiotics are essential for medical procedures, food security, and public health. However, ill-advised usage leads to increased pathogen resistance to antimicrobial substances, posing a threat of fatal infections and limiting the benefits of antibiotics. Therefore, early detection of antimicrobial resistance genes (ARGs), especially in pathogens, is crucial for human health. Most computational methods for ARG detection rely on homology to a predefined gene database and therefore are limited in their ability to discover novel genes.

RESULTS

We introduce DRAMMA, a machine learning method for predicting new ARGs with no sequence similarity to known ARGs or any annotated gene. DRAMMA utilizes various features, including protein properties, genomic context, and evolutionary patterns. The model demonstrated robust predictive performance both in cross-validation and an external validation set annotated by an empirical ARG database. Analyses of the high-ranking model-generated candidates revealed a significant enrichment of candidates within the Bacteroidetes/Chlorobi and Betaproteobacteria taxonomic groups.

CONCLUSIONS

DRAMMA enables rapid ARG identification for global-scale genomic and metagenomic samples, thus holding promise for the discovery of novel ARGs that lack sequence similarity to any known resistance genes. Further, our model has the potential to facilitate early detection of specific ARGs, potentially influencing the selection of antibiotics administered to patients. Video Abstract.

Mitigating Health Risks Through Environmental Tracking of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a prevalent nosocomial pathogen and a significant reservoir of antimicrobial resistance genes in residential and built environments. It is also widespread in various indoor and outdoor settings, including sewage, surface waters, soil, recreational waters (both treated and untreated), and industrial effluents. Surveillance efforts for P. aeruginosa are primarily focused on hospitals rather than built environments. However, evidence links multidrug-resistant P. aeruginosa of human origin with activity in built environments and hospital settings. Consequently, tracking this pathogen across all environments is crucial for understanding the mechanisms of reverse transmission from built environments to humans. This review explores public health hygiene by examining the prevalence of P. aeruginosa in various environments, its sequence types, the factors contributing to multidrug resistance, and the identification methods through global surveillance. Whole-genome sequencing with sequence typing and real-time quantitative PCR are widely used to identify and study antimicrobial-resistant strains worldwide. Additionally, advanced techniques such as functional metagenomics, next-generation sequencing, MALDI-TOF, and biosensors are being extensively employed to detect antimicrobial-resistant strains and mitigate the ongoing evolution of bacterial resistance to antibiotics. Our review strongly underscores the importance of environmental monitoring of P. aeruginosa in preventing human infections. Furthermore, strategic planning in built environments is essential for effective epidemiological surveillance of P. aeruginosa and the development of comprehensive risk assessment models.

What role do biocontrol agents with Mg2+ play in the fate of antibiotic resistome and pathogenic bacteria in the phyllosphere?

The contamination of the plant phyllosphere with antibiotics and antibiotic resistance genes (ARGs), caused by application of antibiotics, is a significant environmental issue in agricultural management. Alternatively, biocontrol agents are environmentally friendly and have attracted a lot of interest. However, the influence of biocontrol agents on the phyllosphere resistome remains unknown. In this study, we applied biocontrol agents to control the wildfire disease in the Solanaceae crops and investigated their effects on the resistome and the pathogen in the phyllosphere by using metagenomics. A total of 250 ARGs were detected from 15 samples, which showed a variation in distribution across treatments of biocontrol agents (BA), BA with Mg2+ (T1), BA with Mn2+ (T2), and kasugamycin (T3) and nontreated (CK). The results showed that the abundance of ARGs under the treatment of BA-Mg2+ was lower than that in the CK group. The abundance of cphA3 (carbapenem resistance), PME-1 (carbapenem resistance), tcr3 (tetracycline antibiotic resistance), and AAC (3)-VIIIa (aminoglycoside antibiotic resistance) in BA-Mg2+ was significantly higher than that in BA-Mn2+ (P < 0.05). The abundance of cphA3, PME_1, and tcr3 was significantly negatively related to the abundance of the phyllosphere pathogen Pseudomonas syringae (P < 0.05). We also found that the upstream and downstream regions of cphA3 were relatively conserved, in which rpl, rpm, and rps gene families were identified in most sequences (92%). The Ka/Ks of cphA3 was 0 in all observed sequences, indicating that under the action of purifying selection, nonsynonymous substitutions are often gradually eliminated in the population. Overall, this study clarifies the effect of biocontrol agents with Mg2+ on the distribution of the phyllosphere resistome and provides evolutionary insights into the biocontrol process.

