Predicting antibiotic resistance gene abundance in activated sludge using shotgun metagenomics and machine learning

Machine learning predicts the growth of cyanobacterial genera in river systems and reveals their different environmental responses

Cyanobacterial blooms are a common and serious problem in global freshwater environments. However, the response mechanisms of various cyanobacterial genera to multiple nutrients and pollutants, as well as the factors driving their competitive dominance, remain unclear or controversial. The relative abundance and cell density of two dominant cyanobacterial genera (i.e., Cyanobium and Microcystis) in river ecosystems along a gradient of anthropogenic disturbance were predicted by random forest with post-interpretability based on physicochemical indices. Results showed that the optimized predictions all reached strong fitting with R2 > 0.75, and conventional water quality indices played a dominant role. One-dimensional and two-dimensional partial dependence plot (PDP) revealed that the responses of Cyanobium and Microcystis to nutrients and temperature were similar, but they showed differences in preferrable nutrient utilization and response to pollutants. Further prediction and PDP for the ratio of Cyanobium and Microcystis unveiled that their distinct responses to PAHs and SPAHs were crucial drivers for their competitive dominance over each other. This study presents a new way for analyzing the response of cyanobacterial genera to multiple environmental factors and their dominance relationships by interpretable machine learning, which is suitable for the identification and interpretation of high-dimensional nonlinear ecosystems with complex interactions.

Understanding the mechanism of microplastic-associated antibiotic resistance genes in aquatic ecosystems: Insights from metagenomic analyses and machine learning

The pervasive presence of microplastics (MPs) in aquatic systems facilitates the transmission of antibiotic resistance genes (ARGs), thereby posing risks to ecosystems and human well-being. However, owing to variations in environmental backgrounds and the limited scope of research subjects, studies on ARGs in MPs lack unified conclusions, particularly regarding whether different types of MPs selectively promote ARG enrichment. Analysing large-scale datasets can better encompass broad spatiotemporal scales and diverse samples, facilitating a more extensive exploration of the complex ecological relationships between MPs and ARGs. The present study integrated existing metagenomic datasets to conduct a comprehensive risk assessment and comparative analysis of resistance groups across various MPs. In addition, we endeavoured to elucidate potential associations between ARGs and bacterial taxa, as well as MP structural features, using machine learning (ML) methods. The findings of our research highlight the pivotal role of MP type in shaping plastispheres, accounting for 9.56 % of the biotic variation (Adonis index) and explaining 18.59 % of the ARG variance. Compared to conventional MPs, biodegradable MPs, such as polyhydroxyalkanoates (PHA) and polylactic acid (PLA), exhibit lower species uniformity and diversity but pose a higher risk of ARG occurrence. These ML approaches effectively forecasted ARG abundance by using the bacterial taxa and molecular structure descriptors (MDs) of MPs (average R2tra = 0.882, R2test = 0.759). