Temporal Dynamics and Contribution of Phage Community to the Prevalence of Antibiotic Resistance Genes in a Full-Scale Sludge Anaerobic Digestion Plant.

Anaerobic digestion (AD) is an important biological resource recovery process, where microorganisms play key roles for material transformation. There has been some knowledge about the prokaryotic community and antibiotic resistance genes (ARGs) in AD, but there has been very limited knowledge of phages. In this study, samples from a full-scale AD plant were collected over 13 months, sequenced, and analyzed for viral and prokaryotic metagenomes. Totally, 3015 viral operational taxonomic units (vOTUs) were detected, mostly assigned to Caudoviricetes. The phage community had faster temporal variation than the prokaryotic community. Warm seasons harbored a higher abundance of both temperate phages and broad host-range phages. Seven ARGs of 6 subtypes were carried by 20 vOTUs, a representative ermT gene was synthesized and expressed, and the resistance activity in the host was examined, confirming the real activity of virus-carried ARGs in the AD process. Some of the ARGs were horizontally transferred between the phage and prokaryotic genomes. However, phage infection was not found to contribute to ARG transfer. This study provided an insight into the ecological patterns of the phage community, confirmed the antibiotic resistance activity of virus-carried ARGs, evaluated the contribution of phages on the ARG prevalence, and laid the foundation for the control strategies of the community and antibiotic resistance in the AD process.

The proliferation of antibiotic resistance genes (ARGs) and microbial communities in industrial wastewater treatment plant treating N,N-dimethylformamide (DMF) by AAO process.

The excessive use of antibiotics has resulted in the contamination of the environment with antibiotic resistance genes (ARGs), posing a significant threat to public health. Wastewater treatment plants (WWTPs) are known to be reservoirs of ARGs and considered to be hotspots for horizontal gene transfer (HGT) between bacterial communities. However, most studies focused on the distribution and dissemination of ARGs in hospital and urban WWTPs, and little is known about their fate in industrial WWTPs. In this study, collected the 15 wastewater samples containing N,N-dimethylformamide (DMF) from five stages of the anaerobic anoxic aerobic (AAO) process in an industrial WWTPs. The findings revealed a stepwise decrease in DMF and chemical oxygen demand (COD) content with the progression of treatment. However, the number and abundances of ARGs increase in the effluents of biological treatments. Furthermore, the residues of DMF and the treatment process altered the structure of the bacterial community. The correlation analysis indicated that the shift in bacterial community structures might be the main driver for the dynamics change of ARGs. Interestingly, observed that the AAO process may acted as a microbial source and increased the total abundance of ARGs instead of attenuating it. Additionally, found that non-pathogenic bacteria had higher ARGs abundance than pathogenic bacteria in effluents. The study provides insights into the microbial community structure and the mechanisms that drive the variation in ARGs abundance in industrial WWTPs.

Resistome analysis of Escherichia coli isolates from layers in Hungary.

The authors aimed to investigate eight strains of Escherichia coli (E. coli) strains from Hungarian layer flocks for antimicrobial resistance genes (ARG), using metagenomic methods. The strains were isolated from cloacal swabs of healthy adult layers. This study employed shotgun sequencing-based genetic and bioinformatic analysis along with determining phenotypic minimum inhibitory concentrations. A total of 59 ARGs were identified in the eight E. coli isolates, carrying ARGs against 15 groups of antibiotics. Among these, 28 ARGs were identified as transferable. Specifically, four ARGs were plasmid-derived, 18 ARGs were phage-derived and an additional six ARGs were predicted to be mobile, contributing to their mobility and potential spread between bacteria.

Machine learning reveals the selection pressure exerted by nonantibiotic pharmaceuticals at environmentally relevant concentrations on antibiotic resistance genotypes.

