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

Predicting antimicrobial resistance in E. coli with discriminative position fused deep learning classifier

Escherichia coli (E. coli) has become a particular concern due to the increasing incidence of antimicrobial resistance (AMR) observed worldwide. Using machine learning (ML) to predict E. coli AMR is a more efficient method than traditional laboratory testing. However, further improvement in the predictive performance of existing models remains challenging. In this study, we collected 1937 high-quality whole genome sequencing (WGS) data from public databases with an antimicrobial resistance phenotype and modified the existing workflow by adding an attention mechanism to enable the modified workflow to focus more on core single nucleotide polymorphisms (SNPs) that may significantly lead to the development of AMR in E. coli. While comparing the model performance before and after adding the attention mechanism, we also performed a cross-comparison among the published models using random forest (RF), support vector machine (SVM), logistic regression (LR), and convolutional neural network (CNN). Our study demonstrates that the discriminative positional colors of Chaos Game Representation (CGR) images can selectively influence and highlight genome regions without prior knowledge, enhancing prediction accuracy. Furthermore, we developed an online tool (https://github.com/tjiaa/E.coli-ML/tree/main) for assisting clinicians in the rapid prediction of the AMR phenotype of E. coli and accelerating clinical decision-making.

Nature-based bioreactors: Tackling antibiotic resistance in urban wastewater treatment

The overuse and misuse of antibiotics have accelerated the selection of antibiotic-resistant bacteria, significantly impacting human, animal, and environmental health. As aquatic environments are vulnerable to antibiotic resistance, suitable management practices should be adopted to tackle this phenomenon. Here we show an effective, nature-based solution for reducing antibiotic resistance from actual wastewater. We utilize a bioreactor that relies on benthic (biofilms) and planktonic microbial communities to treat secondary effluent from a small urban wastewater treatment plant (<10,000 population equivalent). This treated effluent is eventually released into the local aquatic ecosystem. We observe high removal efficiency for genes that provide resistance to commonly used antibiotic families, as well as for mobile genetic elements that could potentially aid in their spread. Importantly, we notice a buildup of sulfonamide (sul1 and sul2) and tetracycline (tet(C), tet(G), and tetR) resistance genes specifically in biofilms. This advancement marks the initial step in considering this bioreactor as a nature-based, cost-effective tertiary treatment option for small UWWTPs facing antibiotic resistance challenges.

Hierarchical multi-task deep learning-assisted construction of human gut microbiota reactive oxygen species-scavenging enzymes database

In the process of oxygen reduction, reactive oxygen species (ROS) are generated as intermediates, including superoxide anion (O2-), hydrogen peroxide (H2O2), and hydroxyl radicals (OH-). ROS can be destructive, and an imbalance between oxidants and antioxidants in the body can lead to pathological inflammation. Inappropriate ROS production can cause oxidative damage, disrupting the balance in the body and potentially leading to DNA damage in intestinal epithelial cells and beneficial bacteria. Microorganisms have evolved various enzymes to mitigate the harmful effects of ROS. Accurately predicting the types of ROS-scavenging enzymes (ROSes) is crucial for understanding the oxidative stress mechanisms and formulating strategies to combat diseases related to the "gut-organ axis." Currently, there are no available ROSes databases (DBs). In this study, we propose a systematic workflow comprising three modules and employ a hierarchical multi-task deep learning approach to collect, expand, and explore ROSes-related entries. Based on this, we have developed the human gut microbiota ROSes DB (http://39.101.72.186/), which includes 7,689 entries. This DB provides user-friendly browsing and search features to support various applications. With the assistance of ROSes DB, various communication-based microbial interactions can be explored, further enabling the construction and analysis of the evolutionary and complex networks of ROSes DB in human gut microbiota species.IMPORTANCEReactive oxygen species (ROS) is generated during the process of oxygen reduction, including superoxide anion, hydrogen peroxide, and hydroxyl radicals. ROS can potentially cause damage to cells and DNA, leading to pathological inflammation within the body. Microorganisms have evolved various enzymes to mitigate the harmful effects of ROS, thereby maintaining a balance of microorganisms within the host. The study highlights the current absence of a ROSes DB, emphasizing the crucial importance of accurately predicting the types of ROSes for understanding oxidative stress mechanisms and developing strategies for diseases related to the "gut-organ axis." This research proposes a systematic workflow and employs a multi-task deep learning approach to establish the human gut microbiota ROSes DB. This DB comprises 7,689 entries and serves as a valuable tool for researchers to delve into the role of ROSes in the human gut microbiota.

