A sampling and metagenomic sequencing-based methodology for monitoring antimicrobial resistance in swine herds

Metagenomics as a Transformative Tool for Antibiotic Resistance Surveillance: Highlighting the Impact of Mobile Genetic Elements with a Focus on the Complex Role of Phages

Extensive use of antibiotics in human healthcare as well as in agricultural and environmental settings has led to the emergence and spread of antibiotic-resistant bacteria, rendering many infections increasingly difficult to treat. Coupled with the limited development of new antibiotics, the rise of antimicrobial resistance (AMR) has caused a major health crisis worldwide, which calls for immediate action. Strengthening AMR surveillance systems is, therefore, crucial to global and national efforts in combating this escalating threat. This review explores the potential of metagenomics, a sequenced-based approach to analyze entire microbial communities without the need for cultivation, as a transformative and rapid tool for improving AMR surveillance strategies as compared to traditional cultivation-based methods. We emphasize the importance of monitoring mobile genetic elements (MGEs), such as integrons, transposons, plasmids, and bacteriophages (phages), in relation to their critical role in facilitating the dissemination of genetic resistance determinants via horizontal gene transfer (HGT) across diverse environments and clinical settings. In this context, the strengths and limitations of current bioinformatic tools designed to detect AMR-associated MGEs in metagenomic datasets, including the emerging potential of predictive machine learning models, are evaluated. Moreover, the controversial role of phages in AMR transmission is discussed alongside the potential of phage therapy as a promising alternative to conventional antibiotic treatment.

Gut Microbiota of Ruminants and Monogastric Livestock: An Overview

The diversity and composition of the gut microbiota are widely recognized as fundamental factors influencing the well-being and productivity of domestic animals. Advancements in sequencing technologies have revolutionized studies in this research field, allowing for deeper insights into the composition and functionality of microbiota in livestock. Ruminants and monogastric animals exhibit distinct digestive systems and microbiota characteristics: ruminants rely on fermentation, while monogastrics use enzymatic digestion, and monogastric animals have simpler stomach structures, except for horses and rabbits, where both processes coexist. Understanding the gut microbiota's impact and composition in both animal types is essential for optimizing production efficiency and promoting animal health. Following this perspective, the present manuscript review aims to provide a comprehensive overview of the gut microbiota in ruminants (such as cattle, sheep, and goats) and monogastric animals (including horses, pigs, rabbits, and chickens).

Methodological aspects of investigating the resistome in pig farm environments

A typical One Health issue, antimicrobial resistance (AMR) development and its spread among people, animals, and the environment attracts significant research attention. The animal sector is one of the major contributors to the development and dissemination of AMR and accounts for more than 50 % of global antibiotics usage. The use of antibiotics exerts a selective pressure for resistant bacteria in the exposed microbiome, but many questions about the epidemiology of AMR in farm environments remain unanswered. This is connected to several methodological challenges and limitations, such as inconsistent sampling methods, complexity of farm environment samples and the lack of standardized protocols for sample collection, processing and bioinformatical analysis. In this project, we combined metagenomics and bioinformatics to optimise the methodology for reproducible research on the resistome in complex samples from the indoor farm environment. The work included optimizing sample collection, transportation, and storage, as well as DNA extraction, sequencing, and bioinformatic analysis, such as metagenome assembly and antibiotic resistance gene (ARG) detection. Our studies suggest that the current most optimal and cost-effective pipeline for ARG search should be based on Illumina sequencing of sock sample material at high depth (at least 25 M 250 bp PE for AMR gene families and 43 M for gene variants). We present a computational analysis utilizing MEGAHIT assembly to balance the identification of bacteria carrying ARGs with the potential loss of diversity and abundance of resistance genes. Our findings indicate that searching against multiple ARG databases is essential for detecting the highest diversity of ARGs.

Metagenomics unveils the role of hospitals and wastewater treatment plants on the environmental burden of antibiotic resistance genes and opportunistic pathogens

Antimicrobial resistance (AMR) is a global health challenge, with hospitals and wastewater treatment plants (WWTPs) serving as significant pathways for the dissemination of antibiotic resistance genes (ARGs). This study investigates the potential of wastewater-based epidemiology (WBE) as an early warning system for assessing the burden of AMR at the population level. In this comprehensive year-long study, effluent was collected weekly from three large hospitals, and treated and untreated wastewater were collected monthly from three associated community WWTPs. Metagenomic analysis revealed a significantly higher relative abundance and diversity of ARGs in hospital wastewater than in WWTPs. Notably, ARGs conferring resistance to clinically significant antibiotics such as β-lactams, aminoglycosides, sulfonamides, and tetracyclines were more prevalent in hospital effluents. Conversely, resistance genes associated with rifampicin and MLS (macrolides-lincosamide-streptogramin) were more commonly detected in the WWTPs, particularly in the treated effluent. Network analysis identified the potential bacterial hosts, which are the key carriers of these ARGs. The study further highlighted the variability in ARG removal efficiencies across the WWTPs, with none achieving complete elimination of ARGs or a significant reduction in bacterial diversity. Additionally, ARG profiles remained relatively consistent in hospital and community wastewater throughout the study, indicating a persistent release of a baseload of ARGs and pathogenic bacteria into surface waters, potentially polluting aquatic environments and entering the food chain. The study underscores the need for routine WBE surveillance, enhanced wastewater treatment strategies, and hospital-level source control measures to mitigate AMR dissemination into the environment.

Slaughtering processes impact microbial communities and antimicrobial resistance genes of pig carcasses

Several steps in the abattoir can influence the presence of microbes and associated resistance genes (ARGs) on the animal carcasses used for further meat processing. We investigated how these processes influence the resistome-microbiome of groups of pigs with different on-farm antimicrobial exposure status, from the moment they entered the abattoir until the end of carcass processing. Using a targeted enrichment metagenomic approach, we identified 672 unique ARGs conferring resistance to 43 distinct AMR classes from pooled skin (N = 42) and carcass swabs (N = 63) collected sequentially before, during, and after the slaughter process and food safety interventions. We observed significant variations in the resistome and microbial profiles of pigs before and after slaughter, as well as a significant decline in ARG counts, diversity, and microbial DNA load during slaughter and carcass processing, irrespective of prior antimicrobial treatments on the farm. These results suggest that existing interventions in the abattoir are effective in reducing not only the pathogen load but also the overall bacterial burden, including ARGs on pork carcasses. Concomitant with reductions in microbial and ARG counts, we observed an increase in the relative abundance of non-drug-specific ARGs, such as those conferring resistance to metals and biocides, and in particular mercury. Using a strict colocalization procedure, we found that most mercury ARGs were associated with genomes from the Pseudomonadaceae and Enterobacteriaceae families. Collectively, these findings demonstrate that slaughter and processing practices within the abattoir can shape the microbial and ARG profiles of pork carcasses during the transition from living muscle to meat.