The emergence and increasing prevalence of antibiotic resistance pose a global public risk for human health, and nonantimicrobial pharmaceuticals play an important role in this process. Herein, five nonantimicrobial pharmaceuticals, including acetaminophen (ACT), clofibric acid (CA), carbamazepine (CBZ), caffeine (CF) and nicotine (NCT), tetracycline-resistant strains, five ARGs (sul1, sul2, tetG, tetM and tetW) and one integrase gene (intI1), were detected in 101 wastewater samples during two typical sewage treatment processes including anaerobic-oxic (A/O) and biological aerated filter (BAF) in Harbin, China. The impact of nonantibiotic pharmaceuticals at environmentally relevant concentrations on both the resistance genotypes and resistance phenotypes were explored. The results showed that a significant impact of nonantibiotic pharmaceuticals at environmentally relevant concentrations on tetracycline resistance genes encoding ribosomal protection proteins (RPPs) was found, while no changes in antibiotic phenotypes, such as minimal inhibitory concentrations (MICs), were observed. Machine learning was applied to further sort out the contribution of nonantibiotic pharmaceuticals at environmentally relevant concentrations to different ARG subtypes. The highest contribution and correlation were found at concentrations of 1400-1800 ng/L for NCT, 900-1500 ng/L for ACT and 7000-10,000 ng/L for CF for tetracycline resistance genes encoding RPPs, while no significant correlation was found between the target compounds and ARGs when their concentrations were lower than 500 ng/L for NCT, 100 ng/L for ACT and 1000 ng/L for CF, which were higher than the concentrations detected in effluent samples. Therefore, the removal of nonantibiotic pharmaceuticals in WWTPs can reduce their selection pressure for resistance genes in wastewater.

Phages in sludge from the A/O wastewater treatment process play an important role in the transmission of ARGs.

Phages can influence the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs) through transduction, but their profiles and effects on the transmission of ARGs are unclear, especially in complex swine sludge. In this study, we investigated the characterization of phage and ARG profiles in sludge generated from anoxic/oxic (A/O) wastewater treatment processes on swine farms using metagenomes and viromes. The results demonstrated that 205-221 subtypes of ARGs could be identified in swine sludge, among which sul1, tet(M), and floR were the dominant ARGs, indicating that sludge is an important reservoir of ARGs, especially in sludge (S) tanks. The greater abundance of mobile genetic elements (MGEs) in the S tank could significantly contribute to the greater abundance of ARGs there compared to the anoxic (A) and oxic (O) tanks (P < 0.05). However, when we compared the abundances of ARGs and MGEs in the A and O tanks, we observed opposite significant differences (P < 0.05), suggesting that MGEs are not the only factor influencing the abundance of ARGs. The high proportion of lysogenic phages in sludge from the S tank can also have a major impact on the ARG profile. Siphoviridae, Myoviridae, and Podoviridae were the dominant phage families in sludge, and a network diagram of bacteria-ARG-phages revealed that dominant phages and bacteria acted simultaneously as potential hosts for ARGs, which may have led to phage-mediated HGT of ARGs. Therefore, the risk of phage-mediated HGT of ARGs cannot be overlooked.

Mobile genetic elements mediate the cross-media transmission of antibiotic resistance genes from pig farms and their risks.

Manure composting in traditional small-scale pig farms leads to the migration and diffusion of antibiotics and antibiotics resistance genes (ARGs) along the chain of transmission to the surrounding environment, increasing the risk of environmental resistance. Understanding the transmission patterns, driving factors, and health risks of ARGs on small-scale pig farms is important for effective control of ARGs transmission. This study was conducted on a small pig farm and its surrounding environment. The cross-media transmission of ARGs and their risks in the farming habitat were investigated using Metagenomic annotation and qPCR quantitative detection. The results indicate that ARGs in farms spread with manure pile-soil-channel sediment-mudflat sediment. Pig farm manure contributed 22.49 % of the mudflat sediment ARGs. Mobile genetic elements mediate the spread of ARGs across different media. Among them, tnpA and IS26 have the highest degree. Transmission of high-risk ARGs sul1 and tetM resulted in a 50 % and 116 % increase in host risk for sediment, respectively. This study provides a basis for farm manure management and control of the ARGs spread.