Filamentous bacteria-induced sludge bulking can alter antibiotic resistance gene profiles and increase potential risks in wastewater treatment systems

Sludge bulking caused by filamentous bacteria is a prevalent issue in wastewater treatment systems. While previous studies have primarily concentrated on controlling sludge bulking, the biological risks associated with it have been overlooked. This study demonstrates that excessive growth of filamentous bacteria during sludge bulking can significantly increase the abundance of antibiotic resistance genes (ARGs) in activated sludge. Through metagenomic analysis, we identified specific ARGs carried by filamentous bacteria, such as Sphaerotilus and Thiothrix, which are responsible for bulking. Additionally, by examining over 1,000 filamentous bacterial genomes, we discovered a diverse array of ARGs across different filamentous bacteria derived from wastewater treatment systems. Our findings indicate that 74.84% of the filamentous bacteria harbor at least one ARG, with the occurrence frequency of ARGs in these bacteria being approximately 1.5 times higher than that in the overall bacterial population in activated sludge. Furthermore, genomic and metagenomic analyses have shown that the ARGs in filamentous bacteria are closely linked to mobile genetic elements and are frequently found in potentially pathogenic bacteria, highlighting potential risks posed by these filamentous bacteria. These insights enhance our understanding of ARGs in activated sludge and underscore the importance of risk management in wastewater treatment systems.

A trait-based ecological perspective on the soil microbial antibiotic-related genetic machinery

Antibiotic resistance crisis dictates the need for resistance monitoring and the search for new antibiotics. The development of monitoring protocols is hindered by the great diversity of resistance factors, while the "streetlight effect" denies the possibility of discovering novel drugs based on existing databases. In this study, we address these challenges using high-throughput environmental screening viewed from a trait-based ecological perspective. Through an in-depth analysis of the metagenomes of 658 topsoil samples spanning Europe, we explored the distribution of 241 prokaryotic and fungal genes responsible for producing metabolites with antibiotic properties and 485 antibiotic resistance genes. We analyzed the diversity of these gene collections at different levels and modeled the distribution of each gene across environmental gradients. Our analyses revealed several nonparallel distribution patterns of the genes encoding sequential steps of enzymatic pathways synthesizing large antibiotic groups, pointing to gaps in existing databases and suggesting potential for discovering new analogues of known antibiotics. We show that agricultural activity caused a continental-scale homogenization of microbial antibiotic-related machinery, emphasizing the importance of maintaining indigenous ecosystems within the landscape mosaic. Based on the relationships between the proportion of the genes in the metagenomes with the main predictors (soil pH, land cover type, climate temperature and humidity), we illustrate how the properties of chemical structures dictate the distribution of the genes responsible for their synthesis across environments. With this understanding, we propose general principles to facilitate the discovery of antibiotics, including principally new ones, establish abundance baselines for antibiotic resistance genes, and predict their dissemination.

Healthcare as a driver, reservoir and amplifier of antimicrobial resistance: opportunities for interventions

Antimicrobial resistance (AMR) is a global health challenge that threatens humans, animals and the environment. Evidence is emerging for a role of healthcare infrastructure, environments and patient pathways in promoting and maintaining AMR via direct and indirect mechanisms. Advances in vaccination and monoclonal antibody therapies together with integrated surveillance, rapid diagnostics, targeted antimicrobial therapy and infection control measures offer opportunities to address healthcare-associated AMR risks more effectively. Additionally, innovations in artificial intelligence, data linkage and intelligent systems can be used to better predict and reduce AMR and improve healthcare resilience. In this Review, we examine the mechanisms by which healthcare functions as a driver, reservoir and amplifier of AMR, contextualized within a One Health framework. We also explore the opportunities and innovative solutions that can be used to combat AMR throughout the patient journey. We provide a perspective on the current evidence for the effectiveness of interventions designed to mitigate healthcare-associated AMR and promote healthcare resilience within high-income and resource-limited settings, as well as the challenges associated with their implementation.

Artificial intelligence tools for the identification of antibiotic resistance genes

The fight against bacterial antibiotic resistance must be given critical attention to avert the current and emerging crisis of treating bacterial infections due to the inefficacy of clinically relevant antibiotics. Intrinsic genetic mutations and transferrable antibiotic resistance genes (ARGs) are at the core of the development of antibiotic resistance. However, traditional alignment methods for detecting ARGs have limitations. Artificial intelligence (AI) methods and approaches can potentially augment the detection of ARGs and identify antibiotic targets and antagonistic bactericidal and bacteriostatic molecules that are or can be developed as antibiotics. This review delves into the literature regarding the various AI methods and approaches for identifying and annotating ARGs, highlighting their potential and limitations. Specifically, we discuss methods for (1) direct identification and classification of ARGs from genome DNA sequences, (2) direct identification and classification from plasmid sequences, and (3) identification of putative ARGs from feature selection.