Metagenomics analysis of sewage for surveillance of antimicrobial resistance in South Africa

Our 24-month study used metagenomics to investigate antimicrobial resistance (AMR) abundance in raw sewage from wastewater treatment works (WWTWs) in two municipalities in Gauteng Province, South Africa. At the AMR class level, data showed similar trends at all WWTWs, showing that aminoglycoside, beta-lactam, sulfonamide and tetracycline resistance was most abundant. AMR abundance differences were shown between municipalities, where Tshwane Metropolitan Municipality (TMM) WWTWs showed overall higher abundance of AMR compared to Ekurhuleni Metropolitan Municipality (EMM) WWTWs. Also, within each municipality, there were differing trends in AMR abundance. Notably, within TMM, certain AMR classes (macrolides and macrolides_streptogramin B) were in higher abundance at a WWTW serving an urban high-income area, while other AMR classes (aminoglycosides) were in higher abundance at a WWTW serving a semi-urban low income area. At the AMR gene level, all WWTWs samples showed the most abundance for the sul1 gene (encoding sulfonamide resistance). Following this, the next 14 most abundant genes encoded resistance to sulfonamides, aminoglycosides, macrolides, tetracyclines and beta-lactams. Notably, within TMM, some macrolide-encoding resistance genes (mefC, msrE, mphG and mphE) were in highest abundance at a WWTW serving an urban high-income area; while sul1, sul2 and tetC genes were in highest abundance at a WWTW serving a semi-urban low income area. Differential abundance analysis of AMR genes at WWTWs, following stratification of data by season, showed some notable variance in six AMR genes, of which blaKPC-2 and blaKPC-34 genes showed the highest prevalence of seasonal abundance differences when comparing data within a WWTW. The general trend was to see higher abundances of AMR genes in colder seasons, when comparing seasonal data within a WWTW. Our study investigated wastewater samples in only one province of South Africa, from WWTWs located within close proximity to one another. We would require a more widespread investigation at WWTWs distributed across all regions/provinces of South Africa, in order to describe a more comprehensive profile of AMR abundance across the country.

Meta-analysis of wastewater microbiome for antibiotic resistance profiling

The microbial composition and stress molecules are main drivers influencing the development and spread of antibiotic resistance bacteria (ARBs) and genes (ARGs) in the environment. A reliable and rapid method for identifying associations between microbiome composition and resistome remains challenging. In the present study, secondary metagenome data of sewage and hospital wastewaters were assessed for differential taxonomic and ARG profiling. Subsequently, Random Forest (RF)-based ML models were used to predict ARG profiles based on taxonomic composition and model validation on hospital wastewaters. Total ARG abundance was significantly higher in hospital wastewaters (15 ppm) than sewage (5 ppm), while the resistance towards methicillin, carbapenem, and fluoroquinolone were predominant. Although, Pseudomonas constituted major fraction, Streptomyces, Enterobacter, and Klebsiella were characteristic of hospital wastewaters. Prediction modeling showed that the relative abundance of pathogenic genera Escherichia, Vibrio, and Pseudomonas contributed most towards variations in total ARG count. Moreover, the model was able to identify host-specific patterns for contributing taxa and related ARGs with >90% accuracy in predicting the ARG subtype abundance. More than >80% accuracy was obtained for hospital wastewaters, demonstrating that the model can be validly extrapolated to different types of wastewater systems. Findings from the study showed that the ML approach could identify ARG profile based on bacterial composition including 16S rDNA amplicon data, and can serve as a viable alternative to metagenomic binning for identification of potential hosts of ARGs. Overall, this study demonstrates the promising application of ML techniques for predicting the spread of ARGs and provides guidance for early warning of ARBs emergence.

Laboratory tools for the direct detection of bacterial respiratory infections and antimicrobial resistance: a scoping review

Rapid laboratory tests are urgently required to inform antimicrobial use in food animals. Our objective was to synthesize knowledge on the direct application of long-read metagenomic sequencing to respiratory samples to detect bacterial pathogens and antimicrobial resistance genes (ARGs) compared to PCR, loop-mediated isothermal amplification, and recombinase polymerase amplification. Our scoping review protocol followed the Joanna Briggs Institute and PRISMA Scoping Review reporting guidelines. Included studies reported on the direct application of these methods to respiratory samples from animals or humans to detect bacterial pathogens ±ARGs and included turnaround time (TAT) and analytical sensitivity. We excluded studies not reporting these or that were focused exclusively on bioinformatics. We identified 5,636 unique articles from 5 databases. Two-reviewer screening excluded 3,964, 788, and 784 articles at 3 levels, leaving 100 articles (19 animal and 81 human), of which only 7 studied long-read sequencing (only 1 in animals). Thirty-two studies investigated ARGs (only one in animals). Reported TATs ranged from minutes to 2 d; steps did not always include sample collection to results, and analytical sensitivity varied by study. Our review reveals a knowledge gap in research for the direct detection of bacterial respiratory pathogens and ARGs in animals using long-read metagenomic sequencing. There is an opportunity to harness the rapid development in this space to detect multiple pathogens and ARGs on a single sequencing run. Long-read metagenomic sequencing tools show potential to address the urgent need for research into rapid tests to support antimicrobial stewardship in food animal production.

The European livestock resistome

Metagenomic sequencing has proven to be a powerful tool in the monitoring of antimicrobial resistance (AMR). Here, we provide a comparative analysis of the resistome from pigs, poultry, veal calves, turkey, and rainbow trout, for a total of 538 herds across nine European countries. We calculated the effects of per-farm management practices and antimicrobial usage (AMU) on the resistome in pigs, broilers, and veal calves. We also provide an in-depth study of the associations between bacterial diversity, resistome diversity, and AMR abundances as well as co-occurrence analysis of bacterial taxa and antimicrobial resistance genes (ARGs) and the universality of the latter. The resistomes of veal calves and pigs clustered together, as did those of avian origin, while the rainbow trout resistome was different. Moreover, we identified clear core resistomes for each specific food-producing animal species. We identified positive associations between bacterial alpha diversity and both resistome alpha diversity and abundance. Network analyses revealed very few taxa-ARG associations in pigs but a large number for the avian species. Using updated reference databases and optimized bioinformatics, previously reported significant associations between AMU, biosecurity, and AMR in pig and poultry farms were validated. AMU is an important driver for AMR; however, our integrated analyses suggest that factors contributing to increased bacterial diversity might also be associated with higher AMR load. We also found that dispersal limitations of ARGs are shaping livestock resistomes, and future efforts to fight AMR should continue to emphasize biosecurity measures.IMPORTANCEUnderstanding the occurrence, diversity, and drivers for antimicrobial resistance (AMR) is important to focus future control efforts. So far, almost all attempts to limit AMR in livestock have addressed antimicrobial consumption. We here performed an integrated analysis of the resistomes of five important farmed animal populations across Europe finding that the resistome and AMR levels are also shaped by factors related to bacterial diversity, as well as dispersal limitations. Thus, future studies and interventions aimed at reducing AMR should not only address antimicrobial usage but also consider other epidemiological and ecological factors.

Fine-tuning of post-weaning pig microbiome structure and functionality by in-feed zinc oxide and antibiotics use

Introduction

Post-weaning diarrhoea (PWD) is a multifactorial disease that affects piglets after weaning, contributing to productive and economic losses. Its control includes the use of in-feed prophylactic antibiotics and therapeutic zinc oxide (ZnO), treatments that, since 2022, are no longer permitted in the European Union due to spread of antimicrobial resistance genes and pollution of soil with heavy metals. A dysbiosis in the microbiota has been suggested as a potential risk factor of PWD onset. Understanding pig's microbiota development around weaning and its changes in response to ZnO and antibiotics is crucial to develop feasible alternatives to prophylactic and metaphylactic antimicrobial use.

Methods

This study used shotgun metagenomic sequencing to investigate the environmental and faecal microbiota on 10 farms using (Treated) or not using (ZnO-free) in-feed antibiotics and ZnO during the first 14 days post-weaning (dpw). Environmental samples from clean pens were collected at weaning day (0dpw), and faecal samples at 0, 7 and 14dpw. Diarrhoeic faecal samples were collected at 7dpw when available.

Results

The analysis of data revealed that the faecal microbiota composition and its functionality was impacted by the sampling time point (microbiota maturation after weaning) but not by the farm environment. Treatment with antibiotics and ZnO showed no effects on diversity indices while the analyses of microbiota taxonomic and functional profiles revealed increased abundance of taxa and metabolic functions associated with Phascolarctobacterium succinatutens or different species of Prevotella spp. on the Treated farms, and with Megasphaera elsdenii and Escherichia coli on the ZnO-free farms. The analysis of diarrhoea samples revealed that the treatment favoured the microbiota transition or maturation from 0dpw to 14dpw in Treated farms, resembling the composition of healthy animals, when compared to diarrhoea from ZnO-free farms, which were linked in composition to 0dpw samples.

