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908300.9947ARGNet: 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
907910.9947Review, 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
962520.9945Water chlorination increases the relative abundance of an antibiotic resistance marker in developing sourdough starters. Multiple factors explain the proper development of sourdough starters. Although the role of raw ingredients and geography, among other things, have been widely studied recently, the possible effect of air quality and water chlorination on the overall bacterial communities associated with sourdough remains to be explored. Here, using 16S rRNA amplicon sequencing, we show that clean, filtered-air severely limited the presence of lactic acid bacteria in sourdough starters, suggesting that surrounding air is an important source of microorganisms necessary for the development of sourdough starters. We also show that water chlorination at levels commonly found in drinking water systems has a limited impact on the overall bacterial communities developing in sourdough starters. However, using targeted sequencing, which offers a higher resolution, we found that the abundance of integron 1, a genetic mechanism responsible for the horizontal exchange of antibiotic-resistance genes in spoilage and pathogenic bacteria, increased significantly with the level of water chlorination. Although our results suggest that water chlorination might not impact sourdough starters at a deep phylogenetic level, they indicate that it can favor the spread of genetic elements associated with spoilage bacteria. IMPORTANCE: Proper development of sourdough starters is critical for making tasty and healthy bread. Although many factors contributing to sourdough development have been studied, the effect of water chlorination on the bacterial communities in sourdough has been largely ignored. Researchers used sequencing techniques to investigate this effect and found that water chlorination at levels commonly found in drinking water systems has a limited impact on the overall bacterial communities developing in sourdough starters. However, they discovered that water chlorination could increase the abundance of integron 1, a genetic mechanism responsible for the horizontal exchange of antibiotic resistance genes in spoilage and pathogenic bacteria. This suggests that water chlorination could favor the growth of key spoilage bacteria and compromise the quality and safety of the bread. These findings emphasize the importance of considering water quality when developing sourdough starters for the best possible bread.202439283274
663430.9944Making waves: The NORMAN antibiotic resistant bacteria and resistance genes database (NORMAN ARB&ARG)-An invitation for collaboration to tackle antibiotic resistance. With the global concerns on antibiotic resistance (AR) as a public health issue, it is pivotal to have data exchange platforms for studies on antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in the environment. For this purpose, the NORMAN Association is hosting the NORMAN ARB&ARG database, which was developed within the European project ANSWER. The present article provides an overview on the database functionalities, the extraction and the contribution of data to the database. In this study, AR data from three studies from China and Nepal were extracted and imported into the NORMAN ARB&ARG in addition to the existing AR data from 11 studies (mainly European studies) on the database. This feasibility study demonstrates how the scientific community can share their data on AR to generate an international evidence base to inform AR mitigation strategies. The open and FAIR data are of high potential relevance for regulatory applications, including the development of emission limit values / environmental quality standards in relation to AR. The growth in sharing of data and analytical methods will foster collaboration on risk management of AR worldwide, and facilitate the harmonization in the effort for identification and surveillance of critical hotspots of AR. The NORMAN ARB&ARG database is publicly available at: https://www.norman-network.com/nds/bacteria/.202438723350
642740.9944Cyanobacterial blooms contribute to the diversity of antibiotic-resistance genes in aquatic ecosystems. Cyanobacterial blooms are a global ecological problem that directly threatens human health and crop safety. Cyanobacteria have toxic effects on aquatic microorganisms, which could drive the selection for resistance genes. The effect of cyanobacterial blooms on the dispersal and abundance of antibiotic-resistance genes (ARGs) of concern to human health remains poorly known. We herein investigated the effect of cyanobacterial blooms on ARG composition in Lake Taihu, China. The numbers and relative abundances of total ARGs increased obviously during a Planktothrix bloom. More pathogenic microorganisms were present during this bloom than during a Planktothrix bloom or during the non-bloom period. Microcosmic experiments using additional aquatic ecosystems (an urban river and Lake West) found that a coculture of Microcystis aeruginosa and Planktothrix agardhii increased the richness of the bacterial community, because its phycosphere provided a richer microniche for bacterial colonization and growth. Antibiotic-resistance bacteria were naturally in a rich position, successfully increasing the momentum for the emergence and spread of ARGs. These results demonstrate that cyanobacterial blooms are a crucial driver of ARG diffusion and enrichment in freshwater, thus providing a reference for the ecology and evolution of ARGs and ARBs and for better assessing and managing water quality.202033277584