Preference and regulation mechanism mediated via mobile genetic elements for antibiotic and metal resistomes during composting amended with nano ZVI loaded on biochar

This study assessed the effectiveness of nano zero-valent iron loaded on biochar (BC-nZVI) during swine manure composting. BC-nZVI significantly reduced the abundance of antibiotic resistance genes (ARGs), metal resistance genes (MRGs), and mobile genetic elements (MGEs). BC-nZVI modified the preference of MGEs to carry ARGs and MRGs, and the corrosion products of BC-nZVI could destroy cell structure, hinder electron transfer between cells, and weaken the association between ARGs, MRGs, and host bacteria. Functional genes analysis revealed that BC-nZVI down-regulated the abundance of genes affecting the transmission and metabolism of ARGs and MRGs, including type IV secretion systems, transporter systems, two-component systems, and multidrug efflux pumps. Furthermore, the BC-nZVI decreased genes related to flagella and pili production and cell membrane permeability, thereby hindering the transfer of ARGs, MRGs, and MGEs in the environment. Redundancy analysis demonstrated that changes in the microbial community induced by BC-nZVI were pivotal factors impacting the abundance of ARGs, MRGs, and MGEs. Overall, this study confirmed the efficacy of BC-nZVI in reducing resistance genes during swine manure composting, offering a promising environmental strategy to mitigate the dissemination of these contaminants.

Antimicrobial resistance and its risks evaluation in wetlands on the Qinghai-Tibetan Plateau

Amidst the global antimicrobial resistance (AMR) crisis, antibiotic resistance has permeated even the most remote environments. To understand the dissemination and evolution of AMR in minimally impacted ecosystems, the resistome and mobilome of wetlands across the Qinghai-Tibetan Plateau and its marginal regions were scrutinized using metagenomic sequencing techniques. The composition of wetland microbiomes exhibits significant variability, with dominant phyla including Proteobacteria, Actinobacteria, Bacteroidetes, and Verrucomicrobia. Notably, a substantial abundance of Antibiotic Resistance Genes (ARGs) and Mobile Genetic Elements (MGEs) was detected, encompassing 17 ARG types, 132 ARG subtypes, and 5 types of MGEs (Insertion Sequences, Insertions Sequences, Genomic Islands, Transposons, and Integrative Conjugative Elements). No significant variance was observed in the prevalence of resistome and mobilome across different wetland types (i.e., the Yellow River, other rivers, lakes, and marshes) (R=-0.5882, P=0.607). The co-occurrence of 74 ARG subtypes and 22 MGEs was identified, underscoring the pivotal role of MGEs in shaping ARG pools within the Qinghai-Tibetan Plateau wetlands. Metagenomic binning and analysis of assembled genomes (MAGs) revealed that 93 out of 206 MAGs harbored ARGs (45.15 %). Predominantly, Burkholderiales, Pseudomonadales, and Enterobacterales were identified as the primary hosts of these ARGs, many of which represent novel species. Notably, a substantial proportion of ARG-carrying MAGs also contained MGEs, reaffirming the significance of MGEs in AMR dissemination. Furthermore, utilizing the arg_ranker framework for risk assessment unveiled severe contamination of high-risk ARGs across most plateau wetlands. Moreover, some prevalent human pathogens were identified as potential hosts for these high-risk ARGs, posing substantial transmission risks. This study aims to investigate the prevalence of resistome and mobilome in wetlands, along with evaluating the risk posed by high-risk ARGs. Such insights are crucial for informing environmental protection strategies and facilitating the management of water resources on the Qinghai-Tibetan Plateau.

Distinct species turnover patterns shaped the richness of antibiotic resistance genes on eight different microplastic polymers

Elucidating the formation mechanism of plastisphere antibiotic resistance genes (ARGs) on different polymers is necessary to understand the ecological risks of plastisphere ARGs. Here, we explored the turnover and assembly mechanism of plastisphere ARGs on 8 different microplastic polymers (4 biodegradable (bMPs) and 4 non-biodegradable microplastics (nMPs)) by metagenomic sequencing. Our study revealed the presence of 479 ARGs with abundance ranging from 41.37 to 58.17 copies/16S rRNA gene in all plastispheres. These ARGs were predominantly multidrug resistance genes. The richness of plastisphere ARGs on different polymers had a significant correlation with the contribution of species turnover to plastisphere ARGs β diversity. Furthermore, polymer type was the most critical factor affecting the composition of plastisphere ARGs. More opportunistic pathogens carrying diverse ARGs on BMPs (PBAT, PBS, and PHA) with higher horizontal gene transfer potential may further magnify the ecological risks and human health threats. For example, the opportunistic pathogens Riemerella anatipestifer, Vibrio campbellii, and Vibrio cholerae are closely related to human production and life, which were the important potential hosts of many plastisphere ARGs and mobile genetic elements on BMPs. Thus, we emphasize the urgency of developing the formation mechanism of plastisphere ARGs and the necessity of controlling BMPs and ARG pollution, especially BMPs, with ever-increasing usage in daily life.

