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908300.9921ARGNet: 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.202438725076
509810.9906Feature selection and aggregation for antibiotic resistance GWAS in Mycobacterium tuberculosis: a comparative study. INTRODUCTION: Drug resistance (DR) of pathogens remains a global healthcare concern. In contrast to other bacteria, acquiring mutations in the core genome is the main mechanism of drug resistance for Mycobacterium tuberculosis (MTB). For some antibiotics, the resistance of a particular isolate can be reliably predicted by identifying specific mutations, while for other antibiotics the knowledge of resistance mechanisms is limited. Statistical machine learning (ML) methods are used to infer new genes implicated in drug resistance leveraging large collections of isolates with known whole-genome sequences and phenotypic states for different drugs. However, high correlations between the phenotypic states for commonly used drugs complicate the inference of true associations of mutations with drug phenotypes by ML approaches. METHODS: Recently, several new methods have been developed to select a small subset of reliable predictors of the dependent variable, which may help reduce the number of spurious associations identified. In this study, we evaluated several such methods, namely, logistic regression with different regularization penalty functions, a recently introduced algorithm for solving the best-subset selection problem (ABESS) and "Hungry, Hungry SNPos" (HHS) a heuristic algorithm specifically developed to identify resistance-associated genetic variants in the presence of resistance co-occurrence. We assessed their ability to select known causal mutations for resistance to a specific drug while avoiding the selection of mutations in genes associated with resistance to other drugs, thus we compared selected ML models for their applicability for MTB genome wide association studies. RESULTS AND DISCUSSION: In our analysis, ABESS significantly outperformed the other methods, selecting more relevant sets of mutations. Additionally, we demonstrated that aggregating rare mutations within protein-coding genes into markers indicative of changes in PFAM domains improved prediction quality, and these markers were predominantly selected by ABESS, suggesting their high informativeness. However, ABESS yielded lower prediction accuracy compared to logistic regression methods with regularization.202540606161
668620.9906The Impact of Wastewater on Antimicrobial Resistance: A Scoping Review of Transmission Pathways and Contributing Factors. BACKGROUND/OBJECTIVES: Antimicrobial resistance (AMR) is a global issue driven by the overuse of antibiotics in healthcare, agriculture, and veterinary settings. Wastewater and treatment plants (WWTPs) act as reservoirs for antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). The One Health approach emphasizes the interconnectedness of human, animal, and environmental health in addressing AMR. This scoping review analyzes wastewater's role in the AMR spread, identifies influencing factors, and highlights research gaps to guide interventions. METHODS: This scoping review followed the PRISMA-ScR guidelines. A comprehensive literature search was conducted across the PubMed and Web of Science databases for articles published up to June 2024, supplemented by manual reference checks. The review focused on wastewater as a source of AMR, including hospital effluents, industrial and urban sewage, and agricultural runoff. Screening and selection were independently performed by two reviewers, with conflicts resolved by a third. RESULTS: Of 3367 studies identified, 70 met the inclusion criteria. The findings indicated that antibiotic residues, heavy metals, and microbial interactions in wastewater are key drivers of AMR development. Although WWTPs aim to reduce contaminants, they often create conditions conducive to horizontal gene transfer, amplifying resistance. Promising interventions, such as advanced treatment methods and regulatory measures, exist but require further research and implementation. CONCLUSIONS: Wastewater plays a pivotal role in AMR dissemination. Targeted interventions in wastewater management are essential to mitigate AMR risks. Future studies should prioritize understanding AMR dynamics in wastewater ecosystems and evaluating scalable mitigation strategies to support global health efforts.202540001375