Discussion

The results provide a comprehensive overview of the beneficial effects of ZnO and antibiotics in PWD in the microbiota transition after weaning, preventing the overgrowth of pathogens such as pathogenic E. coli and revealing the key aspects in microbiota maturation that antibiotics or ZnO alternatives should fulfil.

Modelling the effectiveness of surveillance based on metagenomics in detecting, monitoring, and forecasting antimicrobial resistance in livestock production under economic constraints

Current surveillance of antimicrobial resistance (AMR) is mostly based on testing indicator bacteria using minimum inhibitory concentration (MIC) panels. Metagenomics has the potential to identify all known antimicrobial resistant genes (ARGs) in complex samples and thereby detect changes in the occurrence earlier. Here, we simulate the results of an AMR surveillance program based on metagenomics in the Danish pig population. We modelled both an increase in the occurrence of ARGs and an introduction of a new ARG in a few farms and the subsequent spread to the entire population. To make the simulation realistic, the total cost of the surveillance was constrained, and the sampling schedule was set at one pool per month with 5, 20, 50, or 100 samples. Our simulations demonstrate that a pool of 20-50 samples and a sequencing depth of 250 million fragments resulted in the shortest time to detection in both scenarios, with a time delay to detection of change of [Formula: see text]15 months in all scenarios. Compared with culture-based surveillance, our simulation indicates that there are neither significant reductions nor increases in time to detect a change using metagenomics. The benefit of metagenomics is that it is possible to monitor all known resistance in one sampling and laboratory procedure in contrast to the current monitoring that is based on the phenotypic characterisation of selected indicator bacterial species. Therefore, overall changes in AMR in a population will be detected earlier using metagenomics due to the fact that the resistance gene does not have to be transferred to and expressed by an indicator bacteria before it is possible to detect.

The resistomes of rural and urban pigs and poultry in Ghana

IMPORTANCE

To the best of our knowledge, this is the first report on the resistomes that are measured using metagenomics in livestock from Sub-Saharan Africa. We find notable differences in the microbiomes between both pigs and poultry, and those also varied markedly compared to similar samples from Europe. However, for both animal species, the same bacterial taxa drove such differences. In pigs and urban free-range poultry, we find a very low abundance of antimicrobial resistance genes (ARGs), whereas rural free-range poultry displayed similarity to the European average, and industrialized poultry exhibited higher levels. These findings show how different African livestock bacterial communities and resistomes are from their European counterparts. They also underscore the importance of continued surveillance and investigation into antimicrobial resistance across diverse ecosystems, contributing significantly to global efforts toward combating the threat of antibiotic resistance.

Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria

Antibiotic resistance is a global public health concern, posing a significant threat to the effectiveness of antibiotics in treating bacterial infections. The accurate and timely detection of antibiotic-resistant bacteria is crucial for implementing appropriate treatment strategies and preventing the spread of resistant strains. This manuscript provides an overview of the current and emerging technologies used for the detection of antibiotic-resistant bacteria. We discuss traditional culture-based methods, molecular techniques, and innovative approaches, highlighting their advantages, limitations, and potential future applications. By understanding the strengths and limitations of these technologies, researchers and healthcare professionals can make informed decisions in combating antibiotic resistance and improving patient outcomes.

Antimicrobial resistance monitoring in the Danish swine production by phenotypic methods and metagenomics from 1999 to 2018

BackgroundIn Denmark, antimicrobial resistance (AMR) in pigs has been monitored since 1995 by phenotypic approaches using the same indicator bacteria. Emerging methodologies, such as metagenomics, may allow novel surveillance ways.AimThis study aimed to assess the relevance of indicator bacteria (Escherichia coli and Enterococcus faecalis) for AMR surveillance in pigs, and the utility of metagenomics.MethodsWe collated existing data on AMR and antimicrobial use (AMU) from the Danish surveillance programme and performed metagenomics sequencing on caecal samples that had been collected/stored through the programme during 1999-2004 and 2015-2018. We compared phenotypic and metagenomics results regarding AMR, and the correlation of both with AMU.ResultsVia the relative abundance of AMR genes, metagenomics allowed to rank these genes as well as the AMRs they contributed to, by their level of occurrence. Across the two study periods, resistance to aminoglycosides, macrolides, tetracycline, and beta-lactams appeared prominent, while resistance to fosfomycin and quinolones appeared low. In 2015-2018 sulfonamide resistance shifted from a low occurrence category to an intermediate one. Resistance to glycopeptides consistently decreased during the entire study period. Outcomes of both phenotypic and metagenomics approaches appeared to positively correlate with AMU. Metagenomics further allowed to identify multiple time-lagged correlations between AMU and AMR, the most evident being that increased macrolide use in sow/piglets or fatteners led to increased macrolide resistance with a lag of 3-6 months.ConclusionWe validated the long-term usefulness of indicator bacteria and showed that metagenomics is a promising approach for AMR surveillance.

Quantitative PCR versus metagenomics for monitoring antibiotic resistance genes: balancing high sensitivity and broad coverage

The widespread occurrence of clinically relevant antibiotic resistance within humans, animals, and environment motivates the development of sensitive and accurate detection and quantification methods. Metagenomics and quantitative PCR (qPCR) are amongst the most used approaches. In this study, we aimed to evaluate and compare the performance of these methods to screen antibiotic resistance genes in animal faecal, wastewater, and water samples. Water and wastewater samples were from hospital effluent, different treatment stages of two treatment plants, and of the receiving river at the discharge point. The animal samples were from pig and chicken faeces. Antibiotic resistance gene coverage, sensitivity, and usefulness of the quantitative information were analyzed and discussed. While both methods were able to distinguish the resistome profiles and detect gradient stepwise mixtures of pig and chicken faeces, qPCR presented higher sensitivity for the detection of a few antibiotic resistance genes in water/wastewater. In addition, the comparison of predicted and observed antibiotic resistance gene quantifications unveiled the higher accuracy of qPCR. Metagenomics analyses, while less sensitive, provided a markedly higher coverage of antibiotic resistance genes compared to qPCR. The complementarity of both methods and the importance of selecting the best method according to the study purpose are discussed.

The ALEX algorithm - estimating average lifetime antimicrobial exposure of danish slaughter pigs in a fast, automated and robust way

Calculating and estimating antimicrobial exposure at specific batch level is key to understanding consumption patterns of antimicrobials in the Danish pig sector. Understanding consumption and trading patterns can assist in developing treatment plans at national levels and might lead to reducing antimicrobial resistance levels. The ALEX algorithm is a fast, automated and robust algorithm developed to estimate the average lifetime antimicrobial exposure of Danish slaughter pigs. The algorithm estimates antimicrobial exposure in the different life stages of the pig (piglet, weaner and finisher) together with the type of production network (the number of farms within a network and ownership of these). We present the algorithm and give two examples of usage. Furthermore, we compare the ALEX algorithm with an acknowledged exposure estimation algorithm, and we present a sensitivity analysis.