Genome-resolved metagenomics: a game changer for microbiome medicine

Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.

Subtask-Aware Representation Learning for Predicting Antibiotic Resistance Gene Properties via Gating-Controlled Mechanism

The crisis of antibiotic resistance has become a significant global threat to human health. Understanding properties of antibiotic resistance genes (ARGs) is the first step to mitigate this issue. Although many methods have been proposed for predicting properties of ARGs, most of these methods focus only on predicting antibiotic classes, while ignoring other properties of ARGs, such as resistance mechanisms and transferability. However, acquiring all of these properties of ARGs can help researchers gain a more comprehensive understanding of the essence of antibiotic resistance, which will facilitate the development of antibiotics. In this paper, the task of predicting properties of ARGs is modeled as a multi-task learning problem, and an effective subtask-aware representation learning-based framework is proposed accordingly. More specifically, property-specific expert networks and shared expert networks are utilized respectively to learn subtask-specific features for each subtask and shared features among different subtasks. In addition, a gating-controlled mechanism is employed to dynamically allocate weights to subtask-specific semantics and shared semantics obtained respectively from property-specific expert networks and shared expert networks, thus adjusting distinctive contributions of subtask-specific features and shared features to achieve optimal performance for each subtask simultaneously. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs properties prediction.

TGC-ARG: Anticipating Antibiotic Resistance via Transformer-Based Modeling and Contrastive Learning

In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) often require substantial time and effort and are limited in accuracy. Moreover, the decentralized nature of existing data repositories complicates comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce a novel computational framework named TGC-ARG designed to predict potential ARGs. This framework takes protein sequences as input, utilizes SCRATCH-1D for protein secondary structure prediction, and employs feature extraction techniques to derive distinctive features from both sequence and structural data. Subsequently, a Siamese network is employed to foster a contrastive learning environment, enhancing the model's ability to effectively represent the data. Finally, a multi-layer perceptron (MLP) integrates and processes sequence embeddings alongside predicted secondary structure embeddings to forecast ARG presence. To evaluate our approach, we curated a pioneering open dataset termed ARSS (Antibiotic Resistance Sequence Statistics). Comprehensive comparative experiments demonstrate that our method surpasses current state-of-the-art methodologies. Additionally, through detailed case studies, we illustrate the efficacy of our approach in predicting potential ARGs.

Large language model for horizontal transfer of resistance gene: From resistance gene prevalence detection to plasmid conjugation rate evaluation

The burgeoning issue of plasmid-mediated resistance genes (ARGs) dissemination poses a significant threat to environmental integrity. However, the prediction of ARGs prevalence is overlooked, especially for emerging ARGs that are potentially evolving gene exchange hotspot. Here, we explored to classify plasmid or chromosome sequences and detect resistance gene prevalence by using DNABERT. Initially, the DNABERT fine-tuned in plasmid and chromosome sequences followed by multilayer perceptron (MLP) classifier could achieve 0.764 AUC (Area under curve) on external datasets across 23 genera, outperforming 0.02 AUC than traditional statistic-based model. Furthermore, Escherichia, Pseudomonas single genera based model were also be trained to explore its predict performance to ARGs prevalence detection. By integrating K-mer frequency attributes, our model could boost the performance to predict the prevalence of ARGs in an external dataset in Escherichia with 0.0281-0.0615 AUC and Pseudomonas with 0.0196-0.0928 AUC. Finally, we established a random forest model aimed at forecasting the relative conjugation transfer rate of plasmids with 0.7956 AUC, drawing on data from existing literature. It identifies the plasmid's repression status, cellular density, and temperature as the most important factors influencing transfer frequency. With these two models combined, they provide useful reference for quick and low-cost integrated evaluation of resistance gene transfer, accelerating the process of computer-assisted quantitative risk assessment of ARGs transfer in environmental field.

Deep learning for predicting 16S rRNA gene copy number

Culture-independent 16S rRNA gene metabarcoding is a commonly used method for microbiome profiling. To achieve more quantitative cell fraction estimates, it is important to account for the 16S rRNA gene copy number (hereafter 16S GCN) of different community members. Currently, there are several bioinformatic tools available to estimate the 16S GCN values, either based on taxonomy assignment or phylogeny. Here we present a novel approach ANNA16, Artificial Neural Network Approximator for 16S rRNA gene copy number, a deep learning-based method that estimates the 16S GCN values directly from the 16S gene sequence strings. Based on 27,579 16S rRNA gene sequences and gene copy number data from the rrnDB database, we show that ANNA16 outperforms the commonly used 16S GCN prediction algorithms. Interestingly, Shapley Additive exPlanations (SHAP) shows that ANNA16 can identify unexpected informative positions in 16S rRNA gene sequences without any prior phylogenetic knowledge, which suggests potential applications beyond 16S GCN prediction.