IMPORTANCE

Our study applied metagenomics analysis to examine the impact of biocontrol agents (BAs) on the phyllosphere resistome and the pathogen. Irregular use of antibiotics has led to the escalating dissemination of antibiotic resistance genes (ARGs) in the environment. The majority of BA research has focused on the effect of monospecies on the plant disease control process, the role of the compound BA with nutrition elements in the phyllosphere disease, and the resistome is still unknown. We believe BAs are eco-friendly alternatives for antibiotics to combat the transfer of ARGs. Our results revealed that BA-Mg2+ had a lower relative abundance of ARGs compared to the CK group, and the phyllosphere pathogen Pseudomonas syringae was negatively related to three specific ARGs, cphA3, PME-1, and tcr3. These three genes also present different Ka/Ks. We believe that the identification of the distribution and evolution modes of ARGs further elucidates the ecological role and facilitates the development of BAs, which will attract general interest in this field.

Bioinformatic approaches for studying the microbiome of fermented food

High-throughput DNA sequencing-based approaches continue to revolutionise our understanding of microbial ecosystems, including those associated with fermented foods. Metagenomic and metatranscriptomic approaches are state-of-the-art biological profiling methods and are employed to investigate a wide variety of characteristics of microbial communities, such as taxonomic membership, gene content and the range and level at which these genes are expressed. Individual groups and consortia of researchers are utilising these approaches to produce increasingly large and complex datasets, representing vast populations of microorganisms. There is a corresponding requirement for the development and application of appropriate bioinformatic tools and pipelines to interpret this data. This review critically analyses the tools and pipelines that have been used or that could be applied to the analysis of metagenomic and metatranscriptomic data from fermented foods. In addition, we critically analyse a number of studies of fermented foods in which these tools have previously been applied, to highlight the insights that these approaches can provide.

A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.

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.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

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.

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.

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.

Text mining for identification of biological entities related to antibiotic resistant organisms

Antimicrobial resistance is a significant public health problem worldwide. In recent years, the scientific community has been intensifying efforts to combat this problem; many experiments have been developed, and many articles are published in this area. However, the growing volume of biological literature increases the difficulty of the biocuration process due to the cost and time required. Modern text mining tools with the adoption of artificial intelligence technology are helpful to assist in the evolution of research. In this article, we propose a text mining model capable of identifying and ranking prioritizing scientific articles in the context of antimicrobial resistance. We retrieved scientific articles from the PubMed database, adopted machine learning techniques to generate the vector representation of the retrieved scientific articles, and identified their similarity with the context. As a result of this process, we obtained a dataset labeled "Relevant" and "Irrelevant" and used this dataset to implement one supervised learning algorithm to classify new records. The model's overall performance reached 90% accuracy and the f-measure (harmonic mean between the metrics) reached 82% accuracy for positive class and 93% for negative class, showing quality in the identification of scientific articles relevant to the context. The dataset, scripts and models are available at https://github.com/engbiopct/TextMiningAMR.

Biological Functions and Applications of Antimicrobial Peptides

Abstract:

Despite antimicrobial resistance, which is attributed to the misuse of broad-spectrum antibiotics, antibiotics can indiscriminately kill pathogenic and beneficial microorganisms. These events disrupt the delicate microbial balance in both humans and animals, leading to secondary infections and other negative effects. Antimicrobial peptides (AMPs) are functional natural biopolymers in plants and animals. Due to their excellent antimicrobial activities and absence of microbial resistance, AMPs have attracted enormous research attention. We reviewed the antibacterial, antifungal, antiviral, antiparasitic, as well as antitumor properties of AMPs and research progress on AMPs. In addition, we highlighted various recommendations and potential research areas for their progress and challenges in practical applications.

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.