510150.9944Identification of Key Features Pivotal to the Characteristics and Functions of Gut Bacteria Taxa through Machine Learning Methods. BACKGROUND: Gut bacteria critically influence digestion, facilitate the breakdown of complex food substances, aid in essential nutrient synthesis, and contribute to immune system balance. However, current knowledge regarding intestinal bacteria remains insufficient. OBJECTIVE: This study aims to discover essential differences for different intestinal bacteria. METHODS: This study was conducted by investigating a total of 1478 gut bacterial samples comprising 235 Actinobacteria, 447 Bacteroidetes, and 796 Firmicutes, by utilizing sophisticated machine learning algorithms. By building on the dataset provided by Chen et al., we engaged sophisticated machine learning techniques to further investigate and analyze the gut bacterial samples. Each sample in the dataset was described by 993 unique features associated with gut bacteria, including 342 features annotated by the Antibiotic Resistance Genes Database, Comprehensive Antibiotic Research Database, Kyoto Encyclopedia of Genes and Genomes, and Virulence Factors of Pathogenic Bacteria. We employed incremental feature selection methods within a computational framework to identify the optimal features for classification. RESULTS: Eleven feature ranking algorithms selected several key features as pivotal to the characteristics and functions of gut bacteria. These features appear to facilitate the identification of specific gut bacterial species. Additionally, we established quantitative rules for identifying Actinobacteria, Bacteroidetes, and Firmicutes. CONCLUSION: This research underscores the significant potential of machine learning in studying gut microbes and enhances our understanding of the multifaceted roles of gut bacteria.202540671232
648460.9944Microbial assisted phytodepuration for water reclamation: Environmental benefits and threats. Climate changes push for water reuse as a priority to counteract water scarcity and minimize water footprint especially in agriculture, one of the highest water consuming human activities. Phytodepuration is indicated as a promising technology for water reclamation, also in the light of its economic and ecological sustainability, and the use of specific bacterial inocula for microbial assisted phytodepuration has been proposed as a further advance for its implementation. Here we provided an overview on the selection and use of plant growth promoting bacteria in Constructed Wetland (CW) systems, showing their advantages in terms of plant growth support and pollutant degradation abilities. Moreover, CWs are also proposed for the removal of emerging organic pollutants like antibiotics from urban wastewaters. We focused on this issue, still debated in the literature, revealing the necessity to deepen the knowledge on the antibiotic resistance spread into the environment in relation to treated wastewater release and reuse. In addition, given the presence in the plant system of microhabitats (e.g. rhizosphere) that are hot spot for Horizontal Gene Transfer, we highlighted the importance of gene exchange to understand if these events can promote the diffusion of antibiotic resistance genes and antibiotic resistant bacteria, possibly entering in the food production chain when treated wastewater is used for irrigation. Ideally, this new knowledge will lead to improve the design of phytodepuration systems to maximize the quality and safety of the treated effluents in compliance with the 'One Health' concept.202031605997
907570.9944CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter. BACKGROUND: The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge. RESULTS: To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation. CONCLUSIONS: The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at https://github.com/JoseBarbero/CamPype .202337474912
908180.9943Identification and reconstruction of novel antibiotic resistance genes from metagenomes. BACKGROUND: Environmental and commensal bacteria maintain a diverse and largely unknown collection of antibiotic resistance genes (ARGs) that, over time, may be mobilized and transferred to pathogens. Metagenomics enables cultivation-independent characterization of bacterial communities but the resulting data is noisy and highly fragmented, severely hampering the identification of previously undescribed ARGs. We have therefore developed fARGene, a method for identification and reconstruction of ARGs directly from shotgun metagenomic data. RESULTS: fARGene uses optimized gene models and can therefore with high accuracy identify previously uncharacterized resistance genes, even if their sequence similarity to known ARGs is low. By performing the analysis directly on the metagenomic fragments, fARGene also circumvents the need for a high-quality assembly. To demonstrate the applicability of fARGene, we reconstructed β-lactamases from five billion metagenomic reads, resulting in 221 ARGs, of which 58 were previously not reported. Based on 38 ARGs reconstructed by fARGene, experimental verification showed that 81% provided a resistance phenotype in Escherichia coli. Compared to other methods for detecting ARGs in metagenomic data, fARGene has superior sensitivity and the ability to reconstruct previously unknown genes directly from the sequence reads. CONCLUSIONS: We conclude that fARGene provides an efficient and reliable way to explore the unknown resistome in bacterial communities. The method is applicable to any type of ARGs and is freely available via GitHub under the MIT license.201930935407
660090.9943Metagenomic 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
7488100.9943Metagenomic 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
6686110.9943The 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
6425120.9943Freshwater plastisphere: a review on biodiversity, risks, and biodegradation potential with implications for the aquatic ecosystem health. The plastisphere, a unique microbial biofilm community colonizing plastic debris and microplastics (MPs) in aquatic environments, has attracted increasing attention owing to its ecological and public health implications. This review consolidates current state of knowledge on freshwater plastisphere, focussing on its biodiversity, community assembly, and interactions with environmental factors. Current biomolecular approaches revealed a variety of prokaryotic and eukaryotic taxa associated with plastic surfaces. Despite their ecological importance, the presence of potentially pathogenic bacteria and mobile genetic elements (i.e., antibiotic resistance genes) raises concerns for ecosystem and human health. However, the extent of these risks and their implications remain unclear. Advanced sequencing technologies are promising for elucidating the functions of plastisphere, particularly in plastic biodegradation processes. Overall, this review emphasizes the need for comprehensive studies to understand plastisphere dynamics in freshwater and to support effective management strategies to mitigate the impact of plastic pollution on freshwater resources.202438699475
8661130.9943Differential priority effects impact taxonomy and functionality of host-associated microbiomes. Most multicellular eukaryotes host complex communities of microorganisms, but the factors that govern their assembly are poorly understood. The settlement of specific microorganisms may have a lasting impact on community composition, a phenomenon known as the priority effect. Priority effects of individual bacterial strains on a host's microbiome are, however, rarely studied and their impact on microbiome functionality remains unknown. We experimentally tested the effect of two bacterial strains (Pseudoalteromonas tunicata D2 and Pseudovibrio sp. D323) on the assembly and succession of the microbial communities associated with the green macroalga Ulva australis. Using 16S rRNA gene sequencing and qPCR, we found that both strains exert a priority effect, with strain D2 causing initially strong but temporary taxonomic changes and strain D323 causing weaker but consistent changes. Consistent changes were predominately facilitatory and included taxa that may benefit the algal host. Metagenome analyses revealed that the strains elicited both shared (e.g., depletion of type III secretion system genes) and unique (e.g., enrichment of antibiotic resistance genes) effects on the predicted microbiome functionality. These findings indicate strong idiosyncratic effects of colonizing bacteria on the structure and function of host-associated microbial communities. Understanding the idiosyncrasies in priority effects is key for the development of novel probiotics to improve host condition.202334995388