658230.9906Effective Treatment Strategies for the Removal of Antibiotic-Resistant Bacteria, Antibiotic-Resistance Genes, and Antibiotic Residues in the Effluent From Wastewater Treatment Plants Receiving Municipal, Hospital, and Domestic Wastewater: Protocol for a Systematic Review. BACKGROUND: The widespread and unrestricted use of antibiotics has led to the emergence and spread of antibiotic-resistant bacteria (ARB), antibiotic-resistance genes (ARGs), and antibiotic residues in the environment. Conventional wastewater treatment plants (WWTPs) are not designed for effective and adequate removal of ARB, ARGs, and antibiotic residues, and therefore, they play an important role in the dissemination of antimicrobial resistance (AMR) in the natural environment. OBJECTIVE: We will conduct a systematic review to determine the most effective treatment strategies for the removal of ARB, ARGs, and antibiotic residues from the treated effluent disposed into the environment from WWTPs that receive municipal, hospital, and domestic discharge. METHODS: We will search the MEDLINE, EMBASE, Web of Science, World Health Organization Global Index Medicus, and ProQuest Environmental Science Collection databases for full-text peer-reviewed journal articles published between January 2001 and December 2020. We will select only articles published in the English language. We will include studies that measured (1) the presence, concentration, and removal rate of ARB/ARGs going from WWTP influent to effluent, (2) the presence, concentration, and types of antibiotics in the effluent, and (3) the possible selection of ARB in the effluent after undergoing treatment processes in WWTPs. At least two independent reviewers will extract data and perform risk of bias assessment. An acceptable or narrative synthesis method will be followed to synthesize the data and present descriptive characteristics of the included studies in a tabular form. The study has been approved by the Ethics Review Board at the International Centre for Diarrhoeal Disease Research, Bangladesh (protocol number: PR-20113). RESULTS: This protocol outlines our proposed methodology for conducting a systematic review. Our results will provide an update to the existing literature by searching additional databases. CONCLUSIONS: Findings from our systematic review will inform the planning of proper treatment methods that can effectively reduce the levels of ARB, ARGs, and residual antibiotics in effluent, thus lowering the risk of the environmental spread of AMR and its further transmission to humans and animals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/33365.202134842550
650640.9906Mitigating antimicrobial resistance through effective hospital wastewater management in low- and middle-income countries. Hospital wastewater (HWW) is a significant environmental and public health threat, containing high levels of pollutants such as antibiotic-resistant bacteria (ARB), antibiotic-resistant genes (ARGs), antibiotics, disinfectants, and heavy metals. This threat is of particular concern in low- and middle-income countries (LMICs), where untreated effluents are often used for irrigating vegetables crops, leading to direct and indirect human exposure. Despite being a potential hotspot for the spread of antimicrobial resistance (AMR), existing HWW treatment systems in LMICs primarily target conventional pollutants and lack effective standards for monitoring the removal of ARB and ARGs. Consequently, untreated or inadequately treated HWW continues to disseminate ARB and ARGs, exacerbating the risk of AMR proliferation. Addressing this requires targeted interventions, including cost-effective treatment solutions, robust AMR monitoring protocols, and policy-driven strategies tailored to LMICs. This perspective calls for a paradigm shift in HWW management in LMIC, emphasizing the broader implementation of onsite treatment systems, which are currently rare. Key recommendations include developing affordable and contextually adaptable technologies for eliminating ARB and ARGs and enforcing local regulations for AMR monitoring and control in wastewater. Addressing these challenges is essential for protecting public health, preventing the environmental spread of resistance, and contributing to a global effort to preserve the efficacy of antibiotics. Recommendations include integrating scalable onsite technologies, leveraging local knowledge, and implementing comprehensive AMR-focused regulatory frameworks.202439944563
511850.9906Automated extraction of genes associated with antibiotic resistance from the biomedical literature. The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction.202235134132
713160.9906Longitudinal study of the short- and long-term effects of hospitalisation and oral trimethoprim-sulfadiazine administration on the equine faecal microbiome and resistome. BACKGROUND: Hospitalisation and antimicrobial treatment are common in horses and significantly impact the intestinal microbiota. Antimicrobial treatment might also increase levels of resistant bacteria in faeces, which could spread to other ecological compartments, such as the environment, other animals and humans. In this study, we aimed to characterise the short- and long-term effects of transportation, hospitalisation and trimethoprim-sulfadiazine (TMS) administration on the faecal microbiota and resistome of healthy equids. METHODS: In a longitudinal experimental study design, in which the ponies served as their own control, faecal