A systematic review of antibiotics and antibiotic resistance genes (ARGs) in mariculture wastewater: Antibiotics removal by microalgal-bacterial symbiotic system (MBSS), ARGs characterization on the metagenomic

Antibiotic residues in mariculture wastewater seriously affect the aquatic environment. Antibiotic Resistance Genes (ARGs) produced under antibiotic stress flow through the environment and eventually enter the human body, seriously affecting human health. Microalgal-bacterial symbiotic system (MBSS) can remove antibiotics from mariculture and reduce the flow of ARGs into the environment. This review encapsulates the present scenario of mariculture wastewater, the removal mechanism of MBSS for antibiotics, and the biomolecular information under metagenomic assay. When confronted with antibiotics, there was a notable augmentation in the extracellular polymeric substances (EPS) content within MBSS, along with a concurrent elevation in the proportion of protein (PN) constituents within the EPS, which limits the entry of antibiotics into the cellular interior. Quorum sensing stimulates the microorganisms to produce biological responses (DNA synthesis - for adhesion) through signaling. Oxidative stress promotes gene expression (coupling, conjugation) to enhance horizontal gene transfer (HGT) in MBSS. The microbial community under metagenomic detection is dominated by aerobic bacteria in the bacterial-microalgal system. Compared to aerobic bacteria, anaerobic bacteria had the significant advantage of decreasing the distribution of ARGs. Overall, MBSS exhibits remarkable efficacy in mitigating the challenges posed by antibiotics and resistant genes from mariculture wastewater.

Assessing intracellular and extracellular distribution of antibiotic resistance genes in the commercial organic fertilizers

Compost-based organic fertilizers often contain high levels of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs). Previous studies focused on quantification of total ARGs and MGEs. For a more accurate risk assessment of the dissemination risk of antibiotic resistance, it is necessary to quantify the intracellular and extracellular distribution of ARGs and MGEs. In the present study, extracellular ARGs and MGEs (eARGs and eMGEs) and intracellular ARGs and MGEs (iARGs and iMGEs) were separately analyzed in 51 commercial composts derived from different raw materials by quantitative polymerase chain reaction (qPCR) and metagenomic sequencing. Results showed that eARGs and eMGEs accounted for 11-56% and 4-45% of the total absolute abundance of ARGs and MGEs, respectively. Comparable diversity, host composition and association with MGEs were observed between eARGs and iARGs. Contents of high-risk ARGs were similar between eARGs and iARGs, with high-risk ARGs in the two forms accounting for 6.7% and 8.2% of the total abundances, respectively. Twenty-four percent of the overall ARGs were present in plasmids, while 56.7% of potentially mobile ARGs were found to be associated with plasmids. Variation partitioning analysis, null model and neutral community model indicated that the compositions of both eARGs and iARGs were largely driven by deterministic mechanisms. These results provide important insights into the cellular distribution of ARGs in manure composts that should be paid with specific attention in risk assessment and management.

Application of machine learning for antibiotic resistance in water and wastewater: A systematic review

Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept proposed by the World Health Organization (WHO). Water and wastewater are among the most important environmental media of AR sources, where the phenomena are generally non-linear. Therefore, the aim of this study was to investigate the application of machine learning-based methods (MLMs) to solve AR-induced problems in water and wastewater. For this purpose, most relevant databases were searched in the period between 1987 and 2023 to systematically analyze and categorize the applications. Accordingly, the results showed that out of 12 applications, 11 (91.6%) were for shallow learning and 1 (8.3%) for deep learning. In shallow learning category, n = 6, 50% of the applications were regression and n = 4, 33.3% were classification, mainly using artificial neural networks, decision trees and Bayesian methods for the following objectives: Predicting the survival of antibiotic-resistant bacteria (ARB), determining the order of influencing parameters on AR-based scores, and identifying the major sources of antibiotic resistance genes (ARGs). In addition, only one study (8.3%) was found for clustering and no study for association. Surprisingly, deep learning had been used in only one study (8.3%) to predict ARGs sequences. Therefore, working on the knowledge gaps of AR, especially using clustering, association and deep learning methods, would be a promising option to analyze more aspects of the related problems. However, there is still a long way to go to consider and apply MLMs as unique approaches to study different aspects of AR in water and wastewater.

Parity in bacterial communities and resistomes: Microplastic and natural organic particles in the Tyrrhenian Sea

Petroleum-based microplastic particles (MPs) are carriers of antimicrobial resistance genes (ARGs) in aquatic environments, influencing the selection and spread of antimicrobial resistance. This research characterized MP and natural organic particle (NOP) bacterial communities and resistomes in the Tyrrhenian Sea, a region impacted by plastic pollution and climate change. MP and NOP bacterial communities were similar but different from the free-living planktonic communities. Likewise, MP and NOP ARG abundances were similar but different (higher) from the planktonic communities. MP and NOP metagenome-assembled genomes contained ARGs associated with mobile genetic elements and exhibited co-occurrence with metal resistance genes. Overall, these findings show that MPs and NOPs harbor potential pathogenic and antimicrobial resistant bacteria, which can aid in the spread of antimicrobial resistance. Further, petroleum-based MPs do not represent novel ecological niches for allochthonous bacteria; rather, they synergize with NOPs, collectively facilitating the spread of antimicrobial resistance in marine ecosystems.

