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

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

Phage predation, disease severity and pathogen genetic diversity in cholera patients

Despite an increasingly detailed picture of the molecular mechanisms of phage-bacterial interactions, we lack an understanding of how these interactions evolve and impact disease within patients. Here we report a year-long, nation-wide study of diarrheal disease patients in Bangladesh. Among cholera patients, we quantified Vibrio cholerae (prey) and its virulent phages (predators) using metagenomics and quantitative PCR, while accounting for antibiotic exposure using quantitative mass spectrometry. Virulent phage (ICP1) and antibiotics suppressed V. cholerae to varying degrees and were inversely associated with severe dehydration depending on resistance mechanisms. In the absence of anti-phage defenses, predation was 'effective,' with a high predator:prey ratio that correlated with increased genetic diversity among the prey. In the presence of anti-phage defenses, predation was 'ineffective,' with a lower predator:prey ratio that correlated with increased genetic diversity among the predators. Phage-bacteria coevolution within patients should therefore be considered in the deployment of phage-based therapies and diagnostics.

Genome-centric analyses of 165 metagenomes show that mobile genetic elements are crucial for the transmission of antimicrobial resistance genes to pathogens in activated sludge and wastewater

Wastewater is considered a reservoir of antimicrobial resistance genes (ARGs), where the abundant antimicrobial-resistant bacteria and mobile genetic elements facilitate horizontal gene transfer. However, the prevalence and extent of these phenomena in different taxonomic groups that inhabit wastewater are still not fully understood. Here, we determined the presence of ARGs in metagenome-assembled genomes (MAGs) and evaluated the risks of MAG-carrying ARGs in potential human pathogens. The potential of these ARGs to be transmitted horizontally or vertically was also determined. A total of 5,916 MAGs (completeness >50%, contamination <10%) were recovered, covering 68 phyla and 279 genera. MAGs were dereplicated into 1,204 genome operational taxonomic units (gOTUs) as a proxy for species ( average nucleotide identity >0.95). The dominant ARG classes detected were bacitracin, multi-drug, macrolide-lincosamide-streptogramin (MLS), glycopeptide, and aminoglycoside, and 10.26% of them were located on plasmids. The main hosts of ARGs belonged to Escherichia, Klebsiella, Acinetobacter, Gresbergeria, Mycobacterium, and Thauera. Our data showed that 253 MAGs carried virulence factor genes (VFGs) divided into 44 gOTUs, of which 45 MAGs were carriers of ARGs, indicating that potential human pathogens carried ARGs. Alarmingly, the MAG assigned as Escherichia coli contained 159 VFGs, of which 95 were located on chromosomes and 10 on plasmids. In addition to shedding light on the prevalence of ARGs in individual genomes recovered from activated sludge and wastewater, our study demonstrates a workflow that can identify antimicrobial-resistant pathogens in complex microbial communities.

IMPORTANCE

Antimicrobial resistance (AMR) threatens the health of humans, animals, and natural ecosystems. In our study, an analysis of 165 metagenomes from wastewater revealed antibiotic-targeted alteration, efflux, and inactivation as the most prevalent AMR mechanisms. We identified several genera correlated with multiple ARGs, including Klebsiella, Escherichia, Acinetobacter, Nitrospira, Ottowia, Pseudomonas, and Thauera, which could have significant implications for AMR transmission. The abundance of bacA, mexL, and aph(3")-I in the genomes calls for their urgent management in wastewater. Our approach could be applied to different ecosystems to assess the risk of potential pathogens containing ARGs. Our findings highlight the importance of managing AMR in wastewater and can help design measures to reduce the transmission and evolution of AMR in these systems.

Horizontal transfer potential of antibiotic resistance genes in wastewater treatment plants unraveled by microfluidic-based mini-metagenomics

Wastewater treatment plants (WWTPs) are known to harbor antibiotic resistance genes (ARGs), which can potentially spread to the environment and human populations. However, the extent and mechanisms of ARG transfer in WWTPs are not well understood due to the high microbial diversity and limitations of molecular techniques. In this study, we used a microfluidic-based mini-metagenomics approach to investigate the transfer potential and mechanisms of ARGs in activated sludge from WWTPs. Our results show that while diverse ARGs are present in activated sludge, only a few highly similar ARGs are observed across different taxa, indicating limited transfer potential. We identified two ARGs, ermF and tla-1, which occur in a variety of bacterial taxa and may have high transfer potential facilitated by mobile genetic elements. Interestingly, genes that are highly similar to the sequences of these two ARGs, as identified in this study, display varying patterns of abundance across geographic regions. Genes similar to ermF found are widely found in Asia and the Americas, while genes resembling tla-1 are primarily detected in Asia. Genes similar to both genes are barely detected in European WWTPs. These findings shed light on the limited horizontal transfer potential of ARGs in WWTPs and highlight the importance of monitoring specific ARGs in different regions to mitigate the spread of antibiotic resistance.

CPR bacteria and DPANN archaea play pivotal roles in response of microbial community to antibiotic stress in groundwater

The accumulation of antibiotics in the natural environment can disrupt microbial population dynamics. However, our understanding of how microbial communities adapt to the antibiotic stress in groundwater ecosystems remains limited. By recovering 2675 metagenome-assembled genomes (MAGs) from 66 groundwater samples, we explored the effect of antibiotics on bacterial, archaeal, and fungal communities, and revealed the pivotal microbes and their mechanisms in coping with antibiotic stress. The results indicated that antibiotics had the most significant influence on bacterial and archaeal communities, while the impact on the fungal community was minimal. Analysis of co-occurrence networks between antibiotics and microbes revealed the critical roles of Candidate Phyla Radiation (CPR) bacteria and DPANN archaea, two representative microbial groups in groundwater ecosystem, in coping with antibiotic resistance and enhancing network connectivity and complexity. Further genomic analysis demonstrated that CPR bacteria carried approximately 6 % of the identified antibiotic resistance genes (ARGs), indicating their potential to withstand antibiotics on their own. Meanwhile, the genomes of CPR bacteria and DPANN archaea were found to encode diverse biosynthetic gene clusters (BGCs) responsible for producing antimicrobial metabolites, which could not only assist CPR and DPANN organisms but also benefit the surrounding microbes in combating antibiotic stress. These findings underscore the significant impact of antibiotics on prokaryotic microbial communities in groundwater, and highlight the importance of CPR bacteria and DPANN archaea in enhancing the overall resilience and functionality of the microbial community in the face of antibiotic stress.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

Heterotrophic bacteria in drinking water: evaluating antibiotic resistance and the presence of virulence genes

Heterotrophic bacteria, impacting those with infections or compromised immunity, pose heightened health risks when resistant to antibiotics. This study investigates heterotrophic plate count bacteria in water from North West-C (NWC) and North West-G (NWG) facilities, revealing prevalent β-hemolysis (NWC 82.5%, NWG 86.7%), enzyme production (98%), and antibiotic resistance, especially in NWC. NWG exhibits variations in hemolysin (P = 0.013), lipase (P = 0.009), and DNase activity (P = 0.006). Antibiotics, including ciprofloxacin, persist throughout treatment, with high resistance to β-lactams and trimethoprim (47%-100%), predominantly in NWC. Multiple antibiotic resistance index indicates that 90% of values exceed 0.20, signifying isolates from high antibiotic usage sources. Whole genome sequencing reveals diverse antibiotic resistance genes in heterotrophic strains, emphasizing their prevalence and health risks in water.IMPORTANCEThis study's findings are a stark reminder of a significant health concern: our water sources harbor antibiotic-resistant heterotrophic bacteria, which can potentially cause illness, especially in individuals with weakened immune systems or underlying infections. Antibiotic resistance among these bacteria is deeply concerning, as it threatens the effectiveness of antibiotics, critical for treating various infections. Moreover, detecting virulence factors in a notable proportion of these bacteria highlights their elevated risk to public health. This research underscores the immediate need for enhanced water treatment processes, rigorous water quality monitoring, and the development of strategies to combat antibiotic resistance in the environment. Safeguarding the safety of our drinking water is imperative to protect public health and mitigate the spread of antibiotic-resistant infections, making these findings a compelling call to action for policymakers and public health authorities alike.