6473140.9942The potential implications of reclaimed wastewater reuse for irrigation on the agricultural environment: The knowns and unknowns of the fate of antibiotics and antibiotic resistant bacteria and resistance genes - A review. The use of reclaimed wastewater (RWW) for the irrigation of crops may result in the continuous exposure of the agricultural environment to antibiotics, antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs). In recent years, certain evidence indicate that antibiotics and resistance genes may become disseminated in agricultural soils as a result of the amendment with manure and biosolids and irrigation with RWW. Antibiotic residues and other contaminants may undergo sorption/desorption and transformation processes (both biotic and abiotic), and have the potential to affect the soil microbiota. Antibiotics found in the soil pore water (bioavailable fraction) as a result of RWW irrigation may be taken up by crop plants, bioaccumulate within plant tissues and subsequently enter the food webs; potentially resulting in detrimental public health implications. It can be also hypothesized that ARGs can spread among soil and plant-associated bacteria, a fact that may have serious human health implications. The majority of studies dealing with these environmental and social challenges related with the use of RWW for irrigation were conducted under laboratory or using, somehow, controlled conditions. This critical review discusses the state of the art on the fate of antibiotics, ARB and ARGs in agricultural environment where RWW is applied for irrigation. The implications associated with the uptake of antibiotics by plants (uptake mechanisms) and the potential risks to public health are highlighted. Additionally, knowledge gaps as well as challenges and opportunities are addressed, with the aim of boosting future research towards an enhanced understanding of the fate and implications of these contaminants of emerging concern in the agricultural environment. These are key issues in a world where the increasing water scarcity and the continuous appeal of circular economy demand answers for a long-term safe use of RWW for irrigation.201728689129
6418150.9942Antibiotic resistance genes in anaerobic digestion: Unresolved challenges and potential solutions. Antimicrobial resistance (AMR) threatens public health, necessitating urgent efforts to mitigate the global impact of antibiotic resistance genes (ARGs). Anaerobic digestion (AD), known for volatile solid reduction and energy generation, also presents a feasible approach for the removal of ARGs. This review encapsulates the existing understanding of ARGs and antibiotic-resistant bacteria (ARB) during the AD process, highlighting unresolved challenges pertaining to their detection and quantification. The questions raised and discussed include: Do current ARGs detection methods meet qualitative and quantitative requirements? How can we conduct risk assessments of ARGs? What happens to ARGs when they come into co-exposure with other emerging pollutants? How can the application of internal standards bolster the reliability of the AD resistome study? What are the potential future research directions that could enhance ARG elimination? Investigating these subjects will assist in shaping more efficient management strategies that employ AD for effective ARG control.202539826759
6426160.9942Deciphering the pathogenic risks of microplastics as emerging particulate organic matter in aquatic ecosystem. Microplastics are accumulating rapidly in aquatic ecosystems, providing habitats for pathogens and vectors for antibiotic resistance genes (ARGs), potentially increasing pathogenic risks. However, few studies have considered microplastics as particulate organic matter (POM) to elucidate their pathogenic risks and underlying mechanisms. Here, we performed microcosm experiments with microplastics and natural POM (leaves, algae, soil), thoroughly investigating their distinct effects on the community compositions, functional profiles, opportunistic pathogens, and ARGs in Particle-Associated (PA) and Free-Living (FL) bacterial communities. We found that both microplastics and leaves have comparable impacts on microbial community structures and functions, enriching opportunistic pathogens and ARGs, which may pose potential environmental risks. These effects are likely driven by their influences on water properties, including dissolved organic carbon, nitrate, DO, and pH. However, microplastics uniquely promoted pathogens as keystone species and further amplified their capacity as hosts for ARGs, potentially posing a higher pathogenic risk than natural POM. Our research also emphasized the importance of considering both PA and FL bacteria when assessing microplastic impacts, as they exhibited different responses. Overall, our study elucidates the role and underlying mechanism of microplastics as an emerging POM in intensifying pathogenic risks of aquatic ecosystems in comparison with conventional natural POM.202438805824