Insights on the effects of antimicrobial and heavy metal usage on the antimicrobial resistance profiles of pigs based on culture-independent studies

Antimicrobial resistance is a global threat to human, animal, and environmental health. In pig production, antimicrobials and heavy metals such as zinc oxide are commonly used for treatment and prevention of disease. Nevertheless, the effects of antimicrobials and heavy metals on the porcine resistome composition and the factors influencing this resistance profile are not fully understood. Advances in technologies to determine the presence of antimicrobial resistance genes in diverse sample types have enabled a more complete understanding of the resistome and the factors which influence its composition. The aim of this review is to provide a greater understanding of the influence of antimicrobial and heavy metal usage on the development and transmission of antimicrobial resistance on pig farms. Furthermore, this review aims to identify additional factors that can affect the porcine resistome. Relevant literature that used high-throughput sequencing or quantitative PCR methods to examine links between antimicrobial resistance and antimicrobial and heavy metal use was identified using a systematic approach with PubMed (NCBI), Scopus (Elsevier), and Web of Science (Clarivate Analytics) databases. In total, 247 unique records were found and 28 publications were identified as eligible for inclusion in this review. Based on these, there is clear evidence that antimicrobial and heavy metal use are positively linked with antimicrobial resistance in pigs. Moreover, associations of genes conferring antimicrobial resistance with mobile genetic elements, the microbiome, and the virome were reported, which were further influenced by the host, the environment, or the treatment itself.

Antibiotics degradation by advanced oxidation process (AOPs): Recent advances in ecotoxicity and antibiotic-resistance genes induction of degradation products

Antibiotic contamination could cause serious risks of ecotoxicity and resistance gene induction. Advanced oxidation processes (AOPs) such as Fenton, photocatalysis, activated persulfate, electrochemistry and other AOPs technologies have been proven effective in the degradation of high-risk, refractory organic pollutants such as antibiotics. However, due to the limited mineralization ability, a large number of degradation intermediates will be produced in the oxidation process. The residual or undiscovered ecological risks of degradation products are potential safety hazards and problems necessitating comprehensive studies. In-depth investigations especially on the full assessments of ecotoxicity and resistance genes induction capability of antibiotic degradation products are important issues in reducing the environmental problems of antibiotics. Therefore, this review presents an overview of the current knowledge on the efficiency of different AOPs systems in reducing antibiotics toxicity and antibiotic resistance.

Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock

A debate is currently ongoing as to whether intensive livestock farms may constitute reservoirs of clinically relevant antimicrobial resistance (AMR), thus posing a threat to surrounding communities. Here, combining shotgun metagenome sequencing, machine learning (ML), and culture-based methods, we focused on a poultry farm and connected slaughterhouse in China, investigating the gut microbiome of livestock, workers and their households, and microbial communities in carcasses and soil. For both the microbiome and resistomes in this study, differences are observed across environments and hosts. However, at a finer scale, several similar clinically relevant antimicrobial resistance genes (ARGs) and similar associated mobile genetic elements were found in both human and broiler chicken samples. Next, we focused on Escherichia coli, an important indicator for the surveillance of AMR on the farm. Strains of E. coli were found intermixed between humans and chickens. We observed that several ARGs present in the chicken faecal resistome showed correlation to resistance/susceptibility profiles of E. coli isolates cultured from the same samples. Finally, by using environmental sensing these ARGs were found to be correlated to variations in environmental temperature and humidity. Our results show the importance of adopting a multi-domain and multi-scale approach when studying microbial communities and AMR in complex, interconnected environments.

Gut Microbiome Studies in Livestock: Achievements, Challenges, and Perspectives

The variety and makeup of the gut microbiome are frequently regarded as the primary determinants of health and production performances in domestic animals. High-throughput DNA/RNA sequencing techniques (NGS) have recently gained popularity and permitted previously unheard-of advancements in the study of gut microbiota, particularly for determining the taxonomic composition of such complex communities. Here, we summarize the existing body of knowledge on livestock gut microbiome, discuss the state-of-the-art in sequencing techniques, and offer predictions for next research. We found that the enormous volumes of available data are biased toward a small number of globally distributed and carefully chosen varieties, while local breeds (or populations) are frequently overlooked despite their demonstrated resistance to harsh environmental circumstances. Furthermore, the bulk of this research has mostly focused on bacteria, whereas other microbial components such as protists, fungi, and viruses have received far less attention. The majority of these data were gathered utilizing traditional metabarcoding techniques that taxonomically identify the gut microbiota by analyzing small portions of their genome (less than 1000 base pairs). However, to extend the coverage of microbial genomes for a more precise and thorough characterization of microbial communities, a variety of increasingly practical and economical shotgun techniques are currently available.

Antimicrobial Resistance and Its Drivers-A Review

Antimicrobial resistance (AMR) is a critical issue in health care in terms of mortality, quality of services, and financial damage. In the battle against AMR, it is crucial to recognize the impacts of all four domains, namely, mankind, livestock, agriculture, and the ecosystem. Many sociocultural and financial practices that are widespread in the world have made resistance management extremely complicated. Several pathways, including hospital effluent, agricultural waste, and wastewater treatment facilities, have been identified as potential routes for the spread of resistant bacteria and their resistance genes in soil and surrounding ecosystems. The overuse of uncontrolled antibiotics and improper treatment and recycled wastewater are among the contributors to AMR. Health-care organizations have begun to address AMR, although they are currently in the early stages. In this review, we provide a brief overview of AMR development processes, the worldwide burden and drivers of AMR, current knowledge gaps, monitoring methodologies, and global mitigation measures in the development and spread of AMR in the environment.

Detection of Klebsiella pneumoniae human gut carriage: a comparison of culture, qPCR, and whole metagenomic sequencing methods

Klebsiella pneumoniae is an important opportunistic healthcare-associated pathogen and major contributor to the global spread of antimicrobial resistance. Gastrointestinal colonization with K. pneumoniae is a major predisposing risk factor for infection and forms an important hub for the dispersal of resistance. Current culture-based detection methods are time consuming, give limited intra-sample abundance and strain diversity information, and have uncertain sensitivity. Here we investigated the presence and abundance of K. pneumoniae at the species and strain level within fecal samples from 103 community-based adults by qPCR and whole metagenomic sequencing (WMS) compared to culture-based detection. qPCR demonstrated the highest sensitivity, detecting K. pneumoniae in 61.2% and 75.8% of direct-fecal and culture-enriched sweep samples, respectively, including 52/52 culture-positive samples. WMS displayed lower sensitivity, detecting K. pneumoniae in 71.2% of culture-positive fecal samples at a 0.01% abundance cutoff, and was inclined to false positives in proportion to the relative abundance of other Enterobacterales present. qPCR accurately quantified K. pneumoniae to 16 genome copies/reaction while WMS could estimate relative abundance to at least 0.01%. Quantification by both methods correlated strongly with each other (Spearman's rho = 0.91). WMS also supported accurate intra-sample K. pneumoniae sequence type (ST)-level diversity detection from fecal microbiomes to 0.1% relative abundance, agreeing with the culture-based detected ST in 16/19 samples. Our results show that qPCR and WMS are sensitive and reliable tools for detection, quantification, and strain analysis of K. pneumoniae from fecal samples with potential to support infection control and enhance insights in K. pneumoniae gastrointestinal ecology.

Search term “One Health” remains of limited use to identify relevant scientific publications: Denmark as a case study

One Health has become a popular approach, and scientific advancements in the field should be easily findable and accessible to a wide range of relevant audiences, from researchers to policymakers, and across sectors. We conducted a systematic narrative review of available scientific publications concerning One Health in the setting of Denmark that were retrievable using “One Health” as the key search term. Three searches in two databases yielded 30 retrieved publications, 13 of which were included in the review. The included publications had been published between 2015 and 2021. Twelve of the included publications were co-authored in collaboration across institutes from different sectors. Three of the included publications had focus on antimicrobial resistance, three on disease surveillance and/or control, and five were assessments or evaluations. The overall number of publications identified by a search using “One Health” as the key search term was small, and the search identified some publications that were not relevant to One Health. Our work thus highlights a missed scientific and communication opportunity of signposting articles as relevant to One Health. Using the expression “One Health” as keyword could help making One Health research more easily findable and thereby obtaining an overview of research in the field.