Landscape of global urban environmental resistome and its association with local socioeconomic and medical status

Antimicrobial resistance (AMR) poses a critical threat to global health and development, with environmental factors-particularly in urban areas-contributing significantly to the spread of antibiotic resistance genes (ARGs). However, most research to date has been conducted at a local level, leaving significant gaps in our understanding of the global status of antibiotic resistance in urban environments. To address this issue, we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples, which were collected by the MetaSUB International Consortium involving diverse urban environments in 60 cities of 27 countries, utilizing a deep-learning based methodology. Our findings demonstrated the strong geographical specificity of urban environmental resistome, and their correlation with various local socioeconomic and medical conditions. We also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters (BGCs) across different countries, and discovered that the urban environment represents a rich source of novel antibiotics. Our study provides a comprehensive overview of the global urban environmental resistome, and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare's Future

Antibiotic resistance poses a significant threat to global public health due to complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies to address this crisis. For example, AI can analyze genomic data to detect resistance markers early on, enabling early interventions. In addition, AI-powered decision support systems can optimize antibiotic use by recommending the most effective treatments based on patient data and local resistance patterns. AI can accelerate drug discovery by predicting the efficacy of new compounds and identifying potential antibacterial agents. Although progress has been made, challenges persist, including data quality, model interpretability, and real-world implementation. A multidisciplinary approach that integrates AI with other emerging technologies, such as synthetic biology and nanomedicine, could pave the way for effective prevention and mitigation of antimicrobial resistance, preserving the efficacy of antibiotics for future generations.

Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework

High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protein functions to novel gene sequences remain a challenge. To this end, we developed FunGeneTyper, an extensible framework with two new deep learning models (i.e., FunTrans and FunRep), structured databases, and supporting resources for achieving highly accurate (Accuracy > 0.99, F1-score > 0.97) and fine-grained classification of antibiotic resistance genes (ARGs) and virulence factor genes. Using an experimentally confirmed dataset of ARGs comprising remote homologous sequences as the test set, our framework achieves by-far-the-best performance in the discovery of new ARGs from human gut (F1-score: 0.6948), wastewater (0.6072), and soil (0.5445) microbiomes, beating the state-of-the-art bioinformatics tools and sequence alignment-based (F1-score: 0.0556-0.5065) and domain-based (F1-score: 0.2630-0.5224) annotation approaches. Furthermore, our framework is implemented as a lightweight, privacy-preserving, and plug-and-play neural network module, facilitating its versatility and accessibility to developers and users worldwide. We anticipate widespread utilization of FunGeneTyper (https://github.com/emblab-westlake/FunGeneTyper) for precise classification of protein-coding gene functions and the discovery of numerous valuable enzymes. This advancement will have a significant impact on various fields, including microbiome research, biotechnology, metagenomics, and bioinformatics.

A one health approach for monitoring antimicrobial resistance: developing a national freshwater pilot effort

Antimicrobial resistance (AMR) is a world-wide public health threat that is projected to lead to 10 million annual deaths globally by 2050. The AMR public health issue has led to the development of action plans to combat AMR, including improved antimicrobial stewardship, development of new antimicrobials, and advanced monitoring. The National Antimicrobial Resistance Monitoring System (NARMS) led by the United States (U.S) Food and Drug Administration along with the U.S. Centers for Disease Control and U.S. Department of Agriculture has monitored antimicrobial resistant bacteria in retail meats, humans, and food animals since the mid 1990's. NARMS is currently exploring an integrated One Health monitoring model recognizing that human, animal, plant, and environmental systems are linked to public health. Since 2020, the U.S. Environmental Protection Agency has led an interagency NARMS environmental working group (EWG) to implement a surface water AMR monitoring program (SWAM) at watershed and national scales. The NARMS EWG divided the development of the environmental monitoring effort into five areas: (i) defining objectives and questions, (ii) designing study/sampling design, (iii) selecting AMR indicators, (iv) establishing analytical methods, and (v) developing data management/analytics/metadata plans. For each of these areas, the consensus among the scientific community and literature was reviewed and carefully considered prior to the development of this environmental monitoring program. The data produced from the SWAM effort will help develop robust surface water monitoring programs with the goal of assessing public health risks associated with AMR pathogens in surface water (e.g., recreational water exposures), provide a comprehensive picture of how resistant strains are related spatially and temporally within a watershed, and help assess how anthropogenic drivers and intervention strategies impact the transmission of AMR within human, animal, and environmental systems.