Microplastics and chemical leachates from plastic pipes are associated with increased virulence and antimicrobial resistance potential of drinking water microbial communities

There is increasing recognition of the potential impacts of microplastics (MPs) on human health. As drinking water is the most direct route of human exposure to MPs, there is an urgent need to elucidate MPs source and fate in drinking water distribution system (DWDS). Here, we showed polypropylene random plastic pipes exposed to different water quality (chlorination and heating) and environmental (freeze-thaw) conditions accelerated MPs generation and chemical leaching. MPs showed various morphology and aggregation states, and chemical leaches exhibited distinct profiles due to different physicochemical treatments. Based on the physiological toxicity of leachates, oxidative stress level was negatively correlated with disinfection by-products in the leachates. Microbial network analysis demonstrated exposure to leachates (under three treatments) undermined microbial community stability and increased the relative abundance and dominance of pathogenic bacteria. Leachate physical and chemical properties (i.e., MPs abundance, hydrodynamic diameter, zeta potential, total organic carbon, dissolved ECs) exerted significant (p < 0.05) effects on the functional genes related to virulence, antibiotic resistance and metabolic pathways. Notably, chlorination significantly increased correlations among pathogenic bacteria, virulence genes, and antibiotic resistance genes. Overall, this study advances the understanding of direct and indirect risks of these MPs released from plastic pipes in the DWDS.

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

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

Exploring the effects of antimicrobial treatment on the gut and oral microbiomes and resistomes from elderly long-term care facility residents via shotgun DNA sequencing

Monitoring antibiotic-resistant bacteria (ARB) and understanding the effects of antimicrobial drugs on the human microbiome and resistome are crucial for public health. However, no study has investigated the association between antimicrobial treatment and the microbiome-resistome relationship in long-term care facilities, where residents act as reservoirs of ARB but are not included in the national surveillance for ARB. We conducted shotgun metagenome sequencing of oral and stool samples from long-term care facility residents and explored the effects of antimicrobial treatment on the human microbiome and resistome using two types of comparisons: cross-sectional comparisons based on antimicrobial treatment history in the past 6 months and within-subject comparisons between stool samples before, during and 2-4 weeks after treatment using a single antimicrobial drug. Cross-sectional analysis revealed two characteristics in the group with a history of antimicrobial treatment: the archaeon Methanobrevibacter was the only taxon that significantly increased in abundance, and the total abundance of antimicrobial resistance genes (ARGs) was also significantly higher. Within-subject comparisons showed that taxonomic diversity did not decrease during treatment, suggesting that the effect of the prescription of a single antimicrobial drug in usual clinical treatment on the gut microbiota is likely to be smaller than previously thought, even among very elderly people. Additional analysis of the detection limit of ARGs revealed that they could not be detected when contig coverage was <2.0. This study is the first to report the effects of usual antimicrobial treatments on the microbiome and resistome of long-term care facility residents.

Assembly, stability, and dynamics of the infant gut microbiome are linked to bacterial strains and functions in mother's milk

The establishment of the gut microbiome in early life is critical for healthy infant development. Although human milk is recommended as the sole source of nutrition for the human infant, little is known about how variation in milk composition, and especially the milk microbiome, shapes the microbial communities in the infant gut. Here, we quantified the similarity between the maternal milk and the infant gut microbiome using 507 metagenomic samples collected from 195 mother-infant pairs at one, three, and six months postpartum. We found that the microbial taxonomic overlap between milk and the infant gut was driven by bifidobacteria, in particular by B. longum. Infant stool samples dominated by B. longum also showed higher temporal stability compared to samples dominated by other species. We identified two instances of strain sharing between maternal milk and the infant gut, one involving a commensal (B. longum) and one a pathobiont (K. pneumoniae). In addition, strain sharing between unrelated infants was higher among infants born at the same hospital compared to infants born in different hospitals, suggesting a potential role of the hospital environment in shaping the infant gut microbiome composition. The infant gut microbiome at one month compared to six months of age was enriched in metabolic pathways associated with de-novo molecule biosynthesis, suggesting that early colonisers might be more versatile and metabolically independent compared to later colonizers. Lastly, we found a significant overlap in antimicrobial resistance genes carriage between the mother's milk and their infant's gut microbiome. Taken together, our results suggest that the human milk microbiome has an important role in the assembly, composition, and stability of the infant gut microbiome.

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

Blanket antimicrobial resistance gene database with structural information, BOARDS, provides insights on historical landscape of resistance prevalence and effects of mutations in enzyme structure

Antimicrobial resistance (AMR) in pathogenic bacteria poses a significant threat to public health, yet there is still a need for development in the tools to deeply understand AMR genes based on genetic or structural information. In this study, we present an interactive web database named Blanket Overarching Antimicrobial-Resistance gene Database with Structural information (BOARDS, sbml.unist.ac.kr), a database that comprehensively includes 3,943 reported AMR gene information for 1,997 extended spectrum beta-lactamase (ESBL) and 1,946 other genes as well as a total of 27,395 predicted protein structures. These structures, which include both wild-type AMR genes and their mutants, were derived from 80,094 publicly available whole-genome sequences. In addition, we developed the rapid analysis and detection tool of antimicrobial-resistance (RADAR), a one-stop analysis pipeline to detect AMR genes across whole-genome sequencing (WGSs). By integrating BOARDS and RADAR, the AMR prevalence landscape for eight multi-drug resistant pathogens was reconstructed, leading to unexpected findings such as the pre-existence of the MCR genes before their official reports. Enzymatic structure prediction-based analysis revealed that the occurrence of mutations found in some ESBL genes was found to be closely related to the binding affinities with their antibiotic substrates. Overall, BOARDS can play a significant role in performing in-depth analysis on AMR.IMPORTANCEWhile the increasing antibiotic resistance (AMR) in pathogen has been a burden on public health, effective tools for deep understanding of AMR based on genetic or structural information remain limited. In this study, a blanket overarching antimicrobial-resistance gene database with structure information (BOARDS)-a web-based database that comprehensively collected AMR gene data with predictive protein structural information was constructed. Additionally, we report the development of a RADAR pipeline that can analyze whole-genome sequences as well. BOARDS, which includes sequence and structural information, has shown the historical landscape and prevalence of the AMR genes and can provide insight into single-nucleotide polymorphism effects on antibiotic degrading enzymes within protein structures.

Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools

BACKGROUND

Although the spread of antimicrobial resistance (AMR) through food and its production poses a significant concern, there is limited research on the prevalence of AMR bacteria in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance targets within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches.

RESULTS

We simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Kraken2/Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2/Bracken indicated presence of organisms not included in the synthetic metagenomes. Metaphlan3/Metaphlan4 analysis of community composition was more specific but with lower sensitivity than the Kraken2/Bracken analysis. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 (which allows read to map to multiple targets) falsely reported presence of distantly related ARGs at all isolate genome coverage levels. The presence of background microbiota in metagenomes influenced the accuracy of ARG detection by KMA, resulting in mcr-1 detection at 0.1X isolate coverage in the lettuce but not in the beef metagenome.