Phylum barrier and Escherichia coli intra-species phylogeny drive the acquisition of antibiotic-resistance genes

Escherichia coli is a ubiquitous bacterium that has been widely exposed to antibiotics over the last 70 years. It has adapted by acquiring different antibiotic-resistance genes (ARGs), the census of which we aim to characterize here. To do so, we analysed 70 301 E. coli genomes obtained from the EnteroBase database and detected 1 027 651 ARGs using the AMRFinder, Mustard and ResfinderFG ARG databases. We observed a strong phylogroup and clonal lineage specific distribution of some ARGs, supporting the argument for epistasis between ARGs and the strain genetic background. However, each phylogroup had ARGs conferring a similar antibiotic class resistance pattern, indicating phenotypic adaptive convergence. The G+C content or the type of ARG was not associated with the frequency of the ARG in the database. In addition, we identified ARGs from anaerobic, non-Proteobacteria bacteria in four genomes of E. coli, supporting the hypothesis that the transfer between anaerobic bacteria and E. coli can spontaneously occur but remains exceptional. In conclusion, we showed that phylum barrier and intra-species phylogenetic history are major drivers of the acquisition of a resistome in E. coli.

Microbial retention and resistances in stormwater quality improvement devices treating road runoff

Current knowledge about the microbial communities that occur in urban road runoff is scarce. Road runoff of trafficked roads can be heavily polluted and is treated by stormwater quality improvement devices (SQIDs). However, microbes may influence the treatment process of these devices or could lead to stress resistant opportunistic microbial strains. In this study, the microbial community in the influent, effluent and the filter materials used to remove dissolved heavy metals from two different SQIDs were analyzed to determine microbial load, retention, composition, and mobile resistance genes. Although the microbes were replaced by new taxa in the effluent, there was no major retention of microbial genera. Further, the bacterial abundance of the SQIDs effluent was relatively stable over time. The heavy metal content correlated with intl1 and with microbial genera. The filter media itself was enriched with Intl1 gene cassettes, carrying several heavy metal and multidrug resistance genes (e.g. czrA, czcA, silP, mexW and mexI), indicating that this is a hot spot for horizontal gene transfer. Overall, the results shed light on road runoff microbial communities, and pointed to distinct bacterial communities within the SQIDs, which subsequently influence the microbial community and the genes released with the treated water.

Interkingdom Gut Microbiome and Resistome of the Cockroach Blattella germanica

Cockroaches are intriguing animals with two coexisting symbiotic systems, an endosymbiont in the fat body, involved in nitrogen metabolism, and a gut microbiome whose diversity, complexity, role, and developmental dynamics have not been fully elucidated. In this work, we present a metagenomic approach to study Blattella germanica populations not treated, treated with kanamycin, and recovered after treatment, both naturally and by adding feces to the diet, with the aim of better understanding the structure and function of its gut microbiome along the development as well as the characterization of its resistome.IMPORTANCE For the first time, we analyze the interkingdom hindgut microbiome of this species, including bacteria, fungi, archaea, and viruses. Network analysis reveals putative cooperation between core bacteria that could be key for ecosystem equilibrium. We also show how antibiotic treatments alter microbiota diversity and function, while both features are restored after one untreated generation. Combining data from B. germanica treated with three antibiotics, we have characterized this species' resistome. It includes genes involved in resistance to several broad-spectrum antibiotics frequently used in the clinic. The presence of genetic elements involved in DNA mobilization indicates that they can be transferred among microbiota partners. Therefore, cockroaches can be considered reservoirs of antibiotic resistance genes (ARGs) and potential transmission vectors.

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.

Approaches for characterizing and tracking hospital-associated multidrug-resistant bacteria

Hospital-associated infections are a major concern for global public health. Infections with antibiotic-resistant pathogens can cause empiric treatment failure, and for infections with multidrug-resistant bacteria which can overcome antibiotics of "last resort" there exists no alternative treatments. Despite extensive sanitization protocols, the hospital environment is a potent reservoir and vector of antibiotic-resistant organisms. Pathogens can persist on hospital surfaces and plumbing for months to years, acquire new antibiotic resistance genes by horizontal gene transfer, and initiate outbreaks of hospital-associated infections by spreading to patients via healthcare workers and visitors. Advancements in next-generation sequencing of bacterial genomes and metagenomes have expanded our ability to (1) identify species and track distinct strains, (2) comprehensively profile antibiotic resistance genes, and (3) resolve the mobile elements that facilitate intra- and intercellular gene transfer. This information can, in turn, be used to characterize the population dynamics of hospital-associated microbiota, track outbreaks to their environmental reservoirs, and inform future interventions. This review provides a detailed overview of the approaches and bioinformatic tools available to study isolates and metagenomes of hospital-associated bacteria, and their multi-layered networks of transmission.

