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1. ARGContextProfiler: extracting and scoring the genomic contexts of antibiotic resistance genes using assembly graphs

   https://doi.org/10.3389/fmicb.2025.1604461  

   Moumi, Nazifa Ahmed; Ahmed, Shafayat; Brown, Connor; Pruden, Amy; Zhang, Liqing (May 2025, Frontiers in Microbiology)

   Antibiotic resistance (AR) presents a global health challenge, necessitating an improved understanding of the ecology, evolution, and dissemination of antibiotic resistance genes (ARGs). Several tools, databases, and algorithms are now available to facilitate the identification of ARGs in metagenomic sequencing data; however, direct annotation of short-read data provides limited contextual information. Knowledge of whether an ARG is carried in the chromosome or on a specific mobile genetic element (MGE) is critical to understanding mobility, persistence, and potential for co-selection. Here we developed ARGContextProfiler, a pipeline designed to extract and visualize ARG genomic contexts. By leveraging the assembly graph for genomic neighborhood extraction and validating contexts through read mapping, ARGContextProfiler minimizes chimeric errors that are a common artifact of assembly outputs. Testing on real, synthetic, and semi-synthetic data, including long-read sequencing data from environmental samples, demonstrated that ARGContextProfiler offers superior accuracy, precision, and sensitivity compared to conventional assembly-based methods. ARGContextProfiler thus provides a powerful tool for uncovering the genomic context of ARGs in metagenomic sequencing data, which can be of value to both fundamental and applied research aimed at understanding and stemming the spread of AR. The source code of ARGContextProfiler is publicly available atGitHub. 

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2. Resistance genes are distinct in protein-protein interaction networks according to drug class and gene mobility

   https://doi.org/10.1101/2024.02.05.578986  

   Moumi, Nazifa Ahmed; Brown, Connor L; Ahmed, Shafayat; Vikesland, Peter J; Pruden, Amy; Zhang, Liqing (February 2024, bioRxiv)

   Abstract With growing calls for increased surveillance of antibiotic resistance as an escalating global health threat, improved bioinformatic tools are needed for tracking antibiotic resistance genes (ARGs) across One Health domains. Most studies to date profile ARGs using sequence homology, but such approaches provide limited information about the broader context or function of the ARG in bacterial genomes. Here we introduce a new pipeline for identifying ARGs in genomic data that employs machine learning analysis of Protein-Protein Interaction Networks (PPINs) as a means to improve predictions of ARGs while also providing vital information about the context, such as gene mobility. A random forest model was trained to effectively differentiate between ARGs and nonARGs and was validated using the PPINs of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, andEnterobacter cloacae), which represent urgent threats to human health because they tend to be multi-antibiotic resistant. The pipeline exhibited robustness in discriminating ARGs from nonARGs, achieving an average area under the precision-recall curve of 88%. We further identified that the neighbors of ARGs, i.e., genes connected to ARGs by only one edge, were disproportionately associated with mobile genetic elements, which is consistent with the understanding that ARGs tend to be mobile compared to randomly sampled genes in the PPINs. This pipeline showcases the utility of PPINs in discerning distinctive characteristics of ARGs within a broader genomic context and in differentiating ARGs from nonARGs through network-based attributes and interaction patterns. The code for running the pipeline is publicly available athttps://github.com/NazifaMoumi/PPI-ARG-ESKAPE 

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3. Decade-long warming accelerates antibiotic resistance in grassland soils

   https://doi.org/10.1038/s41586-026-10413-x  

   Wu, Linwei; Mu, Da-Shuai; An, Jing; Wang, Yanan; Fan, Xiaomin; Lu, De-Chen; Zhang, Ya; Xie, Yinan; Michael, Jonathan; Curtis, Daniel; et al (April 2026, Nature)

   Soils are critical reservoirs of antibiotic-resistance genes (ARGs), which are strongly shaped by microbial interactions and environmental conditions and are therefore highly sensitive to disturbance. Although climate warming is recognized as one of the most significant disturbances to microbial communities and their functions, its impacts on soil resistomes remain poorly understood. Here we investigated the effects of decade-long experimental warming on ARGs in grassland soils using integrated experimental and computational approaches. Our results revealed that ARG abundance substantially increased (23.9%) under warming—particularly glycopeptide- and rifamycin-resistance genes. Warming specifically enriched Actinomycetota hosts, including various potential plant pathogens, and enhanced ARG mobility. Large-scale unprecedented isolates-based phenotypic analyses also validated that warming increased bacterial resistance to multiple antibiotics. Further mechanistic analyses revealed that warming increased ARG abundance primarily through co-selection of resistance genes physically linked to adaptive traits (for example, thermal tolerance and nitrogen assimilation) and positive selection for thermal tolerance genes, which could be further amplified via horizontal gene transfer. Together, these findings convincingly demonstrate that climate warming substantially accelerates soil antibiotic resistance at genomic, ecological and evolutionary levels, with broad implications for public health and environmental sustainability in a warming world. 