CONCLUSIONS

This study demonstrates accurate detection of ARGs in synthetic metagenomes using various bioinformatic methods, provided that reads from the ARG-encoding organism exceed approximately 5X isolate coverage (i.e. 0.4% of a 40 million read metagenome). While lowering thresholds for target gene detection improved sensitivity, this led to the identification of alternative ARG-alleles, potentially confounding the identification of critical ARGs in the resistome. Further advancements in sequencing technologies providing increased coverage depth or extended read lengths may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking clinically important ARGs in agri-food samples.

Chicken feedlot revisited: Co-dispersal of antibiotic and metal resistome under banning in-feed veterinary antibiotics

Intensive livestock farming has been implicated as a notorious hotspot for antibiotic resistance genes (ARGs) due to the excessive or inappropriate use of in-feed antibiotics over the past few decades. Since China implemented a ban on the use of antibiotics in animal feed since 2020, the dissemination of ARGs in the vicinity of feedlots has remained unclear. This study presents a case study that aims to investigate the dispersal of antibiotics and ARGs from a chicken feedlot (established in 2020) to the adjacent aquatic and soil environments. Comparing the sample collected from upstream area, the water and sediment samples from midstream and downstream areas showed an increase in total antibiotic residues and metal content (Cu and Zn) by 4.2-5.3 fold and 1.3-22.6 fold, respectively. The downstream water samples exhibited a 2.49-2.93-fold increase in the abundance of ARGs and a 1.48-1.75-fold increase in the abundance of metal resistance genes (MRGs). The results of Pearson correlation and metagenome-assembled genome revealed a tendency for the co-occurrence of ARGs and MRGs. The dissemination of ARGs and MRGs is primarily driven by tetracycline, tylosin, Cu, and, Mn, with mobile genetic elements playing a more significant role than bacterial communities. These findings shed light on the overlooked co-dispersal pattern of ARGs and MRGs in the environment surrounding feedlots, particularly in the context of banning in-feed veterinary antibiotics.

Fresh produce as a reservoir of antimicrobial resistance genes: A case study of Switzerland

Antimicrobial resistance (AMR) can be transferred to humans through food and fresh produce can be an ideal vector as it is often consumed raw or minimally processed. The production environment of fresh produce and the agricultural practices and regulations can vary substantially worldwide, and consequently, the contamination sources of AMR. In this study, 75 imported and 75 non-imported fresh produce samples purchased from Swiss retailers were tested for the presence of antimicrobial resistant bacteria (ARB) and antimicrobial resistance genes (ARGs). Moreover, the plasmidome of 4 selected samples was sequenced to have an insight on the diversity of mobile resistome. In total, 91 ARB were isolated from fresh produce, mainly cephalosporin-resistant Enterobacterales (n = 64) and carbapenem-resistant P. aeruginosa (n = 13). All P. aeruginosa, as well as 16 Enterobacterales' isolates were multidrug-resistant. No differences between imported and Swiss fresh produce were found regarding the number of ARB. In 95 % of samples at least one ARG was detected, being the most frequent sul1, blaTEM, and ermB. Abundance of sul1 and intI1 correlated strongly with the total amount of ARGs, suggesting they could be good indicators for AMR in fresh produce. Furthermore, sul1 correlated with the fecal marker yccT, indicating that fecal contamination could be one of the sources of AMR. The gene sulI was significantly higher in most imported samples, suggesting higher anthropogenic contamination in the food production chain of imported produce. The analyses of the plasmidome of coriander and carrot samples revealed the presence of several ARGs as well as genes conferring resistance to antiseptics and disinfectants in mobile genetic elements. Overall, this study demonstrated that fresh produce contributes to the dissemination of ARGs and ARB.

SPIRE: a Searchable, Planetary-scale mIcrobiome REsource

Meta'omic data on microbial diversity and function accrue exponentially in public repositories, but derived information is often siloed according to data type, study or sampled microbial environment. Here we present SPIRE, a Searchable Planetary-scale mIcrobiome REsource that integrates various consistently processed metagenome-derived microbial data modalities across habitats, geography and phylogeny. SPIRE encompasses 99 146 metagenomic samples from 739 studies covering a wide array of microbial environments and augmented with manually-curated contextual data. Across a total metagenomic assembly of 16 Tbp, SPIRE comprises 35 billion predicted protein sequences and 1.16 million newly constructed metagenome-assembled genomes (MAGs) of medium or high quality. Beyond mapping to the high-quality genome reference provided by proGenomes3 (http://progenomes.embl.de), these novel MAGs form 92 134 novel species-level clusters, the majority of which are unclassified at species level using current tools. SPIRE enables taxonomic profiling of these species clusters via an updated, custom mOTUs database (https://motu-tool.org/) and includes several layers of functional annotation, as well as crosslinks to several (micro-)biological databases. The resource is accessible, searchable and browsable via http://spire.embl.de.

In-silico analysis of probiotic attributes and safety assessment of probiotic strain Bacillus coagulans BCP92 for human application

The whole genome sequence (WGS) of Bacillus coagulans BCP92 is reported along with its genomic analysis of probiotics and safety features. The identification of bacterial strain was carried out using the 16S rDNA sequencing method. Furthermore, gene-related probiotic features, safety assessment (by in vitro and in silico), and genome stability were also studied using the WGS analysis for the possible use of the bacterial strain as a probiotic. From the BLAST analysis, bacterial strain was identified as Bacillus (Heyndrickxia) coagulans. WGS analysis indicated that the genome consists of a 3 475 658 bp and a GC-content of 46.35%. Genome mining of BCP92 revealed that the strain is consist of coding sequences for d-lactate dehydrogenase and l-lactate dehydrogenases, 36 genes involved in fermentation activities, 29 stress-responsive as well as many adhesions related genes. The genome, also possessing genes, is encoded for the synthesis of novel circular bacteriocin. Using an in-silico approach for the bacterial genome study, it was possible to determine that the Bacillus (Heyndrickxia) coagulans strain BCP92 contains genes that are encoded for the probiotic abilities and did not harbour genes that are risk associated, thus confirming the strain's safety and suitability as a probiotic to be used for human application.

Premise Plumbing Pipe Materials and In-Building Disinfectants Shape the Potential for Proliferation of Pathogens and Antibiotic Resistance Genes

In-building disinfectants are commonly applied to control the growth of pathogens in plumbing, particularly in facilities such as hospitals that house vulnerable populations. However, their application has not been well optimized, especially with respect to interactive effects with pipe materials and potential unintended effects, such as enrichment of antibiotic resistance genes (ARGs) across the microbial community. Here, we used triplicate convectively mixed pipe reactors consisting of three pipe materials (PVC, copper, and iron) for replicated simulation of the distal reaches of premise plumbing and evaluated the effects of incrementally increased doses of chlorine, chloramine, chlorine dioxide, and copper-silver disinfectants. We used shotgun metagenomic sequencing to characterize the resulting succession of the corresponding microbiomes over the course of 37 weeks. We found that both disinfectants and pipe material affected ARG and microbial community taxonomic composition both independently and interactively. Water quality and total bacterial numbers were not found to be predictive of pathogenic species markers. One result of particular concern was the tendency of disinfectants, especially monochloramine, to enrich ARGs. Metagenome assembly indicated that many ARGs were enriched specifically among the pathogenic species. Functional gene analysis was indicative of a response of the microbes to oxidative stress, which is known to co/cross-select for antibiotic resistance. These findings emphasize the need for a holistic evaluation of pathogen control strategies for plumbing.