Deciphering Multidrug-Resistant Plasmids in Disinfection Residual Bacteria from a Wastewater Treatment Plant.

Current disinfection processes pose an emerging environmental risk due to the ineffective removal of antibiotic-resistant bacteria, especially disinfection residual bacteria (DRB) carrying multidrug-resistant plasmids (MRPs). However, the characteristics of DRB-carried MRPs are poorly understood. In this study, qPCR analysis reveals that the total absolute abundance of four plasmids in postdisinfection effluent decreases by 1.15 log units, while their relative abundance increases by 0.11 copies/cell compared to investigated wastewater treatment plant (WWTP) influent. We obtain three distinctive DRB-carried MRPs (pWWTP-01-03) from postdisinfection effluent, each carrying 9-11 antibiotic-resistant genes (ARGs). pWWTP-01 contains all 11 ARGs within an ∼25 Kbp chimeric genomic island showing strong patterns of recombination with MRPs from foodborne outbreaks and hospitals. Antibiotic-, disinfectant-, and heavy-metal-resistant genes on the same plasmid underscore the potential roles of disinfectants and heavy metals in the coselection of ARGs. Additionally, pWWTP-02 harbors an adhesin-type virulence operon, implying risks of both antibiotic resistance and pathogenicity upon entering environments. Furthermore, some MRPs from DRB are capable of transferring and could confer selective advantages to recipients under environmentally relevant antibiotic pressure. Overall, this study advances our understanding of DRB-carried MRPs and highlights the imminent need to monitor and control wastewater MRPs for environmental security.

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.

Occurrence and distribution of antibiotics and antibiotic resistance genes from the land to ocean in Daliao River-Liaodong Bay, China.

In this study, the pollution status of antibiotics and ARGs in sediments from the land-sea intersection of Liaodong Bay was analyzed. The results showed that the level of antibiotic pollution ranged from ND to 433.27 ng/kg, with quinolones and tetracycline as the dominant antibiotics. The relative abundance of ARGs ranged from 3.62 × 10-3 to 1.32 × 10-1 copies/16SrRNA copies, with aminoglycoside and MLSB resistance genes being dominant. Regarding spatial distribution, the land and estuary areas showed higher antibiotic pollution levels than the offshore areas. Similarly, the land and estuary areas exhibited higher antibiotic diversity than the offshore areas. The ARGs were widely distributed on land, and their abundance gradually decreased to the downstream estuary area. Land and coastal areas exhibited higher ARG diversity than estuary areas. Analysis of environmental factors revealed a significant correlation between ARGs and non-corresponding antibiotics, and some ARGs were affected by heavy metals Cu and Pb.

Comparison of qPCR and metagenomic sequencing methods for quantifying antibiotic resistance genes in wastewater.

Surveillance methods of circulating antibiotic resistance genes (ARGs) are of utmost importance in order to tackle what has been described as one of the greatest threats to humanity in the 21st century. In order to be effective, these methods have to be accurate, quickly deployable, and scalable. In this study, we compare metagenomic shotgun sequencing (TruSeq DNA sequencing) of wastewater samples with a state-of-the-art PCR-based method (Resistomap HT-qPCR) on four wastewater samples that were taken from hospital, industrial, urban and rural areas. ARGs that confer resistance to 11 antibiotic classes have been identified in these wastewater samples using both methods, with the most abundant observed classes of ARGs conferring resistance to aminoglycoside, multidrug-resistance (MDR), macrolide-lincosamide-streptogramin B (MLSB), tetracycline and beta-lactams. In comparing the methods, we observed a strong correlation of relative abundance of ARGs obtained by the two tested methods for the majority of antibiotic classes. Finally, we investigated the source of discrepancies in the results obtained by the two methods. This analysis revealed that false negatives were more likely to occur in qPCR due to mutated primer target sites, whereas ARGs with incomplete or low coverage were not detected by the sequencing method due to the parameters set in the bioinformatics pipeline. Indeed, despite the good correlation between the methods, each has its advantages and disadvantages which are also discussed here. By using both methods together, a more robust ARG surveillance program can be established. Overall, the work described here can aid wastewater treatment plants that plan on implementing an ARG surveillance program.

Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes.

Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40-60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.

Historical trajectories of antibiotics resistance genes assessed through sedimentary DNA analysis of a subtropical eutrophic lake.

Investigating the occurrence of antibiotic-resistance genes (ARGs) in sedimentary archives provides opportunities for reconstructing the distribution and dissemination of historical (i.e., non-anthropogenic origin) ARGs. Although ARGs in freshwater environments have attracted great attention, historical variations in the diversity and abundance of ARGs over centuries to millennia remain largely unknown. In this study, we investigated the vertical change patterns of bacterial communities, ARGs and mobile genetic elements (MGEs) found in sediments of Lake Chenghai spanning the past 600 years. Within resistome preserved in sediments, 177 ARGs subtypes were found with aminoglycosides and multidrug resistance being the most abundant. The ARG abundance in the upper sediment layers (equivalent to the post-antibiotic era since the 1940s) was lower than those during the pre-antibiotic era, whereas the ARG diversity was higher during the post-antibiotic era, possibly because human-induced lake eutrophication over the recent decades facilitated the spread and proliferation of drug-resistant bacteria. Statistical analysis suggested that MGEs abundance and the bacterial community structure were significantly correlated with the abundance and diversity of ARGs, suggesting that the occurrence and distribution of ARGs may be transferred between different bacteria by MGEs. Our results provide new perspectives on the natural history of ARGs in freshwater environments and are essential for understanding the temporal dynamics and dissemination of ARGs.

A metagenomic study of antibiotic resistance genes in a hypereutrophic subtropical lake contaminated by anthropogenic sources.

Antibiotic resistance genes (ARGs) are a major threat to human and environmental health. This study investigated the occurrence and distribution of ARGs in Lake Cajititlán, a hypereutrophic subtropical lake in Mexico contaminated by anthropogenic sources (urban wastewater and runoff from crop and livestock production). ARGs (a total of 475 genes) were detected in 22 bacterial genera, with Pseudomonas (144 genes), Stenotrophomonas (88 genes), Mycobacterium (54 genes), and Rhodococcus (27 genes) displaying the highest frequencies of ARGs. Among these, Pseudomonas aeruginosa and Stenotrophomonas maltophilia showed the highest number of ARGs. The results revealed a diverse array of ARGs, including resistance to macrolides (11.55 %), aminoglycosides (8.22 %), glycopeptides (6.22 %), tetracyclines (4 %), sulfonamides (4 %), carbapenems (1.11 %), phenicols (0.88 %), fluoroquinolones (0.44 %), and lincosamides (0.22 %). The most frequently observed ARGs were associated with multidrug resistance (63.33 %), with MexF (42 genes), MexW (36 genes), smeD (31 genes), mtrA (25 genes), and KHM-1 (22 genes) being the most common. Lake Cajititlán is a recreational area for swimming, fishing, and boating, while also supporting irrigation for agriculture and potentially acting as a drinking water source for some communities. This raises concerns about the potential for exposure to antibiotic-resistant bacteria through these activities. The presence of ARGs in Lake Cajititlán poses a significant threat to both human and environmental health. Developing strategies to mitigate the risks of antibiotic resistance, including improving wastewater treatment, and promoting strategic antibiotic use and disposal, is crucial. This study represents a significant advancement in the understanding of antibiotic resistance dynamics in a hypereutrophic subtropical lake in a developing country, providing valuable insights for the scientific community and policymakers.

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm.

With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

CH4 control and nitrogen removal from constructed wetlands by plant combination.