The vaginal microbiome following orally administered probiotic

INTRODUCTION

Here we present a longitudinal shotgun sequencing metagenomics study of 16 healthy, Danish women in the reproductive age. The aim of the study was to investigate whether lactobacilli, orally consumed, had any impact on the vaginal microbiome and its functional potential.

MATERIALS AND METHODS

The 16 women aged 19-45 years were recruited from Copenhagen, Denmark. One baseline vaginal sample (day 0) and two study samples (day 25-30 and day 55-60, respectively) were sampled. The vaginal samples were analyzed by shotgun metagenomics.

RESULTS

We detected 26 species in the vaginal microbiota of the 16 women, of which six belonged to the Lactobacillus genus. We observed three vaginal microbiome clusters mainly dominated by Gardnerella vaginalis, Lactobacillus iners or Lactobacillus crispatus. The oral probiotic had no detectable effect on either the composition or the functional potential of the vaginal microbiota.

DISCUSSION

Most of the study subjects (11 out of 16 women) exhibited only minor changes in the vaginal microbiome during the treatment with probiotics. Any compositional changes could not be associated to the probiotic treatment. Future studies may benefit from an increased number of participants, and administration of the probiotics during conditions with bacterial imbalance (e.g. during/after antibiotic treatment) or the use of different Lactobacillus spp. known to colonize the vagina.

Widespread stop-codon recoding in bacteriophages may regulate translation of lytic genes

Bacteriophages (phages) are obligate parasites that use host bacterial translation machinery to produce viral proteins. However, some phages have alternative genetic codes with reassigned stop codons that are predicted to be incompatible with bacterial translation systems. We analysed 9,422 phage genomes and found that stop-codon recoding has evolved in diverse clades of phages that infect bacteria present in both human and animal gut microbiota. Recoded stop codons are particularly over-represented in phage structural and lysis genes. We propose that recoded stop codons might function to prevent premature production of late-stage proteins. Stop-codon recoding has evolved several times in closely related lineages, which suggests that adaptive recoding can occur over very short evolutionary timescales.

Methodology for laboratory-based antimicrobial resistance surveillance in animals

Antimicrobial resistance (AMR) is a crucial and emerging multifactorial “One Health” problem involving human and animal health, agriculture, aquaculture, and environment; and posing a potential public health hazard globally. The containment of AMR justifies effective surveillance programs to explicate the magnitude of the problem across the contributing sectors. Laboratory-based AMR testing and characterization is the key component of an AMR surveillance program. An AMR surveillance program should have a “top management” for fund mobilization, planning, formulating, and multilateral coordinating of the surveillance activities. The top management should identify competent participating laboratories to form a network comprising a reference laboratory and an adequate number of sentinel laboratories. The responsibilities of the reference laboratory include the development of standardized test methods for ensuring quality and homogeneity of surveillance activities, providing training to the laboratory personnel, and in-depth AMR characterization. The sentinel laboratories will take the responsibilities of receiving samples, isolation and identification of microbes, and initial AMR characterization. The sentinel laboratories will use simple antimicrobial susceptibility test (AST) methods such as disk diffusion tests, whereas the reference laboratories should use automated quantitative AST methods as well as advanced molecular methods to explicit AMR emergence mechanisms. Standard guidelines set by Clinical Laboratory Standards Institute or the European Committee on Antimicrobial Susceptibility Testing, should be followed to bring about conformity and harmonization in the AST procedures. AMR surveillance program in animals is eventually similar to that in human health with the exception is that veterinary antibiotics and veterinary pathogens should be given preference here. Hence, the review study was envisaged to look deep into the structure of the AMR surveillance program with significance on laboratory-based AMR testing and characterization methods.

OUP accepted manuscript

Background

Real-time quantitative PCR (qPCR) is an affordable method to quantify antimicrobial resistance gene (ARG) targets, allowing comparisons of ARG abundance along animal production chains.

Objectives

We present a comparison of ARG abundance across various animal species, production environments and humans in Europe. AMR variation sources were quantified. The correlation of ARG abundance between qPCR data and previously published metagenomic data was assessed.

Methods

A cross-sectional study was conducted in nine European countries, comprising 9572 samples. qPCR was used to quantify abundance of ARGs [aph(3′)-III, erm(B), sul2, tet(W)] and 16S rRNA. Variance component analysis was conducted to explore AMR variation sources. Spearman’s rank correlation of ARG abundance values was evaluated between pooled qPCR data and earlier published pooled metagenomic data.

Results

ARG abundance varied strongly among animal species, environments and humans. This variation was dominated by between-farm variation (pigs) or within-farm variation (broilers, veal calves and turkeys). A decrease in ARG abundance along pig and broiler production chains (‘farm to fork’) was observed. ARG abundance was higher in farmers than in slaughterhouse workers, and lowest in control subjects. ARG abundance showed a high correlation (Spearman’s ρ > 0.7) between qPCR data and metagenomic data of pooled samples.

Conclusions

qPCR analysis is a valuable tool to assess ARG abundance in a large collection of livestock-associated samples. The between-country and between-farm variation of ARG abundance could partially be explained by antimicrobial use and farm biosecurity levels. ARG abundance in human faeces was related to livestock antimicrobial resistance exposure.

Antibiotic resistance in microbes: History, mechanisms, therapeutic strategies and future prospects

Antibiotics have been used to cure bacterial infections for more than 70 years, and these low-molecular-weight bioactive agents have also been used for a variety of other medicinal applications. In the battle against microbes, antibiotics have certainly been a blessing to human civilization by saving millions of lives. Globally, infections caused by multidrug-resistant (MDR) bacteria are on the rise. Antibiotics are being used to combat diversified bacterial infections. Synthetic biology techniques, in combination with molecular, functional genomic, and metagenomic studies of bacteria, plants, and even marine invertebrates are aimed at unlocking the world's natural products faster than previous methods of antibiotic discovery. There are currently only few viable remedies, potential preventive techniques, and a limited number of antibiotics, thereby necessitating the discovery of innovative medicinal approaches and antimicrobial therapies. MDR is also facilitated by biofilms, which makes infection control more complex. In this review, we have spotlighted comprehensively various aspects of antibiotics viz. overview of antibiotics era, mode of actions of antibiotics, development and mechanisms of antibiotic resistance in bacteria, and future strategies to fight the emerging antimicrobial resistant threat.

Mobility of antibiotic resistance and its co-occurrence with metal resistance in pathogens under oxidative stress

The bacterial communities are challenged with oxidative stress during their exposure to bactericidal antibiotics, metals, and different levels of dissolved oxygen (DO) encountered in diverse environmental habitats. The frequency of antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) co-selection is increased by selective pressure posed by oxidative stress. Hence, study of resistance acquisition is important from an evolutionary perspective. To understand the dependence of oxidative stress on the dissemination of ARGs and MRGs through a pathogenic bacterial population, 12 metagenomes belonging to gut, water and soil habitats were evaluated. The metagenome-wide analysis showed the chicken gut to pose the most diverse pool of ARGs (30.4 ppm) and pathogenic bacteria (Simpson diversity = 0.98). The most common types of resistances found in all the environmental samples were efflux pumps (13.22 ppm) and genes conferring resistance to vancomycin (12.4 ppm), tetracycline (12.1 ppm), or beta-lactam (9.4 ppm) antibiotics. Additionally, limiting DO level in soil was observed to increase the abundance of excision nucleases (uvrA and uvrB), DNA polymerase (polA), catalases (katG), and other oxidative stress response genes (OSGs). This was further evident from major variations occurred in antibiotic efflux genes due to the effect of DO concentration on two human pathogens, namely Salmonella enterica and Shigella sonnei found in all the selected habitats. In conclusion, the microbial community, when challenged with oxidative stress caused by environmental variations in oxygen level, tends to accumulate higher amounts of ARGs with increased dissemination potential through triggering non-lethal mutagenesis. Furthermore, the genetic linkage or co-occurrence of ARGs and MRGs provides evidence for selecting ARGs under high concentrations of heavy metals.