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.

The incidence of antibiotic resistance within and beyond the agricultural ecosystem: A concern for public health

The agricultural ecosystem creates a platform for the development and dissemination of antimicrobial resistance, which is promoted by the indiscriminate use of antibiotics in the veterinary, agricultural, and medical sectors. This results in the selective pressure for the intrinsic and extrinsic development of the antimicrobial resistance phenomenon, especially within the aquaculture‐animal‐manure‐soil‐water‐plant nexus. The existence of antimicrobial resistance in the environment has been well documented in the literature. However, the possible transmission routes of antimicrobial agents, their resistance genes, and naturally selected antibiotic‐resistant bacteria within and between the various niches of the agricultural environment and humans remain poorly understood. This study, therefore, outlines an overview of the discovery and development of commonly used antibiotics; the timeline of resistance development; transmission routes of antimicrobial resistance in the agro‐ecosystem; detection methods of environmental antimicrobial resistance determinants; factors involved in the evolution and transmission of antibiotic resistance in the environment and the agro‐ecosystem; and possible ways to curtail the menace of antimicrobial resistance.

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.

CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database

The Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca) is a curated resource providing reference DNA and protein sequences, detection models and bioinformatics tools on the molecular basis of bacterial antimicrobial resistance (AMR). CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD's Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.

RNAactDrug: a comprehensive database of RNAs associated with drug sensitivity from multi-omics data

Drug sensitivity has always been at the core of individualized cancer chemotherapy. However, we have been overwhelmed by large-scale pharmacogenomic data in the era of next-generation sequencing technology, which makes it increasingly challenging for researchers, especially those without bioinformatic experience, to perform data integration, exploration and analysis. To bridge this gap, we developed RNAactDrug, a comprehensive database of RNAs associated with drug sensitivity from multi-omics data, which allows users to explore drug sensitivity and RNA molecule associations directly. It provides association data between drug sensitivity and RNA molecules including mRNAs, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) at four molecular levels (expression, copy number variation, mutation and methylation) from integrated analysis of three large-scale pharmacogenomic databases (GDSC, CellMiner and CCLE). RNAactDrug currently stores more than 4 924 200 associations of RNA molecules and drug sensitivity at four molecular levels covering more than 19 770 mRNAs, 11 119 lncRNAs, 438 miRNAs and 4155 drugs. A user-friendly interface enriched with various browsing sections augmented with advance search facility for querying the database is offered for users retrieving. RNAactDrug provides a comprehensive resource for RNA molecules acting in drug sensitivity, and it could be used to prioritize drug sensitivity-related RNA molecules, further promoting the identification of clinically actionable biomarkers in drug sensitivity and drug development more cost-efficiently by making this knowledge accessible to both basic researchers and clinical practitioners. Database URL: http://bio-bigdata.hrbmu.edu.cn/RNAactDrug.

Sequencing-based methods and resources to study antimicrobial resistance

Antimicrobial resistance extracts high morbidity, mortality and economic costs yearly by rendering bacteria immune to antibiotics. Identifying and understanding antimicrobial resistance are imperative for clinical practice to treat resistant infections and for public health efforts to limit the spread of resistance. Technologies such as next-generation sequencing are expanding our abilities to detect and study antimicrobial resistance. This Review provides a detailed overview of antimicrobial resistance identification and characterization methods, from traditional antimicrobial susceptibility testing to recent deep-learning methods. We focus on sequencing-based resistance discovery and discuss tools and databases used in antimicrobial resistance studies.

The diversity of uncharacterized antibiotic resistance genes can be predicted from known gene variants-but not always

BACKGROUND

Antibiotic resistance is considered one of the most urgent threats to modern healthcare, and the role of the environment in resistance development is increasingly recognized. It is often assumed that the abundance and diversity of known resistance genes are representative also for the non-characterized fraction of the resistome in a given environment, but this assumption has not been verified. In this study, this hypothesis is tested, using the resistance gene profiles of 1109 metagenomes from various environments.