What samples are suitable for monitoring antimicrobial-resistant genes? Using NGS technology, a comparison between eDNA and mrDNA analysis from environmental water

Introduction

The rise in antimicrobial resistance (AMR) that is affecting humans, animals, and the environment, compromises the human immune system and represents a significant threat to public health. Regarding the impact on water sanitation, the risk that antimicrobial-resistant genes (ARGs) and antimicrobial-resistant bacteria in surface water in cities pose to human health remains unclear. To determine the prevalence of AMR in environmental surface water in Japan, we used DNA sequencing techniques on environmental water DNA (eDNA) and the DNA of multidrug-resistant bacteria (mrDNA).

Methods

The eDNA was extracted from four surface water samples obtained from the Tokyo area and subjected to high- throughput next-generation DNA sequencing using Illumina-derived shotgun metagenome analysis. The sequence data were analyzed using the AmrPlusPlus pipeline and the MEGARes database. Multidrug-resistant bacteria were isolated using a culture-based method from water samples and were screened by antimicrobial susceptibility testing (for tetracycline, ampicillin-sulbactam, amikacin, levofloxacin, imipenem, and clarithromycin). Of the 284 isolates, 22 were identified as multidrug-resistant bacteria. The mrDNA was sequenced using the Oxford nanopore MinION system and analyzed by NanoARG, a web service for detecting and contextualizing ARGs.

Results and discussion

The results from eDNA and mrDNA revealed that ARGs encoding beta-lactams and multidrug resistance, including multidrug efflux pump genes, were frequently detected in surface water samples. However, mrDNA also revealed many sequence reads from multidrug-resistant bacteria, as well as nonspecific ARGs, whereas eDNA revealed specific ARGs such as pathogenic OXA-type and New Delhi metallo (NDM)-beta-lactamase ARGs.

Conclusion

To estimate potential AMR pollution, our findings suggested that eDNA is preferable for detecting pathogen ARGs.

Pathogenic Bacteria Are the Primary Determinants Shaping PM2.5-Borne Resistomes in the Municipal Food Waste Treatment System

Bioaerosol pollution poses a substantial threat to human health during municipal food waste (FW) recycling. However, bioaerosol-borne antibiotic-resistant genes (ARGs) have received little attention. Herein, 48 metagenomic data were applied to study the prevalence of PM2.5-borne ARGs in and around full-scale food waste treatment plants (FWTPs). Overall, FWTP PM2.5 (2.82 ± 1.47 copies/16S rRNA gene) harbored comparable total abundance of ARGs to that of municipal wastewater treatment plant PM2.5 (WWTP), but was significantly enriched with the multidrug type (e.g., AdeC/I/J; p < 0.05), especially the abundant multidrug ARGs could serve as effective indicators to define resistome profiles of FWTPs (Random Forest accuracy >92%). FWTP PM2.5 exhibited a decreasing enrichment of total ARGs along the FWTP-downwind-boundary gradient, eventually reaching levels comparable to urban PM2.5 (1.46 ± 0.21 copies/16S rRNA gene, N = 12). The combined analysis of source-tracking, metagenome-assembled genomes (MAGs), and culture-based testing provides strong evidence that Acinetobacter johnsonii-dominated pathogens contributed significantly to shaping and disseminating multidrug ARGs, while abiotic factors (i.e., SO42-) indirectly participated in these processes, which deserves more attention in developing strategies to mitigate airborne ARGs. In addition, the exposure level of FWTP PM2.5-borne resistant pathogens was about 5-11 times higher than those in urban PM2.5, and could be more severe than hospital PM2.5 in certain scenarios (<41.53%). This work highlights the importance of FWTP in disseminating airborne multidrug ARGs and the need for re-evaluating the air pollution induced by municipal FWTP in public health terms.

Key taxa and mobilome-mediated responses co-reshape the soil antibiotic resistome under dazomet fumigation stress

Agrochemicals are emergingly being implicated in the widespread dissemination of antibiotic resistance genes (ARGs) in agroecosystems. However, minimal research exists on the disturbance of fumigant on soil ARGs. Focusing on a typical fumigant dazomet in a simulated soil microcosm, we characterized the dazomet-triggered timely response and longstanding dynamic of ARGs at one-fold and two-fold field recommended doses using metagenome and quantitative PCR. Dazomet treatments reduced 13.17%-69.98% of absolute abundance of 16S rRNA gene and targeted ARGs, but, awfully, boosted diversity and relative abundance of ARGs up to 1.33-1.60 and 1.62-1.90 folds, respectively. Approximately 77.28% of changes in relative abundance of ARGs could be explained by bacterial community and mobile genetic elements (MGEs). Mechanistically, primary hosts of ARGs shifted from Proteobacteria (control) to Firmicutes and Actinobacteria (treatments) accompanied with corresponding changes in their abundance by combining community analysis, host tracking analysis and antibiotic resistant bacteria assay. Meanwhile, dazomet exposure significantly increased the incidence of MGEs and stimulated the conjugation of antibiotic-resistant plasmid. In addition, absolute abundance of targeted ARGs gradually recovered in the post-fumigation stage. Collectively, our results elucidate the dazomet-triggered emergence and spread of soil ARGs and highlight the importance of navigating toward rational use of fumigant in agricultural fields.

Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information

MOTIVATION

Large-scale clinical proteomics datasets of infectious pathogens, combined with antimicrobial resistance outcomes, have recently opened the door for machine learning models which aim to improve clinical treatment by predicting resistance early. However, existing prediction frameworks typically train a separate model for each antimicrobial and species in order to predict a pathogen's resistance outcome, resulting in missed opportunities for chemical knowledge transfer and generalizability.

RESULTS

We demonstrate the effectiveness of multimodal learning over proteomic and chemical features by exploring two clinically relevant tasks for our proposed deep learning models: drug recommendation and generalized resistance prediction. By adopting this multi-view representation of the pathogenic samples and leveraging the scale of the available datasets, our models outperformed the previous single-drug and single-species predictive models by statistically significant margins. We extensively validated the multi-drug setting, highlighting the challenges in generalizing beyond the training data distribution, and quantitatively demonstrate how suitable representations of antimicrobial drugs constitute a crucial tool in the development of clinically relevant predictive models.

AVAILABILITY AND IMPLEMENTATION

The code used to produce the results presented in this article is available at https://github.com/BorgwardtLab/MultimodalAMR.

Antibiotic resistance genes prevalence prediction and interpretation in beaches affected by urban wastewater discharge

Background

The annual death toll of over 1.2 million worldwide is attributed to infections caused by resistant bacteria, driven by the significant impact of antibiotic misuse and overuse in spreading these bacteria and their associated antibiotic resistance genes (ARGs). While limited data suggest the presence of ARGs in beach environments, efficient prediction tools are needed for monitoring and detecting ARGs to ensure public health safety. This study aims to develop interpretable machine learning methods for predicting ARGs in beach waters, addressing the challenge of black-box models and enhancing our understanding of their internal mechanisms.

Methods

In this study, we systematically collected beach water samples and subsequently isolated bacteria from these samples using various differential and selective media supplemented with different antibiotics. Resistance profiles of bacteria were determined by using Kirby-Bauer disk diffusion method. Further, ARGs were enumerated by using the quantitative polymerase chain reaction (qPCR) to detect and quantify ARGs. The obtained qPCR data and hydro-meteorological were used to create an ML model with high prediction performance and we further used two explainable artificial intelligence (xAI) model-agnostic interpretation methods to describe the internal behavior of ML model.