Next-Generation Food Research: Use of Meta-Omic Approaches for Characterizing Microbial Communities Along the Food Chain

Microorganisms exist along the food chain and impact the quality and safety of foods in both positive and negative ways. Identifying and understanding the behavior of these microbial communities enable the implementation of preventative or corrective measures in public health and food industry settings. Current culture-dependent microbial analyses are time-consuming and target only specific subsets of microbes. However, the greater use of culture-independent meta-omic approaches has the potential to facilitate a thorough characterization of the microbial communities along the food chain. Indeed, these methods have shown potential in contributing to outbreak investigation, ensuring food authenticity, assessing the spread of antimicrobial resistance, tracking microbial dynamics during fermentation and processing, and uncovering the factors along the food chain that impact food quality and safety. This review examines the community-based approaches, and particularly the application of sequencing-based meta-omics strategies, for characterizing microbial communities along the food chain. Expected final online publication date for the Annual Review of Food Science and Technology, Volume 13 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Patterns of Gene Content and Co-occurrence Constrain the Evolutionary Path toward Animal Association in Candidate Phyla Radiation Bacteria

Candidate Phyla Radiation (CPR) bacteria are small, likely episymbiotic organisms found across Earth's ecosystems. Despite their prevalence, the distribution of CPR lineages across habitats and the genomic signatures of transitions among these habitats remain unclear. Here, we expand the genome inventory for Absconditabacteria (SR1), Gracilibacteria, and Saccharibacteria (TM7), CPR bacteria known to occur in both animal-associated and environmental microbiomes, and investigate variation in gene content with habitat of origin. By overlaying phylogeny with habitat information, we show that bacteria from these three lineages have undergone multiple transitions from environmental habitats into animal microbiomes. Based on co-occurrence analyses of hundreds of metagenomes, we extend the prior suggestion that certain Saccharibacteria have broad bacterial host ranges and constrain possible host relationships for Absconditabacteria and Gracilibacteria. Full-proteome analyses show that animal-associated Saccharibacteria have smaller gene repertoires than their environmental counterparts and are enriched in numerous protein families, including those likely functioning in amino acid metabolism, phage defense, and detoxification of peroxide. In contrast, some freshwater Saccharibacteria encode a putative rhodopsin. For protein families exhibiting the clearest patterns of differential habitat distribution, we compared protein and species phylogenies to estimate the incidence of lateral gene transfer and genomic loss occurring over the species tree. These analyses suggest that habitat transitions were likely not accompanied by large transfer or loss events but rather were associated with continuous proteome remodeling. Thus, we speculate that CPR habitat transitions were driven largely by availability of suitable host taxa and were reinforced by acquisition and loss of some capacities. IMPORTANCE Studying the genetic differences between related microorganisms from different environment types can indicate factors associated with their movement among habitats. This is particularly interesting for bacteria from the Candidate Phyla Radiation because their minimal metabolic capabilities require associations with microbial hosts. We found that shifts of Absconditabacteria, Gracilibacteria, and Saccharibacteria between environmental ecosystems and mammalian mouths/guts probably did not involve major episodes of gene gain and loss; rather, gradual genomic change likely followed habitat migration. The results inform our understanding of how little-known microorganisms establish in the human microbiota where they may ultimately impact health.

Health Monitoring of Laboratory Rodent Colonies-Talking about (R)evolution

The health monitoring of laboratory rodents is essential for ensuring animal health and standardization in biomedical research. Progress in housing, gnotobiotic derivation, and hygienic monitoring programs led to enormous improvement of the microbiological quality of laboratory animals. While traditional health monitoring and pathogen detection methods still serve as powerful tools for the diagnostics of common animal diseases, molecular methods develop rapidly and not only improve test sensitivities but also allow high throughput analyses of various sample types. Concurrently, to the progress in pathogen detection and elimination, the research community becomes increasingly aware of the striking influence of microbiome compositions in laboratory animals, affecting disease phenotypes and the scientific value of research data. As repeated re-derivation cycles and strict barrier husbandry of laboratory rodents resulted in a limited diversity of the animals' gut microbiome, future monitoring approaches will have to reform-aiming at enhancing the validity of animal experiments. This review will recapitulate common health monitoring concepts and, moreover, outline strategies and measures on coping with microbiome variation in order to increase reproducibility, replicability and generalizability.

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.

Study of indiscriminate distribution of restrained antimicrobial resistome of different environmental niches

Prophylactic usage and high persistent nature of several antibiotics have put selective pressure on the native microbial population that led to the emergence, propagation, and persistence of antibiotic resistance in nature. The surveillance of antibiotic resistome pattern and identification of points of intervention throughout the different environmental habitats will help to break the flow of antibiotic resistance from environmental bacteria to human pathogens. The present study compares the occurrence, diversity, and abundance of ARGs in industrial sludge, wetland sludge, and sediment sample contaminated with pharmaceutical discharge. Metagenomes were mined for the presence of ARGs against the ResFinder 3.2 database using BLASTn program. Pharmaceutical sample (2.52%) showed high degree of ARG abundance and richness as compared with ETP sludge (2.28%) and wetland sludge samples (1.29%). The modern resistome pattern represented by critically important resistance genes against tetracycline (tetA, tetC, tetW, tetT, and tetS/M) and quinolone (qnrS, qnrVC, and qnrD) was identified in pharmaceutical sediment sample. However, effluent treatment plant (ETP) sludge sample showed abundance of multidrug efflux pumps indicating the presence of primitive resistome profile. In conclusion, the indiscriminate distribution pattern of antibiotic resistance genes in three selected environmental sites suggests enrichment and distribution of environmental niche-driven resistance. The study also suggests effluent discharge site from pharmaceutical industries and ETPs as pivotal points of intervention for the mitigation of antibiotic resistance.

Robustness in quantifying the abundance of antimicrobial resistance genes in pooled faeces samples from batches of slaughter pigs using metagenomics analysis

OBJECTIVES

With the continued spread of antimicrobial resistance (AMR) in animals, it is important to assess its occurrence throughout a microbiome quantitatively in order to evaluate significantly affecting factors, e.g. antimicrobial usage. Metagenomics methods make it possible to measure the abundance of AMR genes in complex samples such as pooled faeces samples from batches of slaughter pigs. This study was performed to determine the random error in pooled samples from batches of pigs at slaughter and the measurement error from the metagenomics processes.

METHODS

In four farms, two pooled samples were obtained from a batch of slaughter pigs by two individual samplers, and each pooled sample was thereafter processed twice. Hierarchically clustered heatmaps were applied to evaluate dissimilarities between samples. The coefficient of variation was used to calculate the percentage difference between samples from the same farm.

RESULTS

Results of the analysis revealed that it was not possible to quantitatively separate the variation arising from sampling and metagenomics processes. They both contributed to the overall measurement error in batches of slaughter pigs.

CONCLUSION

Sampling of single pigs in 30 randomly selected pig pens within the farms provides a composition representative for frequently occurring AMR genes present within the farms, while rare genes were not dispersed in a similar manner. Aggregating the resistance abundance at gene family or antimicrobial class level will reduce the apparent variation originating from errors in sampling and metagenomics processing.