6911170.9942Linking bacterial life strategies with the distribution pattern of antibiotic resistance genes in soil aggregates after straw addition. Straw addition markedly affects the soil aggregates and microbial community structure. However, its influence on the profile of antibiotic resistance genes (ARGs), which are likely associated with changes in bacterial life strategies, remains unclear. To clarify this issue, a soil microcosm experiment was incubated under aerobic (WS) or anaerobic (AnWS) conditions after straw addition, and metagenomic sequencing was used to characterise ARGs and bacterial communities in soil aggregates. The results showed that straw addition shifted the bacterial life strategies from K- to r-strategists in all aggregates, and the aerobic and anaerobic conditions stimulated the growth of aerobic and anaerobic r-strategist bacteria, respectively. The WS decreased the relative abundances of dominant ARGs such as QnrS5, whereas the AnWS increased their abundance. After straw addition, the macroaggregates consistently exhibited a higher number of significantly altered bacteria and ARGs than the silt+clay fractions. Network analysis revealed that the WS increased the number of aerobic r-strategist bacterial nodes and fostered more interactions between r-and K-strategist bacteria, thus promoting ARGs prevalence, whereas AnWS exhibited an opposite trend. These findings provide a new perspective for understanding the fate of ARGs and their controlling factors in soil ecosystems after straw addition. ENVIRONMENTAL IMPLICATIONS: Straw soil amendment has been recommended to mitigate soil fertility degradation, improve soil structure, and ultimately increase crop yields. However, our findings highlight the importance of the elevated prevalence of ARGs associated with r-strategist bacteria in macroaggregates following the addition of organic matter, particularly fresh substrates. In addition, when assessing the environmental risk posed by ARGs in soil that receives crop straw, it is essential to account for the soil moisture content. This is because the species of r-strategist bacteria that thrive under aerobic and anaerobic conditions play a dominant role in the dissemination and accumulation of ARG.202438643583
7671180.9942Predicting the abundance of metal resistance genes in subtropical estuaries using amplicon sequencing and machine learning. Heavy metals are a group of anthropogenic contaminants in estuary ecosystems. Bacteria in estuaries counteract the highly concentrated metal toxicity through metal resistance genes (MRGs). Presently, metagenomic technology is popularly used to study MRGs. However, an easier and less expensive method of acquiring MRG information is needed to deepen our understanding of the fate of MRGs. Thus, this study explores the feasibility of using a machine learning approach-namely, random forests (RF)-to predict MRG abundance based on the 16S rRNA amplicon sequenced datasets from subtropical estuaries in China. Our results showed that the total MRG abundance could be predicted by RF models using bacterial composition at different taxonomic levels. Among them, the relative abundance of bacterial phyla had the highest predicted accuracy (71.7 %). In addition, the RF models constructed by bacterial phyla predicted the abundance of six MRG types and nine MRG subtypes with substantial accuracy (R(2) > 0.600). Five bacterial phyla (Firmicutes, Bacteroidetes, Patescibacteria, Armatimonadetes, and Nitrospirae) substantially determined the variations in MRG abundance. Our findings prove that RF models can predict MRG abundance in South China estuaries during the wet season by using the bacterial composition obtained by 16S rRNA amplicon sequencing.202236068766
7700190.9942Rapid identification of antibiotic resistance gene hosts by prescreening ARG-like reads. Effective risk assessment and control of environmental antibiotic resistance depend on comprehensive information about antibiotic resistance genes (ARGs) and their microbial hosts. Advances in sequencing technologies and bioinformatics have enabled the identification of ARG hosts using metagenome-assembled contigs and genomes. However, these approaches often suffer from information loss and require extensive computational resources. Here we introduce a bioinformatic strategy that identifies ARG hosts by prescreening ARG-like reads (ALRs) directly from total metagenomic datasets. This ALR-based method offers several advantages: (1) it enables the detection of low-abundance ARG hosts with higher accuracy in complex environments; (2) it establishes a direct relationship between the abundance of ARGs and their hosts; and (3) it reduces computation time by approximately 44-96% compared to strategies relying on assembled contigs and genomes. We applied our ALR-based strategy alongside two traditional methods to investigate a typical human-impacted environment. The results were consistent across all methods, revealing that ARGs are predominantly carried by Gammaproteobacteria and Bacilli, and their distribution patterns may indicate the impact of wastewater discharge on coastal resistome. Our strategy provides rapid and accurate identification of antibiotic-resistant bacteria, offering valuable insights for the high-throughput surveillance of environmental antibiotic resistance. This study further expands our knowledge of ARG-related risk management in future.202540059905