samples were collected from six healthy Welsh ponies at the farm (D0-D13-1), immediately following transportation to the hospital (D13-2), during 7 days of hospitalisation without treatment (D14-D21), during 5 days of oral TMS treatment (D22-D26) and after discharge from the hospital up to 6 months later (D27-D211). After DNA extraction, 16S rRNA gene sequencing was performed on all samples. For resistome analysis, shotgun metagenomic sequencing was performed on selected samples. RESULTS: Hospitalisation without antimicrobial treatment did not significantly affect microbiota composition. Oral TMS treatment reduced alpha-diversity significantly. Kiritimatiellaeota, Fibrobacteres and Verrucomicrobia significantly decreased in relative abundance, whereas Firmicutes increased. The faecal microbiota composition gradually recovered after discontinuation of TMS treatment and discharge from the hospital and, after 2 weeks, was more similar to pre-treatment composition than to composition during TMS treatment. Six months later, however, microbiota composition still differed significantly from that at the start of the study and Spirochaetes and Verrucomicrobia were less abundant. TMS administration led to a significant (up to 32-fold) and rapid increase in the relative abundance of resistance genes sul2, tetQ, ant6-1a, and aph(3")-lb. lnuC significantly decreased directly after treatment. Resistance genes sul2 (15-fold) and tetQ (six-fold) remained significantly increased 6 months later. CONCLUSIONS: Oral treatment with TMS has a rapid and long-lasting effect on faecal microbiota composition and resistome, making the equine hindgut a reservoir and potential source of resistant bacteria posing a risk to animal and human health through transmission. These findings support the judicious use of antimicrobials to minimise long-term faecal presence, excretion and the spread of antimicrobial resistance in the environment. Video Abstract.202336850017
658170.9905Do wastewater treatment plants increase antibiotic resistant bacteria or genes in the environment? Protocol for a systematic review. BACKGROUND: Antibiotic resistance is a global public health threat. Water from human activities is collected at wastewater treatment plants where processes often do not sufficiently neutralize antibiotic resistant bacteria and genes, which are further shed into the local environment. This protocol outlines the steps to conduct a systematic review based on the Population, Exposure, Comparator and Outcome (PECO) framework, aiming at answering the question "Are antimicrobial-resistant enterobacteriaceae and antimicrobial resistance genes present (O) in air and water samples (P) taken either near or downstream or downwind or down-gradient from wastewater treatment plants (E), as compared to air and water samples taken either further away or upstream or upwind or up-gradient from such wastewater treatment plant (C)?" Presence of antimicrobial-resistant bacteria and genes will be quantitatively measured by extracting their prevalence or concentration, depending on the reviewed study. METHODS: We will search PubMed, EMBASE, the Cochrane database and Web of Science for original articles published from 1 Jan 2000 to 3 Sep 2018 with language restriction. Articles will undergo a relevance and a design screening process. Data from eligible articles will be extracted by two independent reviewers. Further, we will perform a risk of bias assessment using a decision matrix. We will synthesize and present results in narrative and tabular form and will perform a meta-analysis if heterogeneity of results allows it. DISCUSSION: Antibiotic resistance in environmental samples around wastewater treatment plants may pose a risk of exposure to workers and nearby residents. Results from the systematic review outlined in this protocol will allow to estimate the extend of exposure, to inform policy making and help to design future studies.201931806019
679480.9905Beyond cyanotoxins: increased Legionella, antibiotic resistance genes in western Lake Erie water and disinfection-byproducts in their finished water. BACKGROUND: Western Lake Erie is suffering from harmful cyanobacterial blooms, primarily toxic Microcystis spp., affecting the ecosystem, water safety, and the regional economy. Continued bloom occurrence has raised concerns about public health implications. However, there has been no investigation regarding the potential increase of Legionella and antibiotic resistance genes in source water, and disinfection byproducts in municipal treated drinking water caused by these bloom events. METHODS: Over 2 years, source water (total n = 118) and finished water (total n = 118) samples were collected from drinking water plants situated in western Lake Erie (bloom site) and central Lake Erie (control site). Bloom-related parameters were determined, such as microcystin (MC), toxic Microcystis, total organic carbon, N, and P. Disinfection byproducts (DBPs) [total trihalomethanes (THMs) and haloacetic acids (HAAs)] were assessed in finished water. Genetic markers for Legionella, antibiotic resistance genes, and mobile genetic elements were quantified in source and finished waters. RESULTS: Significantly higher levels of MC-producing Microcystis were observed in the western Lake Erie site compared to the control site. Analysis of DBPs revealed significantly elevated THMs concentrations at the bloom site, while HAAs concentrations remained similar between the two sites. Legionella spp. levels were significantly higher in the bloom site, showing a significant relationship with total cyanobacteria. Abundance of ARGs (tetQ and sul1) and mobile genetic elements (MGEs) were also significantly higher at the bloom site. DISCUSSION: Although overall abundance decreased in finished water, relative abundance of ARGs and MGE among total bacteria increased after treatment, particularly at the bloom site. The findings underscore the need for ongoing efforts to mitigate bloom frequency and intensity in the lake. Moreover, optimizing water treatment processes during bloom episodes is crucial to maintain water quality. The associations observed between bloom conditions, ARGs, and Legionella, necessitate future investigations into the potential enhancement of antibiotic-resistant bacteria and Legionella spp. due to blooms, both in lake environments and drinking water distribution systems.202337700867
516790.9905Decreased Antimicrobial Resistance Gene Richness Following Fecal Microbiota, Live-jslm (REBYOTA®) Administration: Post Hoc Analysis of PUNCH CD3. BACKGROUND: The human gastrointestinal microbiome helps maintain vital functions related to overall health, including resistance to pathogen colonization. Disruption of the microbiome, leading to loss of colonization resistance, can be caused by multiple factors, including antimicrobial use. The loss of colonization resistance may lead to establishment or proliferation of opportunistic bacteria that carry genes associated with antimicrobial resistance, potentially increasing the risk of infection by such antimicrobial-resistant bacteria. A potential approach to mitigating this risk involves restoration of healthier microbiota and pathogen colonization resistance. METHODS: A metagenomic sequencing method was used to conduct a post hoc analysis of antibiotic resistance gene richness among fecal samples from participants administered fecal microbiota, live-jslm (REBYOTA; abbreviated as RBL) or placebo in the PUNCH CD3 study (NCT03244644) for the prevention of recurrent Clostridioides difficile infection. RESULTS: At baseline, participants had higher antibiotic resistance gene richness than a representative healthy cohort. Over time, RBL responders had lower antibiotic resistance gene richness at the class, group, and mechanism levels as compared with placebo responders. These differences were evident as early as 1 week after administration and sustained for at least 6 months. RBL responders also had decreased richness of antibiotic resistance genes deemed high risk based on designated bacterial public health threats. CONCLUSIONS: These data support a model in which microbiota-based products, including RBL, may reduce antibiotic resistance gene richness, thereby possibly reducing the risk of antimicrobial-resistant organism infection. TRIAL REGISTRATION: NCT03244644 (https://clinicaltrials.gov/study/NCT03244644; 9 August 2017).202540672762
9082100.9904GeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure. BACKGROUND: Horizontal gene transfer contributes to bacterial evolution through mobilising genes across various taxonomical boundaries. It is frequently mediated by mobile genetic elements (MGEs), which may capture, maintain, and rearrange mobile genes and co-mobilise them between bacteria, causing horizontal gene co-transfer (HGcoT). This physical linkage between mobile genes poses a great threat to public health as it facilitates dissemination and co-selection of clinically important genes amongst bacteria. Although rapid accumulation of bacterial whole-genome sequencing data since the 2000s enables study of HGcoT at the population level, results based on genetic co-occurrence counts and simple association tests are usually confounded by bacterial population structure when sampled bacteria belong to the same species, leading to spurious conclusions. RESULTS: We have developed a network approach to explore WGS data for evidence of intraspecies HGcoT and have implemented it in R package GeneMates ( github.com/wanyuac/GeneMates ). The package takes as input an allelic presence-absence matrix of interested genes and a matrix of core-genome single-nucleotide polymorphisms, performs association tests with linear mixed models controlled for population structure, produces a network of significantly associated alleles, and identifies clusters within the network as plausible co-transferred alleles. GeneMates users may choose to score consistency of allelic physical distances measured in genome assemblies using a novel approach we have developed and overlay scores to the network for further evidence of HGcoT. Validation studies of GeneMates on known acquired antimicrobial resistance genes in Escherichia coli and Salmonella Typhimurium show advantages of our network approach over simple association analysis: (1) distinguishing between allelic co-occurrence driven by HGcoT and that driven by clonal reproduction, (2) evaluating effects of population structure on allelic co-occurrence, and (3) direct links between allele clusters in the network and MGEs when physical distances are incorporated. CONCLUSION: GeneMates offers an effective approach to detection of intraspecies HGcoT using WGS data.202032972363