Differential prevalence and host-association of antimicrobial resistance traits in disinfected and non-disinfected drinking water systems

Antimicrobial resistance (AMR) in drinking water has received less attention than its counterparts in the urban water cycle. While culture-based techniques or gene-centric PCR have been used to probe the impact of treatment approaches (e.g., disinfection) on AMR in drinking water, to our knowledge there is no systematic comparison of AMR trait distribution and prevalence between disinfected and disinfectant residual-free drinking water systems. We used metagenomics to assess the associations between disinfectant residuals and AMR prevalence and its host association in full-scale drinking water distribution systems (DWDSs) with and without disinfectant residuals. While the differences in AMR profiles between DWDSs were associated with the presence or absence of disinfectant, they were also associated with overall water chemistry and more importantly with microbial community structure. AMR genes and mechanisms differentially abundant in disinfected systems were primarily associated with nontuberculous mycobacteria (NTM). Finally, evaluation of metagenome assembled genomes (MAGs) also suggests that NTM possessing AMR genes conferring intrinsic resistance to key antibiotics were prevalent in disinfected systems, whereas such NTM genomes were not detected in disinfectant residual free DWDSs. Altogether, our findings provide insights into the drinking water resistome and its association with potential opportunistic pathogens, particularly in systems with disinfectant residual.

Temporal Resistome and Microbial Community Dynamics in an Intensive Aquaculture Facility with Prophylactic Antimicrobial Treatment

Excessive use of antimicrobials in aquaculture is concerning, given possible environmental ramifications and the potential contribution to the spread of antimicrobial resistance (AR). In this study, we explored seasonal abundance of antimicrobial resistance genes and bacterial community composition in the water column of an intensive aquaculture pond stocked with Silver Carp (Hypophthalmichthys molitrix) prophylactically treated with sulfamethoprim (25% sulfadiazine; 5% trimethoprim), relative to an adjacent unstocked reservoir. Bacterial community composition was monitored using high-throughput sequencing of 16S rRNA gene amplicons in eight sampling profiles to determine seasonal dynamics, representing principal stages in the fish fattening cycle. In tandem, qPCR was applied to assess relative abundance of selected antimicrobial resistance genes (sul1, sul2, dfrA1, tetA and blaTEM) and class-1 integrons (int1). Concomitantly, resistomes were extrapolated from shotgun metagenomes in representative profiles. Analyses revealed increased relative abundance of sulfonamide and tetracycline resistance genes in fishpond-03, relative to pre-stocking and reservoir levels, whereas no significant differences were observed for genes encoding resistance to antimicrobials that were not used in the fishpond-03. Seasons strongly dictated bacterial community composition, with high abundance of cyanobacteria in summer and increased relative abundance of Flavobacterium in the winter. Our results indicate that prophylactic use of sulfonamides in intensive aquaculture ponds facilitates resistance suggesting that prophylactic use of these antimicrobials in aquaculture should be restricted.

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.

Incorporating the plasmidome into antibiotic resistance surveillance in animal agriculture

Mobile genetic elements (MGE) carrying resistance genes represent a unique challenge to risk assessment and surveillance of antimicrobial resistance (AMR). Yet determining the mobility of resistance genes within animal microbiomes is essential to evaluating the potential dissemination from livestock to potential human pathogens, as well as evaluating co-selection mechanisms that may impact persistence of resistance genes with changing antibiotic use patterns. Current surveillance efforts utilize phenotypic testing and sequencing of individual isolates for tracking of AMR in livestock. In this work, we investigated the utility of using long-read sequencing of the plasmids from mixed Enterobacterales enrichments of swine fecal samples as a surveillance strategy for AMR plasmids. Enrichments were performed in either MacConkey broth without selection or with selection by addition of tetracycline or ceftriaxone, and plasmids were extracted and sequenced in order to evaluate the diversity of plasmids enriched by each method. Intact resistance plasmids were successfully assembled, as well as complex resistance transposons carrying multiple repeated elements that would interfere with assembly by short read sequencing technologies. Comparison of the assembled plasmids with representatives from public databases confirmed the quality of the assemblies and also revealed the occurrence of IncI2 plasmids carrying bla_CMY-2 in Ontario swine samples, which have not been found in previous studies.

Population-level surveillance of antibiotic resistance in Escherichia coli through sewage analysis

IntroductionThe occurrence of antibiotic resistance in faecal bacteria in sewage is likely to reflect the current local clinical resistance situation.AimThis observational study investigated the relationship between Escherichia coli resistance rates in sewage and clinical samples representing the same human populations.MethodsE. coli were isolated from eight hospital (n = 721 isolates) and six municipal (n = 531 isolates) sewage samples, over 1 year in Gothenburg, Sweden. An inexpensive broth screening method was validated against disk diffusion and applied to determine resistance against 11 antibiotics in sewage isolates. Resistance data on E. coli isolated from clinical samples from corresponding local hospital and primary care patients were collected during the same year and compared with those of the sewage isolates by linear regression.ResultsE. coli resistance rates derived from hospital sewage and hospital patients strongly correlated (r2 = 0.95 for urine and 0.89 for blood samples), as did resistance rates in E. coli from municipal sewage and primary care urine samples (r2 = 0.82). Resistance rates in hospital sewage isolates were close to those in hospital clinical isolates while resistance rates in municipal sewage isolates were about half of those measured in primary care isolates. Resistance rates in municipal sewage isolates were more stable between sampling occasions than those from hospital sewage.ConclusionOur findings provide support for development of a low-cost, sewage-based surveillance system for antibiotic resistance in E. coli, which could complement current monitoring systems and provide clinically relevant antibiotic resistance data for countries and regions where surveillance is lacking.

Monitoring antimicrobial resistance trends in commensal Escherichia coli from livestock, the Netherlands, 1998 to 2016

BackgroundMonitoring of antimicrobial resistance (AMR) in animals is essential for public health surveillance. To enhance interpretation of monitoring data, evaluation and optimisation of AMR trend analysis is needed.AimsTo quantify and evaluate trends in AMR in commensal Escherichia coli, using data from the Dutch national AMR monitoring programme in livestock (1998-2016).MethodsFaecal samples were collected at slaughter from broilers, pigs and veal calves. Minimum inhibitory concentration values were obtained by broth microdilution for E. coli for 15 antimicrobials of eight antimicrobial classes. A Poisson regression model was applied to resistant isolate counts, with explanatory variables representing time before and after 2009 (reference year); for veal calves, sampling changed from 2012 represented by an extra explanatory variable.ResultsResistant counts increased significantly from 1998-2009 in broilers and pigs, except for tetracyclines and sulfamethoxazole in broilers and chloramphenicol and aminoglycosides in pigs. Since 2009, resistant counts decreased for all antimicrobials in broilers and for all but the phenicols in pigs. In veal calves, for most antimicrobials no significant decrease in resistant counts could be determined for 2009-16, except for sulfamethoxazole and nalidixic acid. Within animal species, antimicrobial-specific trends were similar.ConclusionsUsing Dutch monitoring data from 1998-2016, this study quantified AMR trends in broilers and slaughter pigs and showed significant trend changes in the reference year 2009. We showed that monitoring in commensal E. coli is useful to quantify trends and detect trend changes in AMR. This model is applicable to similar data from other European countries.

The antimicrobial resistome in relation to antimicrobial use and biosecurity in pig farming, a metagenome-wide association study in nine European countries

OBJECTIVES

Previous studies in food-producing animals have shown associations between antimicrobial use (AMU) and resistance (AMR) in specifically isolated bacterial species. Multi-country data are scarce and only describe between-country differences. Here we investigate associations between the pig faecal mobile resistome and characteristics at the farm-level across Europe.