Results

Using qPCR, we detected blaCTX-M, blaNDM, blaCMY, blaOXA, blatetX, blasul1, and blaaac(6'-Ib-cr) in the beach waters. Further, we developed ML prediction models for blaaac(6'-Ib-cr), blasul1, and blatetX using the hydro-metrological and qPCR-derived data and the models demonstrated strong performance, with R2 values of 0.957, 0.997, and 0.976, respectively.

Conclusions

Our findings show that environmental factors, such as water temperature, precipitation, and tide, are among the important predictors of the abundance of resistance genes at beaches.

Unraveling the antibiotic resistome in backwater zones of large cascade reservoirs: Co-occurrence patterns, horizontal transfer directions and health risks

The widespread occurrence of antibiotic resistant genes (ARGs) throughout aquatic environments has raised global concerns for public health. However, the profiles and patterns of antibiotic resistome in backwater zone of cascade reservoirs, where water flow is slowed down, are still poorly understood. Here, we proposed a metagenomic analysis framework to comprehensively reveal the diversity, abundance, co-occurrence patterns and transfer direction of ARGs in cascade reservoirs system and evaluated their health risks through a procedure based on contigs. A total of 364 ARGs subtypes conferring resistance to different antibiotics classes were detected in our water samples, and the dominant ARGs (macB, bacA, vanRA, bcrA) were similar in different reservoirs. Meanwhile, the distribution of ARGs was influenced by the presence of biotic factors such as metal resistant genes (MRGs) and mobile genetic elements (MGEs), as well as abiotic factors such as dissolved oxygen (DO) and pH. Remarkably, ARGs (vanR, rosB, MexT) co-occurred with plasmids and virulence factor genes (VFGs), which can lead to the emergence and spread of highly virulent and antibiotic resistant bacteria in microbial communities. Overall, this study helps administrators to better understand the complex patterns of ARGs in backwater zones of large cascade reservoirs and provides a proper procedure for detecting the presence of high-risk of ARGs.

Interplay Between Antimicrobial Resistance and Global Environmental Change

Antibiotic resistance genes predate the therapeutic uses of antibiotics. However, the current antimicrobial resistance crisis stems from our extensive use of antibiotics and the generation of environmental stressors that impose new selective pressure on microbes and drive the evolution of resistant pathogens that now threaten human health. Similar to climate change, this global threat results from human activities that change habitats and natural microbiomes, which in turn interact with human-associated ecosystems and lead to adverse impacts on human health. Human activities that alter our planet at global scales exacerbate the current resistance crisis and exemplify our central role in large-scale changes in which we are both protagonists and architects of our success but also casualties of unanticipated collateral outcomes. As cognizant participants in this ongoing planetary experiment, we are driven to understand and find strategies to curb the ongoing crises of resistance and climate change.

Application of high-throughput sequencing technologies and analytical tools for pathogen detection in urban water systems: Progress and future perspectives

The ubiquitous presence of pathogenic microorganisms, such as viruses, bacteria, fungi, and protozoa, in urban water systems poses a significant risk to public health. The emergence of infectious waterborne diseases mediated by urban water systems has become one of the leading global causes of mortality. However, the detection and monitoring of these pathogenic microorganisms have been limited by the complexity and diversity in the environmental samples. Conventional methods were restricted by long assay time, high benchmarks of identification, and narrow application sceneries. Novel technologies, such as high-throughput sequencing technologies, enable potentially full-spectrum detection of trace pathogenic microorganisms in complex environmental matrices. This review discusses the current state of high-throughput sequencing technologies for identifying pathogenic microorganisms in urban water systems with a concise summary. Furthermore, future perspectives in pathogen research emphasize the need for detection methods with high accuracy and sensitivity, the establishment of precise detection standards and procedures, and the significance of bioinformatics software and platforms. We have compiled a list of pathogens analysis software/platforms/databases that boast robust engines and high accuracy for preference. We highlight the significance of analyses by combining targeted and non-targeted sequencing technologies, short and long reads technologies, sequencing technologies, and bioinformatic tools in pursuing upgraded biosafety in urban water systems.

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

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

Functional enrichment of integrons: Facilitators of antimicrobial resistance and niche adaptation

Integrons are genetic elements, found among diverse bacteria and archaea, that capture and rearrange gene cassettes to rapidly generate genetic diversity and drive adaptation. Despite their broad taxonomic and geographic prevalence, and their role in microbial adaptation, the functions of gene cassettes remain poorly characterized. Here, using a combination of bioinformatic and experimental analyses, we examined the functional diversity of gene cassettes from different environments. We find that cassettes encode diverse antimicrobial resistance (AMR) determinants, including those conferring resistance to antibiotics currently in the developmental pipeline. Further, we find a subset of cassette functions is universally enriched relative to their broader metagenomes. These are largely involved in (a)biotic interactions, including AMR, phage defense, virulence, biodegradation, and stress tolerance. The remainder of functions are sample-specific, suggesting that they confer localised functions relevant to their microenvironment. Together, they comprise functional profiles different from bulk metagenomes, representing niche-adaptive components of the prokaryotic pangenome.

Soil-specific responses in the antibiotic resistome of culturable Acinetobacter spp. and other non-fermentative Gram-negative bacteria following experimental manure application

Acinetobacter spp. and other non-fermenting Gram-negative bacteria (NFGNB) represent an important group of opportunistic pathogens due to their propensity for multiple, intrinsic, or acquired antimicrobial resistance (AMR). Antimicrobial resistant bacteria and their genes can spread to the environment through livestock manure. This study investigated the effects of fresh manure from dairy cows under antibiotic prophylaxis on the antibiotic resistome and AMR hosts in microcosms using pasture soil. We specifically focused on culturable Acinetobacter spp. and other NFGNB using CHROMagar Acinetobacter. We conducted two 28-days incubation experiments to simulate natural deposition of fresh manure on pasture soil and evaluated the effects on antibiotic resistance genes (ARGs) and bacterial hosts through shotgun metagenomics. We found that manure application altered the abundance and composition of ARGs and their bacterial hosts, and that the effects depended on the soil source. Manure enriched the antibiotic resistome of bacteria only in the soil where native bacteria had a low abundance of ARGs. Our study highlights the role of native soil bacteria in modulating the consequences of manure deposition on soil and confirms the potential of culturable Acinetobacter spp. and other NFGNB to accumulate AMR in pasture soil receiving fresh manure.

Silicon versus Superbug: Assessing Machine Learning's Role in the Fight against Antimicrobial Resistance

In his 1945 Nobel Prize acceptance speech, Sir Alexander Fleming warned of antimicrobial resistance (AMR) if the necessary precautions were not taken diligently. As the growing threat of AMR continues to loom over humanity, we must look forward to alternative diagnostic tools and preventive measures to thwart looming economic collapse and untold mortality worldwide. The integration of machine learning (ML) methodologies within the framework of such tools/pipelines presents a promising avenue, offering unprecedented insights into the underlying mechanisms of resistance and enabling the development of more targeted and effective treatments. This paper explores the applications of ML in predicting and understanding AMR, highlighting its potential in revolutionizing healthcare practices. From the utilization of supervised-learning approaches to analyze genetic signatures of antibiotic resistance to the development of tools and databases, such as the Comprehensive Antibiotic Resistance Database (CARD), ML is actively shaping the future of AMR research. However, the successful implementation of ML in this domain is not without challenges. The dependence on high-quality data, the risk of overfitting, model selection, and potential bias in training data are issues that must be systematically addressed. Despite these challenges, the synergy between ML and biomedical research shows great promise in combating the growing menace of antibiotic resistance.