Patient-derived pathogenic microbe deposition enhances exposure risk in pediatric clinics

Healthcare-associated infections (HAIs) pose significant risks to pediatric patients in outpatient settings. To prevent HAIs, understanding the sources and transmission routes of pathogenic microorganisms is crucial. This study aimed to identify the sources of opportunistic bacterial pathogens (OBPs) in pediatric outpatient settings and determine their transmission routes. Furthermore, assessing the public health risks associated with the core OBPs is important. We collected 310 samples from various sites in pediatric outpatient areas and quantified the bacteria using qPCR and CFU counting. We also performed 16S rRNA gene and single-bacterial whole-genome sequencing to profile the transmission routes and antibiotic resistance characteristics of OBPs. We observed significant variations in microbial diversity and composition among sampling sites in pediatric outpatient settings, with active communication of the microbiota between linked areas. We found that the primary source of OBPs in multi-person contact areas was the hand surface, particularly in pediatric patients. Five core OBPs, Staphylococcus epidermidis, Acinetobacter baumannii, Pseudomonas aeruginosa, Streptococcus mitis, and Streptococcus oralis, were mainly derived from pediatric patients and spread into the environment. These OBPs accumulated at multi-person contact sites, resulting in high microbial diversity in these areas. Transmission tests confirmed the challenging spread of these pathogens, with S. epidermidis transferring from the patient's hand to the environment, leading to an increased abundance and emergence of related strains. More importantly, S. epidermidis isolated from pediatric patients carried more antibiotic-resistance genes. In addition, two strains of multidrug-resistant A. baumannii were isolated from both a child and a parent, confirming the transmission of the five core OBPs centered around pediatric patients and multi-person contact areas. Our results demonstrate that pediatric patients serve as a significant source of OBPs in pediatric outpatient settings. OBPs carried by pediatric patients pose a high public health risk. To effectively control HAIs, increasing hand hygiene measures in pediatric patients and enhancing the frequency of disinfection in multi-person contact areas remains crucial. By targeting these preventive measures, the spread of OBPs can be reduced, thereby mitigating the risk of HAIs in pediatric outpatient settings.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

To what extent do water reuse treatments reduce antibiotic resistance indicators? A comparison of two full-scale systems

Water reuse is an essential strategy for reducing water demand from conventional sources, alleviating water stress, and promoting sustainability, but understanding the effectiveness of associated treatment processes as barriers to the spread of antibiotic resistance is an important consideration to protecting human health. We comprehensively evaluated the reduction of antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARB) in two field-operational water reuse systems with distinct treatment trains, one producing water for indirect potable reuse (ozone/biologically-active carbon/granular activated carbon) and the other for non-potable reuse (denitrification-filtration/chlorination) using metagenomic sequencing and culture. Relative abundances of total ARGs/clinically-relevant ARGs and cultured ARB were reduced by several logs during primary and secondary stages of wastewater treatment, but to a lesser extent during the tertiary water reuse treatments. In particular, ozonation tended to enrich multi-drug ARGs. The effect of chlorination was facility-dependent, increasing the relative abundance of ARGs when following biologically-active carbon filters, but generally providing a benefit in reduced bacterial numbers and ecological and human health resistome risk scores. Relative abundances of total ARGs and resistome risk scores were lowest in aquifer samples, although resistant Escherichia coli and Klebsiella pneumoniae were occasionally detected in the monitoring well 3-days downgradient from injection, but not 6-months downgradient. Resistant E. coli and Pseudomonas aeruginosa were occasionally detected in the nonpotable reuse distribution system, along with increased levels of multidrug, sulfonamide, phenicol, and aminoglycoside ARGs. This study illuminates specific vulnerabilities of water reuse systems to persistence, selection, and growth of ARGs and ARB and emphasizes the role of multiple treatment barriers, including aquifers and distribution systems.

Microbial communities colonising plastics during transition from the wastewater treatment plant to marine waters

BACKGROUND

Plastics pollution and antimicrobial resistance (AMR) are two major environmental threats, but potential connections between plastic associated biofilms, the 'plastisphere', and dissemination of AMR genes are not well explored.