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4. Evaluation of Metagenomic-Enabled Antibiotic Resistance Surveillance at a Conventional Wastewater Treatment Plant

   https://doi.org/10.3389/fmicb.2021.657954  

   Majeed, Haniyyah J.; Riquelme, Maria V.; Davis, Benjamin C.; Gupta, Suraj; Angeles, Luisa; Aga, Diana S.; Garner, Emily; Pruden, Amy; Vikesland, Peter J. (May 2021, Frontiers in Microbiology)

   null (Ed.)

   Wastewater treatment plants (WWTPs) receive a confluence of sewage containing antimicrobials, antibiotic resistant bacteria, antibiotic resistance genes (ARGs), and pathogens and thus are a key point of interest for antibiotic resistance surveillance. WWTP monitoring has the potential to inform with respect to the antibiotic resistance status of the community served as well as the potential for ARGs to escape treatment. However, there is lack of agreement regarding suitable sampling frequencies and monitoring targets to facilitate comparison within and among individual WWTPs. The objective of this study was to comprehensively evaluate patterns in metagenomic-derived indicators of antibiotic resistance through various stages of treatment at a conventional WWTP for the purpose of informing local monitoring approaches that are also informative for global comparison. Relative abundance of total ARGs decreased by ∼50% from the influent to the effluent, with each sampling location defined by a unique resistome (i.e., total ARG) composition. However, 90% of the ARGs found in the effluent were also detected in the influent, while the effluent ARG-pathogen taxonomic linkage patterns identified in assembled metagenomes were more similar to patterns in regional clinical surveillance data than the patterns identified in the influent. Analysis of core and discriminatory resistomes and general ARG trends across the eight sampling events (i.e., tendency to be removed, increase, decrease, or be found in the effluent only), along with quantification of ARGs of clinical concern, aided in identifying candidate ARGs for surveillance. Relative resistome risk characterization further provided a comprehensive metric for predicting the relative mobility of ARGs and likelihood of being carried in pathogens and can help to prioritize where to focus future monitoring and mitigation. Most antibiotics that were subject to regional resistance testing were also found in the WWTP, with the total antibiotic load decreasing by ∼40–50%, but no strong correlations were found between antibiotics and corresponding ARGs. Overall, this study provides insight into how metagenomic data can be collected and analyzed for surveillance of antibiotic resistance at WWTPs, suggesting that effluent is a beneficial monitoring point with relevance both to the local clinical condition and for assessing efficacy of wastewater treatment in reducing risk of disseminating antibiotic resistance. 

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5. Comparison of antibiotic resistance genes in swine manure storage pits of Iowa, USA

   https://doi.org/10.3389/frabi.2023.1116785  

   Neher, Timothy P.; Soupir, Michelle L.; Andersen, Daniel S.; O’Neill, Maggie L.; Howe, Adina (March 2023, Frontiers in Antibiotics)

   Antimicrobial resistance (AMR) can develop in deep-pit swine manure storage when bacteria are selectively pressured by unmetabolized antibiotics. Subsequent manure application on row crops is then a source of AMR into soil and downstream runoff water. Therefore, understanding the patterns of diverse antibiotic resistance genes (ARGs) in manure among different farms is important for both interpreting the results of the detection of these genes from previous studies and for the use of these genes as bioindicators of manure borne antibiotic resistance in the environment. Previous studies of manure-associated ARGs are based on limited samples of manures. To better understand the distribution of ARGs between manures, we characterized manures from 48 geographically independent swine farms across Iowa. The objectives of this study were to characterize the distribution of ARGs among these manures and to evaluate what factors in manure management may influence the presence of ARGs in manures. Our analysis included quantification of two commonly found ARGs in swine manure, ermB and tetM . Additionally, we characterized a broader suite of 31 ARGs which allowed for simultaneous assays of the presence or absence of multiple genes. We found the company integrator had a significant effect on both ermB ( P=0.0007 ) and tetM gene concentrations ( P=0.0425 ). Our broad analysis on ARG profiles found that the tet(36) gene was broadly present in swine manures, followed by the detection of tetT , tetM , erm(35) , ermF , ermB , str , aadD , and intl3 in samples from 14 farms. Finally, we provide a comparison of methods to detect ARGs in manures, specifically comparing conventional and high-throughput qPCR and discuss their role in ARG environmental monitoring efforts. Results of this study provide insight into commonalities of ARG presence in manure holding pits and provide supporting evidence that company integrator decisions may impact ARG concentrations. 

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