Global warming trend is accelerating. This study proposes a green and economical methane (CH4) control strategy by plant combination in constructed wetlands (CWs). In this study, a single planting of Acorus calamus L. hybrid constructed wetland (HCW-A) and a mixed planting of Acorus calamus L. and Eichhornia crassipes (Mart.) Solms hybrid constructed wetland (HCW-EA) were constructed. The differences in nitrogen removal performance and CH4 emissions between HCW-A and HCW-EA were compared and analyzed. The findings indicated that HCW-EA demonstrated significant improvements over HCW-A, with NH4+-N and TN removal rates increasing by 21.61% and 16.38% respectively, and CH4 emissions decreased by 43.36%. The microbiological analysis results showed that plant combination promoted the enrichment of Proteobacteria, Alphaproteobacteria and Bacillus. More nitrifying bacteria carrying nxrA genes and denitrifying bacteria carrying nirK genes accelerated the nitrogen transformation process. In addition, the absolute abundance ratio of pmoA/mcrA increased, reducing the release of CH4.

[Characteristics of Microorganisms and Antibiotic Resistance Genes of the Riparian Soil in the Lanzhou Section of the Yellow River].

Riparian soil is a critical area of watersheds. The characteristics of biological contaminants in riparian soil affect the pollution control of the watershed water environment. Thus, the microbial community structure, antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs) in the riparian soil of the Lanzhou section of the Yellow River were investigated by analyzing the characteristics of soil samples collected from farmland, mountains, and industrial land. The results showed that the Proteobacteria, Bacteroidetes, and Actinobacteria were the dominant phyla in the riparian soil of Lanzhou section of the Yellow River. The microbial structure in the riparian soil was significantly correlated with the land use type (P < 0.05). The α diversity index of bacterial communities in land types was in the order of farmland > mountain > industry. Sulfonamide-typed ARGs were the most dominant genes in the soil of the Lanzhou section of the Yellow River Basin, among which the sul1 gene had the highest abundance, 20-36 000 times that of other detected ARGs. Moreover, the total absolute abundance of ARGs in industrial soil was the highest. Principal coordinate analysis (PCoA) displayed that the ARGs characteristics had a significant correlation with land types (P < 0.05), and intl1 and tnpA-04 drove the diffuseness of sulfonamide and tetracycline ARGs, respectively. Redundancy analysis (RDA) demonstrated that the content of inorganic salt ions and total phosphorus in the soil of the riparian zone of the Yellow River Lanzhou section were the main environmental factors, modifying the distribution of the microbial structure. Halobacterota and Acidobacteriota were the main microflora that drove the structural change in ARGs.

Fate and removal of fluoroquinolone antibiotics in mesocosmic wetlands: Impact on wetland performance, resistance genes and microbial communities.

The fate of fluoroquinolone antibiotics norfloxacin and ofloxacin were investigated in mesocosmic wetlands, along with their effects on nutrients removal, antibiotic resistance genes (ARGs) and epiphytic microbial communities on Hydrilla verticillate using bionic plants as control groups. Approximately 99% of norfloxacin and ofloxacin were removed from overlaying water, and H. verticillate inhibited fluoroquinolones accumulation in surface sediments compared to bionic plants. Partial least squares path modeling showed that antibiotics significantly inhibited the nutrient removal capacity (0.55) but had no direct effect on plant physiology. Ofloxacin impaired wetland performance more strongly than norfloxacin and more impacted the primary microbial phyla, whereas substrates played the most decisive role on microbial diversities. High antibiotics concentration shifted the most dominant phyla from Proteobacteria to Bacteroidetes and inhibited the Xenobiotics biodegradation function, contributing to the aggravation in wetland performance. Dechloromonas and Pseudomonas were regarded as the key microorganisms for antibiotics degradation. Co-occurrence network analysis excavated that microorganisms degrade antibiotics mainly through co-metabolism, and more complexity and facilitation/reciprocity between microbes attached to submerged plants compared to bionic plants. Furthermore, environmental factors influenced ARGs mainly by altering the community dynamics of differential bacteria. This study offers new insights into antibiotic removal and regulation of ARGs accumulation in wetlands with submerged macrophyte.