9079110.9904Review, Evaluation, and Directions for Gene-Targeted Assembly for Ecological Analyses of Metagenomes. Shotgun metagenomics has greatly advanced our understanding of microbial communities over the last decade. Metagenomic analyses often include assembly and genome binning, computationally daunting tasks especially for big data from complex environments such as soil and sediments. In many studies, however, only a subset of genes and pathways involved in specific functions are of interest; thus, it is not necessary to attempt global assembly. In addition, methods that target genes can be computationally more efficient and produce more accurate assembly by leveraging rich databases, especially for those genes that are of broad interest such as those involved in biogeochemical cycles, biodegradation, and antibiotic resistance or used as phylogenetic markers. Here, we review six gene-targeted assemblers with unique algorithms for extracting and/or assembling targeted genes: Xander, MegaGTA, SAT-Assembler, HMM-GRASPx, GenSeed-HMM, and MEGAN. We tested these tools using two datasets with known genomes, a synthetic community of artificial reads derived from the genomes of 17 bacteria, shotgun sequence data from a mock community with 48 bacteria and 16 archaea genomes, and a large soil shotgun metagenomic dataset. We compared assemblies of a universal single copy gene (rplB) and two N cycle genes (nifH and nirK). We measured their computational efficiency, sensitivity, specificity, and chimera rate and found Xander and MegaGTA, which both use a probabilistic graph structure to model the genes, have the best overall performance with all three datasets, although MEGAN, a reference matching assembler, had better sensitivity with synthetic and mock community members chosen from its reference collection. Also, Xander and MegaGTA are the only tools that include post-assembly scripts tuned for common molecular ecology and diversity analyses. Additionally, we provide a mathematical model for estimating the probability of assembling targeted genes in a metagenome for estimating required sequencing depth.201931749830
5097120.9903Comparing Graph Sample and Aggregation (SAGE) and Graph Attention Networks in the Prediction of Drug-Gene Associations of Extended-Spectrum Beta-Lactamases in Periodontal Infections and Resistance. INTRODUCTION: Gram-negative bacteria exhibit more antibiotic resistance than gram-positive bacteria due to their cell wall structure and composition differences. Porins, or protein channels in these bacteria, can allow small, hydrophilic antibiotics to diffuse, affecting their susceptibility. Mutations in porin protein genes can also impair antibiotic entry. Predicting drug-gene associations of extended-spectrum beta-lactamases (ESBLs) is crucial as they confer resistance to beta-lactam antibiotics, challenging the treatment of infections. This aids clinicians in selecting suitable treatments, optimizing drug usage, enhancing patient outcomes, and controlling antibiotic resistance in healthcare settings. Graph-based neural networks can predict drug-gene associations in periodontal infections and resistance. The aim of the study was to predict drug-gene associations of ESBLs in periodontal infections and resistance. METHODS: The study focuses on analyzing drug-gene associations using probes and drugs. The data was converted into graph language, assigning nodes and edges for drugs and genes. Graph neural networks (GNNs) and similar algorithms were implemented using Google Colab and Python. Cytoscape and CytoHubba are open-source software platforms used for network analysis and visualization. GNNs were used for tasks like node classification, link prediction, and graph-level prediction. Three graph-based models were used: graph convolutional network (GCN), Graph SAGE, and graph attention network (GAT). Each model was trained for 200 epochs using the Adam optimizer with a learning rate of 0.01 and a weight decay of 5e-4. RESULTS: The drug-gene association network has 57 nodes, 79 edges, and a 2.730 characteristic path length. Its structure, organization, and connectivity are analyzed using the GCN and Graph SAGE, which show high accuracy, precision, recall, and an F1-score of 