Incomplete degradation of aromatic-aliphatic copolymer leads to proliferation of microplastics and antibiotic resistance genes

Biodegradable plastics (BDPs) have attracted extensive attention as an alternative to conventional plastics. BDPs could be mineralized by composting, while the quality of compost affected by the presence of BDPs and the residual microplastics (MPs) has not been well evaluated. This study aimed to explore the MPs release potential and environmental implications of commercial BDPs (aromatic-aliphatic copolymer) films in uncontrolled composting. Results showed that the molecular weight of BDPs decreased by >60% within 60 d. However, the non-extracted organic matter and wet-sieving measurements indicated that MPs continuously released and accumulated during regular composting. The average MPs release potential (0.1-5 mm) was 134.6 ± 18.1 particles/mg (BDPs), which resulted in 103-104 particles/g dw in compost. The plastisphere of MPs showed a significantly higher (0.95-16.76 times) abundance of antibiotic resistance genes (ARGs), which resulted in the rising (1.34-2.24 times) of ARGs in compost heaps, in comparison to the control groups. Overall, BDPs promote the spread of ARGs through the selective enrichment of bacteria and horizontal transfer from released MPs. These findings confirmed that BDPs could enhance the release potential of MPs and the dissemination of ARGs, which would promote the holistic understanding and environmental risk of BDPs.

Microbial hitchhikers harbouring antimicrobial-resistance genes in the riverine plastisphere

BACKGROUND

The widespread nature of plastic pollution has given rise to wide scientific and social concern regarding the capacity of these materials to serve as vectors for pathogenic bacteria and reservoirs for Antimicrobial Resistance Genes (ARG). In- and ex-situ incubations were used to characterise the riverine plastisphere taxonomically and functionally in order to determine whether antibiotics within the water influenced the ARG profiles in these microbiomes and how these compared to those on natural surfaces such as wood and their planktonic counterparts.

RESULTS

We show that plastics support a taxonomically distinct microbiome containing potential pathogens and ARGs. While the plastisphere was similar to those biofilms that grew on wood, they were distinct from the surrounding water microbiome. Hence, whilst potential opportunistic pathogens (i.e. Pseudomonas aeruginosa, Acinetobacter and Aeromonas) and ARG subtypes (i.e. those that confer resistance to macrolides/lincosamides, rifamycin, sulfonamides, disinfecting agents and glycopeptides) were predominant in all surface-related microbiomes, especially on weathered plastics, a completely different set of potential pathogens (i.e. Escherichia, Salmonella, Klebsiella and Streptococcus) and ARGs (i.e. aminoglycosides, tetracycline, aminocoumarin, fluoroquinolones, nitroimidazole, oxazolidinone and fosfomycin) dominated in the planktonic compartment. Our genome-centric analysis allowed the assembly of 215 Metagenome Assembled Genomes (MAGs), linking ARGs and other virulence-related genes to their host. Interestingly, a MAG belonging to Escherichia -that clearly predominated in water- harboured more ARGs and virulence factors than any other MAG, emphasising the potential virulent nature of these pathogenic-related groups. Finally, ex-situ incubations using environmentally-relevant concentrations of antibiotics increased the prevalence of their corresponding ARGs, but different riverine compartments -including plastispheres- were affected differently by each antibiotic.

CONCLUSIONS

Our results provide insights into the capacity of the riverine plastisphere to harbour a distinct set of potentially pathogenic bacteria and function as a reservoir of ARGs. The environmental impact that plastics pose if they act as a reservoir for either pathogenic bacteria or ARGs is aggravated by the persistence of plastics in the environment due to their recalcitrance and buoyancy. Nevertheless, the high similarities with microbiomes growing on natural co-occurring materials and even more worrisome microbiome observed in the surrounding water highlights the urgent need to integrate the analysis of all environmental compartments when assessing risks and exposure to pathogens and ARGs in anthropogenically-impacted ecosystems. Video Abstract.

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.

Effects of different concentrations of biological maturity agents on nitrogen and microbial diversity of Auricularia heimuer residue compost

This study investigated the effects of different concentrations of biological maturity agents on the composting process of Auricularia heimuer residue by adding them to the composting process. By measuring the changes in physical and chemical indicators and microbial diversity during composting, the results showed that the addition of biological maturity agents had a certain promoting effect on compost temperature, humidity, pH, seed germination index, and vitality index. Appropriate composting days can promote the accumulation of ammonium nitrogen. The carbon content of humin and E4/E6 of treatments A, B, and E were significantly higher than those of the initial treatment. D0.CK treatment had the most types of resistance genes and the most abundant resistance genes. As composting progresses, the abundance of 13 resistance genes decreased. Adding high concentrations of biological maturity agents can activate the defense mechanism during the composting process, greatly ensuring the safety of fungi residue as a fertilizer.

PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model

MOTIVATION

Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resistance, recent extensive investigations using metagenomic and metatranscriptomic approaches have unveiled a noteworthy concern. A significant fraction of proteins defies annotation through conventional sequence similarity-based methods, an issue that extends to ARGs, potentially leading to their under-recognition due to dissimilarities at the sequence level.

RESULTS

Herein, we proposed an Artificial Intelligence-powered ARG identification framework using a pretrained large protein language model, enabling ARG identification and resistance category classification simultaneously. The proposed PLM-ARG was developed based on the most comprehensive ARG and related resistance category information (>28K ARGs and associated 29 resistance categories), yielding Matthew's correlation coefficients (MCCs) of 0.983 ± 0.001 by using a 5-fold cross-validation strategy. Furthermore, the PLM-ARG model was verified using an independent validation set and achieved an MCC of 0.838, outperforming other publicly available ARG prediction tools with an improvement range of 51.8%-107.9%. Moreover, the utility of the proposed PLM-ARG model was demonstrated by annotating resistance in the UniProt database and evaluating the impact of ARGs on the Earth's environmental microbiota.

AVAILABILITY AND IMPLEMENTATION

PLM-ARG is available for academic purposes at https://github.com/Junwu302/PLM-ARG, and a user-friendly webserver (http://www.unimd.org/PLM-ARG) is also provided.

Anthropogenic contributions to antibiotic resistance gene pollution in household drinking water revealed by machine-learning-based source-tracking

Although the presence of antibiotic resistance genes (ARGs) in drinking water and their potential horizontal gene transfer to pathogenic microbes are known to pose a threat to human health, their pollution levels and potential anthropogenic sources are poorly understood. In this study, broad-spectrum ARG profiling combined with machine-learning-based source classification SourceTracker was performed to investigate the pollution sources of ARGs in household drinking water collected from 95 households in 47 cities of eight countries/regions. In total, 451 ARG subtypes belonging to 19 ARG types were detected with total abundance in individual samples ranging from 1.4 × 10-4 to 1.5 × 10° copies per cell. Source tracking analysis revealed that many ARGs were highly contributed by anthropogenic sources (37.1%), mainly wastewater treatment plants. The regions with the highest detected ARG contribution from wastewater (∼84.3%) used recycled water as drinking water, indicating the need for better ARG control strategies to ensure safe water quality in these regions. Among ARG types, sulfonamide, rifamycin and tetracycline resistance genes were mostly anthropogenic in origin. The contributions of anthropogenic sources to the 20 core ARGs detected in all of the studied countries/regions varied from 36.6% to 84.1%. Moreover, the anthropogenic contribution of 17 potential mobile ARGs identified in drinking water was significantly higher than other ARGs, and metagenomic assembly revealed that these mobile ARGs were carried by diverse potential pathogens. These results indicate that human activities have exacerbated the constant input and transmission of ARGs in drinking water. Our further risk classification framework revealed three ARGs (sul1, sul2 and aadA) that pose the highest risk to public health given their high prevalence, anthropogenic sources and mobility, facilitating accurate monitoring and control of anthropogenic pollution in drinking water.