RESULTS

We conducted mesocosm experiments tracking microbial community changes on plastic surfaces transitioning from wastewater effluent to marine environments over 16 weeks. Commonly used plastics, polypropylene (PP), high density polyethylene (HDPE), low density polyethylene (LDPE) and polyethylene terephthalate (PET) incubated in wastewater effluent, river water, estuarine water, and in the seawater for 16 weeks, were analysed via 16S rRNA gene amplicon and shotgun metagenome sequencing. Within one week, plastic-colonizing communities shifted from wastewater effluent-associated microorganisms to marine taxa, some members of which (e.g. Oleibacter-Thalassolituus and Sphingomonas spp., on PET, Alcanivoracaceae on PET and PP, or Oleiphilaceae, on all polymers), were selectively enriched from levels undetectable in the starting communities. Remarkably, microbial biofilms were also susceptible to parasitism, with Saprospiraceae feeding on biofilms at late colonisation stages (from week 6 onwards), while Bdellovibrionaceae were prominently present on HDPE from week 2 and LDPE from day 1. Relative AMR gene abundance declined over time, and plastics did not become enriched for key AMR genes after wastewater exposure.

CONCLUSION

Although some resistance genes occurred during the mesocosm transition on plastic substrata, those originated from the seawater organisms. Overall, plastic surfaces incubated in wastewater did not act as hotspots for AMR proliferation in simulated marine environments.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

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

Despite an increasingly detailed picture of the molecular mechanisms of bacteriophage (phage)-bacterial interactions, we lack an understanding of how these interactions evolve and impact disease within patients. In this work, we report a year-long, nationwide study of diarrheal disease patients in Bangladesh. Among cholera patients, we quantified Vibrio cholerae (prey) and its virulent phages (predators) using metagenomics and quantitative polymerase chain reaction 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 antiphage defenses, predation was "effective," with a high predator:prey ratio that correlated with increased genetic diversity among the prey. In the presence of antiphage 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.

A comparative genomics study of the microbiome and freshwater resistome in Southern Pantanal

This study explores the resistome and bacterial diversity of two small lakes in the Southern Pantanal, one in Aquidauana sub-region, close to a farm, and one in Abobral sub-region, an environmentally preserved area. Shotgun metagenomic sequencing data from water column samples collected near and far from the floating macrophyte Eichhornia crassipes were used. The Abobral small lake exhibited the highest diversity and abundance of antibiotic resistance genes (ARGs), antibiotic resistance classes (ARGCs), phylum, and genus. RPOB2 and its resistance class, multidrug resistance, were the most abundant ARG and ARGC, respectively. Pseudomonadota was the dominant phylum across all sites, and Streptomyces was the most abundant genus considering all sites.

Nitrogen and phosphorus limitations promoted bacterial nitrate metabolism and propagation of antibiotic resistome in the phycosphere of Auxenochlorella pyrenoidosa

Despite that nitrogen (N) and phosphorus (P) play critical roles in the lifecycle of microalgae, how N and P further affect the distribution of bacteria and antibiotic resistance genes (ARGs) in the phycosphere is still poorly understood. In this study, the effects of N and P on the distribution of ARGs in the phycosphere of Auxenochlorella pyrenoidosa were investigated. Results showed that the growth and chlorophyll synthesis of microalgae were inhibited when N or P was limited, regardless of the N/P ratios, but the extracellular polymeric substances content and nitrate assimilation efficiency were enhanced in contrast. Metagenomic sequencing revealed that N or P limitation resulted in the recruitment of specific bacteria that highly contribute to the nitrate metabolism in the phycosphere. Besides, N or P limitation promoted the propagation of phycosphere ARGs, primarily through horizontal gene transfer mediated by mobile genetic elements. The enrichment of specific bacteria induced by changes in the algal physiology also contributed to the ARGs proliferation under nutrient limitation. Our results demonstrated that the reduction of algal cells caused by nutrient limitation could promote the propagation of ARGs, which provides new insights into the occurrence and spread of ARGs in the phycosphere.

Machine learning for microbiologists

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.

Deep learning methods in metagenomics: a review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

Causal-ARG: a causality-guided framework for annotating properties of antibiotic resistance genes

MOTIVATION

The crisis of antibiotic resistance, which causes antibiotics used to treat bacterial infections to become less effective, has emerged as one of the foremost challenges to public health. Identifying the properties of antibiotic resistance genes (ARGs) is an essential way to mitigate this issue. Although numerous methods have been proposed for this task, most of these approaches concentrate solely on predicting antibiotic class, disregarding other important properties of ARGs. In addition, existing methods for simultaneously predicting multiple properties of ARGs fail to account for the causal relationships among these properties, limiting the predictive performance.

RESULTS

In this study, we propose a causality-guided framework for annotating properties of ARGs, in which causal inference is utilized for representation learning. More specifically, the hidden biological patterns determining the properties of ARGs are described by a Gaussian Mixture Model, and procedure of causal representation learning is used to derive the hidden features. In addition, a causal graph among different properties is constructed to capture the causal relationships among properties of ARGs, which is integrated into the task of annotating properties of ARGs. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework on the task of annotating properties of ARGs.

AVAILABILITY AND IMPLEMENTATION

The data and source codes are available in GitHub at https://github.com/David-WZhao/CausalARG.

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.