RESULTS

This study shows that the diversity and abundance of known antibiotic resistance genes can generally predict the diversity and abundance of undescribed resistance genes. However, the extent of this predictability is dependent on the type of environment investigated. Furthermore, it is shown that carefully selected small sets of resistance genes can describe total resistance gene diversity remarkably well.

CONCLUSIONS

The results of this study suggest that knowledge gained from large-scale quantifications of known resistance genes can be utilized as a proxy for unknown resistance factors. This is important for current and proposed monitoring efforts for environmental antibiotic resistance and has implications for the design of risk ranking strategies and the choices of measures and methods for describing resistance gene abundance and diversity in the environment.

Variability in metagenomic samples from the Puget Sound: Relationship to temporal and anthropogenic impacts

Whole-metagenome sequencing (WMS) has emerged as a powerful tool to assess potential public health risks in marine environments by measuring changes in microbial community structure and function in uncultured bacteria. In addition to monitoring public health risks such as antibiotic resistance determinants, it is essential to measure predictors of microbial variation in order to identify natural versus anthropogenic factors as well as to evaluate reproducibility of metagenomic measurements.This study expands our previous metagenomic characterization of Puget Sound by sampling new nearshore environments including the Duwamish River, an EPA superfund site, and the Hood Canal, an area characterized by highly variable oxygen levels. We also resampled a wastewater treatment plant, nearshore and open ocean sites introducing a longitudinal component measuring seasonal and locational variations and establishing metagenomics sampling reproducibility. Microbial composition from samples collected in the open sound were highly similar within the same season and location across different years, while nearshore samples revealed multi-fold seasonal variation in microbial composition and diversity. Comparisons with recently sequenced predominant marine bacterial genomes helped provide much greater species level taxonomic detail compared to our previous study. Antibiotic resistance determinants and pollution and detoxification indicators largely grouped by location showing minor seasonal differences. Metal resistance, oxidative stress and detoxification systems showed no increase in samples proximal to an EPA superfund site indicating a lack of ecosystem adaptation to anthropogenic impacts. Taxonomic analysis of common sewage influent families showed a surprising similarity between wastewater treatment plant and open sound samples suggesting a low-level but pervasive sewage influent signature in Puget Sound surface waters. Our study shows reproducibility of metagenomic data sampling in multiple Puget Sound locations while establishing baseline measurements of antibiotic resistance determinants, pollution and detoxification systems. Combining seasonal and longitudinal data across these locations provides a foundation for evaluating variation in future studies.

Antibiotic-Resistance Genes in Waste Water

Waste water and waste water treatment plants can act as reservoirs and environmental suppliers of antibiotic resistance. They have also been proposed to be hotspots for horizontal gene transfer, enabling the spread of antibiotic resistance genes between different bacterial species. Waste water contains antibiotics, disinfectants, and metals which can form a selection pressure for antibiotic resistance, even in low concentrations. Our knowledge of antibiotic resistance in waste water has increased tremendously in the past few years with advances in the molecular methods available. However, there are still some gaps in our knowledge on the subject, such as how active is horizontal gene transfer in waste water and what is the role of the waste water treatment plant in the environmental resistome? The purpose of this review is to briefly describe some of the main methods for studying antibiotic resistance in waste waters and the latest research and main knowledge gaps on the issue. In addition, some future research directions are proposed.

Next-generation approaches to understand and combat the antibiotic resistome

Antibiotic resistance is a natural feature of diverse microbial ecosystems. Although recent studies of the antibiotic resistome have highlighted barriers to the horizontal transfer of antibiotic resistance genes between habitats, the rapid global spread of genes that confer resistance to carbapenem, colistin and quinolone antibiotics illustrates the dire clinical and societal consequences of such events. Over time, the study of antibiotic resistance has grown from focusing on single pathogenic organisms in axenic culture to studying antibiotic resistance in pathogenic, commensal and environmental bacteria at the level of microbial communities. As the study of antibiotic resistance advances, it is important to incorporate this comprehensive approach to better inform global antibiotic resistance surveillance and antibiotic development. It is increasingly becoming apparent that although not all resistance genes are likely to geographically and phylogenetically disseminate, the threat presented by those that are is serious and warrants an interdisciplinary research focus. In this Review, we highlight seminal work in the resistome field, discuss recent advances in the studies of resistomes, and propose a resistome paradigm that can pave the way for the improved proactive identification and mitigation of emerging antibiotic resistance threats.