Alterations in faecal microbiome and resistome in Chinese international travellers: a metagenomic analysis

BACKGROUND

International travel increases the risk of acquisition of antibiotic-resistant bacteria and antibiotic resistance genes (ARGs). Previous studies have characterized the changes in the gut microbiome and resistome of Western travellers; however, information on non-Western populations and the effects of travel-related risk factors on the gut microbiome and resistome remains limited.

METHODS

We conducted a prospective observational study on a cohort of 90 healthy Chinese adult residents of Hong Kong. We characterized the microbiome and resistome in stools collected from the subjects before and after travelling to diverse international locations using shotgun metagenomic sequencing and examined their associations with travel-related variables.

RESULTS

Our results showed that travel neither significantly changed the taxonomic composition of the faecal microbiota nor altered the alpha (Shannon) or beta diversity of the faecal microbiome or resistome. However, travel significantly increased the number of ARGs. Ten ARGs, including aadA, TEM, mgrB, mphA, qnrS9 and tetR, were significantly enriched in relative abundance after travel, eight of which were detected in metagenomic bins belonging to Escherichia/Shigella flexneri in the post-trip samples. In sum, 30 ARGs significantly increased in prevalence after travel, with the largest changes observed in tetD and a few qnrS variants (qnrS9, qnrS and qnrS8). We found that travel to low- or middle-income countries, or Africa or Southeast Asia, increased the number of ARG subtypes, whereas travel to low- or middle-income countries and the use of alcohol-based hand sanitizer (ABHS) or doxycycline as antimalarial prophylaxis during travel resulted in increased changes in the beta diversity of the faecal resistome.

CONCLUSIONS

Our study highlights travel to low- or middle-income countries, Africa or Southeast Asia, a long travel duration, or the use of ABHS or doxycycline as antimalarial prophylaxis as important risk factors for the acquisition/enrichment of ARGs during international travel.

Global diversity and biogeography of DNA viral communities in activated sludge systems

BACKGROUND

Activated sludge (AS) systems in wastewater treatment plants (WWTPs) harbor enormous viruses that regulate microbial metabolism and nutrient cycling, significantly influencing the stability of AS systems. However, our knowledge about the diversity of viral taxonomic groups and functional traits in global AS systems is still limited. To address this gap, we investigated the global diversity and biogeography of DNA viral communities in AS systems using 85,114 viral operational taxonomic units (vOTUs) recovered from 144 AS samples collected across 54 WWTPs from 13 different countries.

RESULTS

AS viral communities and their functional traits exhibited distance-decay relationship (DDR) at the global scale and latitudinal diversity gradient (LDG) from equator to mid-latitude. Furthermore, it was observed that AS viral community and functional gene structures were largely driven by the geographic factors and wastewater types, of which the geographic factors were more important. Carrying and disseminating auxiliary metabolic genes (AMGs) associated with the degradation of polysaccharides, sulfate reduction, denitrification, and organic phosphoester hydrolysis, as well as the lysis of crucial functional microbes that govern biogeochemical cycles were two major ways by which viruses could regulate AS functions. It was worth noting that our study revealed a high abundance of antibiotic resistance genes (ARGs) in viral genomes, suggesting that viruses were key reservoirs of ARGs in AS systems.

CONCLUSIONS

Our results demonstrated the highly diverse taxonomic groups and functional traits of viruses in AS systems. Viral lysis of host microbes and virus-mediated HGT can regulate the biogeochemical and nutrient cycles, thus affecting the performance of AS systems. These findings provide important insights into the viral diversity, function, and ecology in AS systems on a global scale. Video Abstract.

Impact of environment on transmission of antibiotic-resistant superbugs in humans and strategies to lower dissemination of antibiotic resistance

Antibiotics are the most efficient type of therapy developed in the twentieth century. From the early 1960s to the present, the rate of discovery of new and therapeutically useful classes of antibiotics has significantly decreased. As a result of antibiotic use, novel strains emerge that limit the efficiency of therapies in patients, resulting in serious consequences such as morbidity or mortality, as well as clinical difficulties. Antibiotic resistance has created major concern and has a greater impact on global health. Horizontal and vertical gene transfers are two mechanisms involved in the spread of antibiotic resistance genes (ARGs) through environmental sources such as wastewater treatment plants, agriculture, soil, manure, and hospital-associated area discharges. Mobile genetic elements have an important part in microbe selection pressure and in spreading their genes into new microbial communities; additionally, it establishes a loop between the environment, animals, and humans. This review contains antibiotics and their resistance mechanisms, diffusion of ARGs, prevention of ARG transmission, tactics involved in microbiome identification, and therapies that aid to minimize infection, which are explored further below. The emergence of ARGs and antibiotic-resistant bacteria (ARB) is an unavoidable threat to global health. The discovery of novel antimicrobial agents derived from natural products shifts the focus from chemical modification of existing antibiotic chemical composition. In the future, metagenomic research could aid in the identification of antimicrobial resistance genes in the environment. Novel therapeutics may reduce infection and the transmission of ARGs.

Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics

Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. Deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process.Communicated by Ramaswamy H. Sarma.

Identification and functional analysis of novel protein-encoding sequences related to stress-resistance

Currently, industrial bioproducts are less competitive than chemically produced goods due to the shortcomings of conventional microbial hosts. Thus, is essential developing robust bacteria for improved cell tolerance to process-specific parameters. In this context, metagenomic approaches from extreme environments can provide useful biological parts to improve bacterial robustness. Here, in order to build genetic constructs that increase bacterial resistance to diverse stress conditions, we recovered novel protein-encoding sequences related to stress-resistance from metagenomic databases using an in silico approach based on Hidden-Markov-Model profiles. For this purpose, we used metagenomic shotgun sequencing data from microbial communities of extreme environments to identify genes encoding chaperones and other proteins that confer resistance to stress conditions. We identified and characterized 10 novel protein-encoding sequences related to the DNA-binding protein HU, the ATP-dependent protease ClpP, and the chaperone protein DnaJ. By expressing these genes in Escherichia coli under several stress conditions (including high temperature, acidity, oxidative and osmotic stress, and UV radiation), we identified five genes conferring resistance to at least two stress conditions when expressed in E. coli. Moreover, one of the identified HU coding-genes which was retrieved from an acidic soil metagenome increased E. coli tolerance to four different stress conditions, implying its suitability for the construction of a synthetic circuit directed to expand broad bacterial resistance.

ARGem: a new metagenomics pipeline for antibiotic resistance genes: metadata, analysis, and visualization

Antibiotic resistance is of crucial interest to both human and animal medicine. It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user's configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available at https://github.com/xlxlxlx/ARGem.