0.94. GAT's performance metrics are lower, with an accuracy of 0.68, precision of 0.47, recall of 0.68, and F1-score of 0.56, suggesting that it may not be as effective in capturing drug-gene relationships. CONCLUSION: Compared to ESBLs, both GCN and Graph SAGE demonstrate excellent performance with accuracy, precision, recall, and an F1-score of 0.94. These results indicate that GCN and Graph SAGE are highly effective in predicting drug-gene associations related to ESBLs. GCN and Graph SAGE outperform GAT in predicting drug-gene associations for ESBLs. Improvements include data augmentation, regularization, and cross-validation. Ethical considerations, fairness, and open-source implementations are crucial for future research in precision periodontal treatment.202439347119
7080130.9903Antibiotics, bacteria, and antibiotic resistance genes: aerial transport from cattle feed yards via particulate matter. BACKGROUND: Emergence and spread of antibiotic resistance has become a global health threat and is often linked with overuse and misuse of clinical and veterinary chemotherapeutic agents. Modern industrial-scale animal feeding operations rely extensively on veterinary pharmaceuticals, including antibiotics, to augment animal growth. Following excretion, antibiotics are transported through the environment via runoff, leaching, and land application of manure; however, airborne transport from feed yards has not been characterized. OBJECTIVES: The goal of this study was to determine the extent to which antibiotics, antibiotic resistance genes (ARG), and ruminant-associated microbes are aerially dispersed via particulate matter (PM) derived from large-scale beef cattle feed yards. METHODS: PM was collected downwind and upwind of 10 beef cattle feed yards. After extraction from PM, five veterinary antibiotics were quantified via high-performance liquid chromatography with tandem mass spectrometry, ARG were quantified via targeted quantitative polymerase chain reaction, and microbial community diversity was analyzed via 16S rRNA amplification and sequencing. RESULTS: Airborne PM derived from feed yards facilitated dispersal of several veterinary antibiotics, as well as microbial communities containing ARG. Concentrations of several antibiotics in airborne PM immediately downwind of feed yards ranged from 0.5 to 4.6 μg/g of PM. Microbial communities of PM collected downwind of feed yards were enriched with ruminant-associated taxa and were distinct when compared to upwind PM assemblages. Furthermore, genes encoding resistance to tetracycline antibiotics were significantly more abundant in PM collected downwind of feed yards as compared to upwind. CONCLUSIONS: Wind-dispersed PM from feed yards harbors antibiotics, bacteria, and ARGs.201525633846
3548140.9903From flagellar assembly to DNA replication: CJSe's role in mitigating microbial antibiotic resistance genes. The emergence of Antibiotic Resistance Genes (ARGs) in Campylobacter jejuni (CJ) poses a severe threat to food safety and human health. However, the specific impact of CJ and its variants on ARGs and other related factors remains to be further elucidated. Herein, integrated metagenomic sequencing and co-occurrence network analysis approach were employed to investigate the impact of CJ and CJ incorporated with biogenic selenium (CJSe) on ARGs, flagellar assembly pathways, microbial communities, and DNA replication pathways in chicken manure. Compared to the Control (CON) and CJ groups, the CJSe group exhibited 2.4-fold increase selenium levels (P < 0.01) in chicken manure. Notable differences were also observed between the CJ and CJSe groups, with sequence results showing a CJ > CJSe > CON trend in total ARG copy numbers. Furthermore, the CJSe group showed 31.6 % fewer flagellar assembly genes compared to the CJ group. Additionally, compared to the CJ group, CJSe inhibited pathways such as basal body/hook (e.g., FliH, FliO, FliQ reduced by 25-52 %) and stator (MotB downregulated by 42.3 %), suppressing flagellar assembly. We also found that both CJ and CJSe influenced bacterial DNA replication pathways, with the former increasing ARG-carrying bacteria and the latter, under selenium-induced selective pressure, reducing ARG-carrying bacteria. Moreover, compared to the CJ group, the CJSe group showed a significantly lower 9.72 % copy number of total archaeal DNA replication genes. Furthermore, through intricate co-occurrence network analysis, we discovered the complex interplay between changes in ARGs and bacterial and archaeal DNA replication dynamics within the microbial community. These findings indicate that CJSe mitigates the threat posed by CJ and reduces ARG prevalence, while its dual functionality enables applications in biofortified crop production and soil remediation in selenium-deficient regions, thereby advancing circular economy systems. While the current study demonstrates CJSe's dual functionality under controlled conditions, future work will implement a dedicated ecological risk assessment framework encompassing Se speciation/leaching tests and non-target organism assays to confirm environmental safety under field-relevant scenarios. This approach aligns with sustainable strategies for food security and public health safeguarding.202541108960