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Antibiotic resistance genes (ARGs) have attracted widespread attention as a new global pollutant, mainly due to the abuse of antibiotics. To investigate the diversity of ARGs in three rodent species, we used metagenomic sequencing analysis to analyze the diversity of antibiotic resistance genes of 17 individuals of Apodemus peninsulae and 17 individuals of Myodes rufocanus collected from Mudanfeng, and nine individuals of Apodemus agrarius collected from Sandaoguan. A total of 19 types and 248 subclasses of ARGs were detected in the three rodent species. Seven ARGs showed significant difference and five ARGs showed extremely significant difference between M. rufocanus and A. agrarius. Seven ARGs showed significant difference and four ARGs showed extremely significant difference between A. peninsulae and A. agrarius. Four ARGs showed significant difference and five ARGs showed extremely significant difference between M. rufocanus and A. peninsulae. ARGs showing high abundance in three rodents were macrolides, lincoamides, tetracyclines, and ß-lactams. ARGs were widely distributed in the three rodent species. The significant differences in ARGs among different species might be due to the different distribution areas and their diet differentiation. The study could provide a basis for further studies of ARGs in mice and improve the understanding of the harm of ARGs transmission.

Soil Microbiomes and their Arsenic Functional Genes in Chronically High-Arsenic Contaminated Soils.

Microbial arsenic transformations play essential roles in controlling pollution and ameliorating risk. This study combined high-throughput sequencing and PCR-based approaches targeting both the 16 S rRNA and arsenic functional genes to investigate the temporal and spatial dynamics of the soil microbiomes impacted by high arsenic contamination (9.13 to 911.88 mg/kg) and to investigate the diversity and abundance of arsenic functional genes in soils influenced by an arsenic gradient. The results showed that the soil microbiomes were relatively consistent and mainly composed of Actinobacteria (uncultured Gaiellales and an unknown_67 - 14 bacterium), Proteobacteria, Firmicutes (particularly, Bacillus), Chloroflexi, and Acidobacteria (unknown_Subgroup_6). Although a range of arsenic functional genes (e.g., arsM, arsC, arrA, and aioA) were identified by shotgun metagenomics, only the arsM gene was detected by the PCR-based method. The relative abundance of the arsM gene accounted for 0.20%-1.57% of the total microbial abundance. Combining all analyses, arsenic methylation mediated by the arsM gene was proposed to be a key process involved in the arsenic biogeochemical cycle and mitigation of arsenic toxicity. This study advances our knowledge about arsenic mechanisms over the long-term in highly contaminated soils.

Unseen Weapons: Bacterial Extracellular Vesicles and the Spread of Antibiotic Resistance in Aquatic Environments.

This paper sheds light on the alarming issue of antibiotic resistance (ABR) in aquatic environments, exploring its detrimental effects on ecosystems and public health. It examines the multifaceted role of antibiotic use in aquaculture, agricultural runoff, and industrial waste in fostering the development and dissemination of resistant bacteria. The intricate interplay between various environmental factors, horizontal gene transfer, and bacterial extracellular vesicles (BEVs) in accelerating the spread of ABR is comprehensively discussed. Various BEVs carrying resistance genes like blaCTX-M, tetA, floR, and sul/I, as well as their contribution to the dominance of multidrug-resistant bacteria, are highlighted. The potential of BEVs as both a threat and a tool in combating ABR is explored, with promising strategies like targeted antimicrobial delivery systems and probiotic-derived EVs holding significant promise. This paper underscores the urgency of understanding the intricate interplay between BEVs and ABR in aquatic environments. By unraveling these unseen weapons, we pave the way for developing effective strategies to mitigate the spread of ABR, advocating for a multidisciplinary approach that includes stringent regulations, enhanced wastewater treatment, and the adoption of sustainable practices in aquaculture.