Metagenomic analysis reveals the dissemination mechanisms and risks of resistance genes in plateau lakes

Antibiotic resistance genes (ARGs) are emerging as environmental pollutants that can persist and disseminate in aquatic environments. Lakes, as important sources of freshwater, also serve as potential natural reservoirs of ARGs. In this study, we analyzed the distribution and potential risks of resistance genes in five typical freshwater lakes on the Yunnan-Guizhou Plateau. Our findings revealed that multidrug and MLS ARGs dominated in the studied lakes. Notably, while Lugu Lake exhibited higher abundance of ARGs, mobile genetic elements (MGEs), and metal resistance genes (MRGs), a greater resistome risk was observed in the eutrophic Xingyun Lake. The dissemination processes of ARGs and MRGs are primarily driven by microbial communities and the horizontal gene transfer via MGEs. Limnohabitans, Flavobacterium, and Acinetobacter were identified as key players in the dissemination of ARGs. Our study highlights the persistence of ARGs and provides valuable baseline data and risk assessment of ARGs in plateau freshwater lakes.

Skin microbiome attributes associate with biophysical skin ageing

Two major arms of skin ageing are changes in the skin's biophysical conditions and alterations in the skin microbiome. This work partitioned both arms to study their interaction in detail. Leveraging the resolution provided by shotgun metagenomics, we explored how skin microbial species, strains and gene content interact with the biophysical traits of the skin during ageing. With a dataset well-controlled for confounding factors, we found that skin biophysical traits, especially the collagen diffusion coefficient, are associated with the composition and the functional potential of the skin microbiome, including the abundance of bacterial strains found in nosocomial infections and the abundance of antibiotic resistance genes. Our findings reveal important associations between skin biophysical features and ageing-related changes in the skin microbiome and generate testable hypotheses for the mechanisms of such associations.

Predicting variable gene content in Escherichia coli using conserved genes

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%-90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943-0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including "hypothetical proteins" was accurately predicted (F1 = 0.902 [0.898-0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876-0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data. IMPORTANCE Having the ability to predict the protein-encoding gene content of a genome is important for assessing genome quality, binning genomes from shotgun metagenomic assemblies, and assessing risk due to the presence of antimicrobial resistance and other virulence genes. In this study, we built a set of binary classifiers for predicting the presence or absence of variable genes occurring in 10%-90% of all publicly available E. coli genomes. Overall, the results show that a large portion of the E. coli variable gene content can be predicted with high accuracy, including genes with functions relating to horizontal gene transfer. This study offers a strategy for predicting gene content using limited input sequence data.

The Microbiome and Antibiotic Resistome in Soil under Biodegradable Composite Carbon Source Amendment

The decomposition of biodegradable composite carbon sources generates a large amount of biodegradable microplastics, which may not only furnish microbial denitrification, but might also pose potential environmental risks. In the present study, the effects of different dosages of a biodegradable composite carbon source on the microbial communities, the nitrogen metabolic pathways and the antibiotic resistome were explored through Illumina MiSeq sequencing analysis and metagenomic analysis. The results of partial least-square discriminant analysis (PLS-DA) and analysis of similarity (ANOSIM) demonstrated that the response of the bacterial community to a biodegradable composite carbon source was more obvious than the fungal community. The application of biodegradable microplastics diminished the complexity of the microbial communities to some extent and obviously stimulated denitrification. Antibiotics resistance gene (ARG) dispersal was not evidently accelerated after the addition of biodegradable composite carbon source. Lysobacter, Methylobacillus, Phyllobacterium, Sinorhizobium, Sphingomonas from Proteobacteria and Actinomadura, Agromyces, Gaiella and Micromonospora from Actinobacteria were the major ARG hosts. Overall, the addition of a biodegradable composite carbon source shaped microbial communities and their antibiotic resistance profiles in this study.

A Comprehensive Self-Resistance Gene Database for Natural-Product Discovery with an Application to Marine Bacterial Genome Mining

In the world of microorganisms, the biosynthesis of natural products in secondary metabolism and the self-resistance of the host always occur together and complement each other. Identifying resistance genes from biosynthetic gene clusters (BGCs) helps us understand the self-defense mechanism and predict the biological activity of natural products synthesized by microorganisms. However, a comprehensive database of resistance genes is still lacking, which hinders natural product annotation studies in large-scale genome mining. In this study, we compiled a resistance gene database (RGDB) by scanning the four available databases: CARD, MIBiG, NCBIAMR, and UniProt. Every resistance gene in the database was annotated with resistance mechanisms and possibly involved chemical compounds, using manual annotation and transformation from the resource databases. The RGDB was applied to analyze resistance genes in 7432 BGCs in 1390 genomes from a marine microbiome project. Our calculation showed that the RGDB successfully identified resistance genes for more than half of the BGCs, suggesting that the database helps prioritize BGCs that produce biologically active natural products.

Extensive screening reveals previously undiscovered aminoglycoside resistance genes in human pathogens

Antibiotic resistance is a growing threat to human health, caused in part by pathogens accumulating antibiotic resistance genes (ARGs) through horizontal gene transfer. New ARGs are typically not recognized until they have become widely disseminated, which limits our ability to reduce their spread. In this study, we use large-scale computational screening of bacterial genomes to identify previously undiscovered mobile ARGs in pathogens. From ~1 million genomes, we predict 1,071,815 genes encoding 34,053 unique aminoglycoside-modifying enzymes (AMEs). These cluster into 7,612 families (<70% amino acid identity) of which 88 are previously described. Fifty new AME families are associated with mobile genetic elements and pathogenic hosts. From these, 24 of 28 experimentally tested AMEs confer resistance to aminoglycoside(s) in Escherichia coli, with 17 providing resistance above clinical breakpoints. This study greatly expands the range of clinically relevant aminoglycoside resistance determinants and demonstrates that computational methods enable early discovery of potentially emerging ARGs.

Metagenomic Profiles of Yak and Cattle Manure Resistomes in Different Feeding Patterns before and after Composting

Antibiotic resistance is a global threat to public health, with antibiotic resistance genes (ARGs) being one of the emerging contaminants; furthermore, animal manure is an important reservoir of biocide resistance genes (BRGs) and metal resistance genes (MRGs). However, few studies have reported differences in the abundance and diversity of BRGs and MRGs between different types of animal manure and the changes in BRGs and MRGs before and after composting. This study employed a metagenomics-based approach to investigate ARGs, BRGs, MRGs, and mobile genetic elements (MGEs) of yak and cattle manure before and after composting under grazing and intensive feeding patterns. The total abundances of ARGs, clinical ARGs, BRGs, MRGs, and MGEs were lower in the manure of grazing livestock than in the manure of the intensively fed group. After composting, the total abundances of ARGs, clinical ARGs, and MGEs in intensively fed livestock manure decreased, whereas those of ARGs, clinical ARGs, MRGs, and MGEs increased in grazing livestock manure. The synergy between MGEs mediated horizontal gene transfer and vertical gene transmission via host bacteria proliferation, which was the main driver that altered the abundance and diversity of ARGs, BRGs, and MRGs in livestock manure and compost. Additionally, tetQ, IS91, mdtF, and fabK were potential indicators for estimating the total abundance of clinical ARGs, BRGs, MRGs, and MGEs in livestock manure and compost. These findings suggest that grazing livestock manure can be directly discharged into the fields, whereas intensively fed livestock manure should be composted before returning to the field. IMPORTANCE The recent increase in the prevalence of antibiotic resistance genes (ARGs), biocide resistance genes (BRGs), and metal resistance genes (MRGs) in livestock manure poses risks to human health. Composting is known to be a promising technology for reducing the abundance of resistance genes. This study investigated the differences and changes in the abundances of ARGs, BRGs, and MRGs between yak and cattle manure under grazing and intensive feeding patterns before and after composting. The results indicate that the feeding pattern significantly affected the abundances of resistance genes in livestock manure. Manure in intensive farming should be composted before being discharged into the field, while grazing livestock manure is not suitable for composting due to an increased number of resistance genes.