7130150.9902Microbial community structure and resistome dynamics on elevator buttons in response to surface disinfection practices. BACKGROUND: Disinfectants have been extensively used in public environments since the COVID-19 outbreak to help control the spread of the virus. This study aims to investigate whether disinfectant use influences the structure of bacterial communities and contributes to bacterial resistance to disinfectants and antibiotics. METHODS: Using molecular biology techniques-including metagenomic sequencing and quantitative PCR (qPCR)-we analyzed the bacterial communities on elevator button surfaces from two tertiary hospitals, one infectious disease hospital, two quarantine hotels (designated for COVID-19 control), and five general hotels in Nanjing, Jiangsu Province, during the COVID-19 pandemic. We focused on detecting disinfectant resistance genes (DRGs), antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs). RESULTS: Significant differences were observed in the bacterial community structures on elevator button surfaces across the four types of environments. Quarantine hotels, which implemented the most frequent disinfection protocols, exhibited distinct bacterial profiles at the phylum, genus, and species levels. Both α-diversity (within-sample diversity) and β-diversity (between-sample diversity) were lower and more distinct in quarantine hotels compared to the other environments. The abundance of DRGs, ARGs, and MGEs was also significantly higher on elevator button surfaces in quarantine hotels. Notably, antibiotic-resistant bacteria (ARBs), including Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa, were detected in all four settings. CONCLUSION: The structure of bacterial communities on elevator button surfaces varies across different environments, likely influenced by the frequency of disinfectant use. Increased resistance gene abundance in quarantine hotels suggests that disinfection practices may contribute to the selection and spread of resistant bacteria. Enhanced monitoring of disinfection effectiveness and refinement of protocols in high-risk environments such as hospitals and hotels are essential to limit the spread of resistant pathogens.202540520307
7488160.9902Metagenomic insights into microorganisms and antibiotic resistance genes of waste antibiotic fermentation residues along production, storage and treatment processes. Antibiotic fermentation residue (AFR) is nutrient-rich solid waste generated from fermentative antibiotic production process. It is demonstrated that AFR contains high-concentration of remaining antibiotics, and thus may promote antibiotic resistance development in receiving environment or feeding farmed animals. However, the dominate microorganisms and antibiotic resistance genes (ARGs) in AFRs have not been adequately explored, hampering understanding on the potential antibiotic resistance risk development caused by AFRs. Herein, seven kinds of representative AFRs along their production, storage, and treatment processes were collected, and multiple methods including amplicon sequencing, metagenomic sequencing, and bioinformatic approaches were adopted to explore the biological characteristics of AFRs. As expected, antibiotic fermentation producer was found as the predominant species in raw AFRs, which were collected at the outlet of fermentation tanks. However, except for producer species, more environment-derived species persisted in stored AFRs, which were temporarily stored at a semi-open space. Lactobacillus genus, classified as Firmicutes phylum and Bacilli class, became predominant bacterial taxa in stored AFRs, which might attribute to its tolerance to high concentration of antibiotics. Results from metagenomic sequencing together with assembly and binning approaches showed that these newly-colonizing species (e.g., Lactobacillus genus) tended to carry ARGs conferring resistance to the remaining antibiotic. However, after thermal treatment, remaining antibiotic could be efficiently removed from AFRs, and microorganisms together with DNA could be strongly destroyed. In sum, the main risk from the AFRs was the remaining antibiotic, while environment-derived bacteria which tolerate extreme environment, survived in ARFs with high content antibiotics, and may carry ARGs. Thus, hydrothermal or other harmless treatment technologies are recommended to remove antibiotic content and inactivate bacteria before recycling of AFRs in pharmaceutical industry.202437923454