METHODS

A cross-sectional study was conducted among 176 conventional pig farms from nine European countries. Twenty-five faecal samples from fattening pigs were pooled per farm and acquired resistomes were determined using shotgun metagenomics and the Resfinder reference database, i.e. the full collection of horizontally acquired AMR genes (ARGs). Normalized fragments resistance genes per kilobase reference per million bacterial fragments (FPKM) were calculated. Specific farm-level data (AMU, biosecurity) were collected. Random-effects meta-analyses were performed by country, relating farm-level data to relative ARG abundances (FPKM).

RESULTS

Total AMU during fattening was positively associated with total ARG (total FPKM). Positive associations were particularly observed between widely used macrolides and tetracyclines, and ARGs corresponding to the respective antimicrobial classes. Significant AMU-ARG associations were not found for β-lactams and only few colistin ARGs were found, despite high use of these antimicrobial classes in younger pigs. Increased internal biosecurity was directly related to higher abundances of ARGs mainly encoding macrolide resistance. These effects of biosecurity were independent of AMU in mutually adjusted models.

CONCLUSIONS

Using resistome data in association studies is unprecedented and adds accuracy and new insights to previously observed AMU-AMR associations. Major components of the pig resistome are positively and independently associated with on-farm AMU and biosecurity conditions.

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

MOTIVATION

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

RESULTS

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

AVAILABILITY AND IMPLEMENTATION

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

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrated metagenomic and metatranscriptomic profiling reveals differentially expressed resistomes in human, chicken, and pig gut microbiomes

Gut microbiota is a reservoir of antibiotic resistance genes (ARGs). Yet, limited information is available regarding the presence (metagenomic DNA level) and expression profiles (metatranscriptomic RNA level) of ARGs in gut microbiota. Here, we used both metagenomic and metatranscriptomic approaches to comprehensively reveal the abundance, diversity, and expression of ARGs in human, chicken, and pig gut microbiomes in China. Based on deep sequencing data and ARG databases, a total of 330 ARGs associated with 21 antibiotic classes were identified in 18 human, chicken, and pig fecal samples. Metatranscriptomic analysis revealed that 49.4, 66.5, and 56.6% of ARGs identified in human, chicken, and pig gut microbiota, respectively, were expressed, indicating that a large proportion of ARGs were not transcriptionally active. Further analysis demonstrated that transcript abundance of tetracycline, aminoglycoside, and beta-lactam resistance genes was mainly contributed by acquired ARGs. We also found that various biocide, chemical, and metal resistance genes were actively transcribed in human and animal guts. The combination of metagenomic and metatranscriptomic analysis in this study allowed us to specifically link ARGs to their transcripts, providing a comprehensive view of the prevalence and expression of ARGs in gut microbiota. Taken together, these data deepen our understanding of the distribution, evolution, and dissemination of ARGs and metal resistance genes in human, chicken, and pig gut microbiota.

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.

Antibiotic resistance: bioinformatics-based understanding as a functional strategy for drug design

The use of antibiotics to manage infectious diseases dates back to ancient civilization, but the lack of a clear distinction between the therapeutic and toxic dose has been a major challenge. This precipitates the notion that antibiotic resistance was from time immemorial, principally because of a lack of adequate knowledge of therapeutic doses and continuous exposure of these bacteria to suboptimal plasma concentration of antibiotics. With the discovery of penicillin by Alexander Fleming in 1924, a milestone in bacterial infections' treatment was achieved. This forms the foundation for the modern era of antibiotic drugs. Antibiotics such as penicillins, cephalosporins, quinolones, tetracycline, macrolides, sulphonamides, aminoglycosides and glycopeptides are the mainstay in managing severe bacterial infections, but resistant strains of bacteria have emerged and hampered the progress of research in this field. Recently, new approaches to research involving bacteria resistance to antibiotics have appeared; these involve combining the molecular understanding of bacteria systems with the knowledge of bioinformatics. Consequently, many molecules have been developed to curb resistance associated with different bacterial infections. However, because of increased emphasis on the clinical relevance of antibiotics, the synergy between in silico study and in vivo study is well cemented and this facilitates the discovery of potent antibiotics. In this review, we seek to give an overview of earlier reviews and molecular and structural understanding of bacteria resistance to antibiotics, while focusing on the recent bioinformatics approach to antibacterial drug discovery.

Understanding the evolution of antibiotic resistance at the molecular level as a functional tool for bioinformatic-based drug design.

Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms

This Opinion considers the application of whole genome sequencing (WGS) and metagenomics for outbreak investigation, source attribution and risk assessment of food‐borne pathogens. WGS offers the highest level of bacterial strain discrimination for food‐borne outbreak investigation and source‐attribution as well as potential for more precise hazard identification, thereby facilitating more targeted risk assessment and risk management. WGS improves linking of sporadic cases associated with different food products and geographical regions to a point source outbreak and can facilitate epidemiological investigations, allowing also the use of previously sequenced genomes. Source attribution may be favoured by improved identification of transmission pathways, through the integration of spatial‐temporal factors and the detection of multidirectional transmission and pathogen–host interactions. Metagenomics has potential, especially in relation to the detection and characterisation of non‐culturable, difficult‐to‐culture or slow‐growing microorganisms, for tracking of hazard‐related genetic determinants and the dynamic evaluation of the composition and functionality of complex microbial communities. A SWOT analysis is provided on the use of WGS and metagenomics for Salmonella and Shigatoxin‐producing Escherichia coli (STEC) serotyping and the identification of antimicrobial resistance determinants in bacteria. Close agreement between phenotypic and WGS‐based genotyping data has been observed. WGS provides additional information on the nature and localisation of antimicrobial resistance determinants and on their dissemination potential by horizontal gene transfer, as well as on genes relating to virulence and biological fitness. Interoperable data will play a major role in the future use of WGS and metagenomic data. Capacity building based on harmonised, quality controlled operational systems within European laboratories and worldwide is essential for the investigation of cross‐border outbreaks and for the development of international standardised risk assessments of food‐borne microorganisms.

Effect of tetracycline treatment regimens on antibiotic resistance gene selection over time in nursery pigs

Background

The majority of antimicrobials given during the production of pigs are given to nursery pigs. The influence of antimicrobial use on the levels of antimicrobial resistant (AMR) genes is important to quantify to be able to assess the impact of resistance on the food chain and risk to human and animal health.

Results

This study investigated the response on the levels of nine AMR genes to five different treatment strategies with oxytetracycline, and the dynamics of gene abundance over time by following 1167 pigs from five different farms in Denmark.

The results showed no significant difference between treatments and an increase in abundance for the efflux pump encoding tet(A) gene and the genes encoding the ribosomal protection proteins tet(O) and tet(W) tetracycline resistant genes following treatment, while tet(M) showed no response to treatment. However, it was also observed that the levels of tet(O), tet(W), and ermB in some farms would drift more over time compared to a single treatment-course with antibiotic.

Conclusion

This study underlines the large variation in AMR levels under natural conditions and the need for increased investigation of the complex interactions of antimicrobial treatment and other environmental and managerial practices in swine production on AMR gene abundance.

Mathematical modelling for antibiotic resistance control policy: do we know enough?

BACKGROUND

Antibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mindful of limiting the spread of resistance and safeguarding the efficacy of antibiotics for current and future generations. Policies that strike the right balance must be informed by a transparent rationale that relies on a robust evidence base.

MAIN TEXT

One way to generate the evidence base needed to inform policies for managing antibiotic resistance is by using mathematical models. These models can distil the key drivers of the dynamics of resistance transmission from complex infection and evolutionary processes, as well as predict likely responses to policy change in silico. Here, we ask whether we know enough about antibiotic resistance for mathematical modelling to robustly and effectively inform policy. We consider in turn the challenges associated with capturing antibiotic resistance evolution using mathematical models, and with translating mathematical modelling evidence into policy.

CONCLUSIONS

We suggest that in spite of promising advances, we lack a complete understanding of key principles. From this we advocate for priority areas of future empirical and theoretical research.