6600170.9902Metagenomic approaches for the quantification of antibiotic resistance genes in swine wastewater treatment system: a systematic review. This systematic review aims to identify the metagenomic methodological approaches employed for the detection of antimicrobial resistance genes (ARGs) in swine wastewater treatment systems. The search terms used were metagenome AND bacteria AND ("antimicrobial resistance gene" OR resistome OR ARG) AND wastewater AND (swine OR pig), and the search was conducted across the following electronic databases: PubMed, Scopus, ScienceDirect, Web of Science, Embase, and Cochrane Library. The search was limited to studies published between 2020 and 2024. Of the 220 studies retrieved, eight met the eligibility criteria for full-text analysis. The number of publications in this research area has increased in recent years, with China contributing the highest number of studies. ARGs are typically identified using bioinformatics pipelines that include steps such as quality trimming, assembly, metagenome-assembled genome (MAG) reconstruction, open reading frame (ORF) prediction, and ARG annotation. However, comparing ARGs quantification across studies remains challenging due to methodological differences and variability in quantification approaches. Therefore, this systematic review highlights the need for methodological standardization to facilitate comparison and enhance our understanding of antimicrobial resistance in swine wastewater treatment systems through metagenomic approaches.202540788461
6648180.9901Multi-Drug Resistant Coliform: Water Sanitary Standards and Health Hazards. Water constitutes and sustains life; however, its pollution afflicts its necessity, further worsening its scarcity. Coliform is one of the largest groups of bacteria evident in fecally polluted water, a major public health concern. Coliform thrive as commensals in the gut of warm-blooded animals, and are indefinitely passed through their feces into the environment. They are also called as model organisms as their presence is indicative of the prevalence of other potential pathogens, thus coliform are and unanimously employed as adept indicators of fecal pollution. As only a limited accessible source of fresh water is available on the planet, its contamination severely affects its usability. Coliform densities vary geographically and seasonally which leads to the lack of universally uniform regulatory guidelines regarding water potability often leads to ineffective detection of these model organisms and the misinterpretation of water quality status. Remedial measures such as disinfection, reducing the nutrient concentration or re-population doesn't hold context in huge lotic ecosystems such as freshwater rivers. There is also an escalating concern regarding the prevalence of multi-drug resistance in coliforms which renders antibiotic therapy incompetent. Antimicrobials are increasingly used in household, clinical, veterinary, animal husbandry and agricultural settings. Sub-optimal concentrations of these antimicrobials are unintentionally but regularly dispensed into the environment through seepages, sewages or runoffs from clinical or agricultural settings substantially adding to the ever-increasing pool of antibiotic resistance genes. When present below their minimum inhibitory concentration (MIC), these antimicrobials trigger the transfer of antibiotic-resistant genes that the coliform readily assimilate and further propagate to pathogens, the severity of which is evidenced by the high Multiple Antibiotic Resistance (MAR) index shown by the bacterial isolates procured from the environmental. This review attempts to assiduously anthologize the use of coliforms as water quality standards, their existent methods of detection and the issue of arising multi-drug resistance in them.201829946253
6689190.9901Wastewater-Based Epidemiology as a Complementary Tool for Antimicrobial Resistance Surveillance: Overcoming Barriers to Integration. This commentary highlights the potential of wastewater-based epidemiology (WBE) as a complementary tool for antimicrobial resistance (AMR) surveillance. WBE can support the early detection of resistance trends at the population level, including in underserved communities. However, several challenges remain, including technical variability, complexities in data interpretation, and regulatory gaps. An additional limitation is the uncertainty surrounding the origin of resistant bacteria and their genes in wastewater, which may derive not only from human sources but also from industrial, agricultural, or infrastructural contributors. Therefore, effective integration of WBE into public health systems will require standardized methods, sustained investment, and cross-sector collaboration. This could be achieved through joint monitoring initiatives that combine hospital wastewater data with agricultural and municipal surveillance to inform antibiotic stewardship policies. Overcoming these barriers could position WBE as an innovative tool for AMR monitoring, enhancing early warning systems and supporting more responsive, equitable, and preventive public health strategies.202540522150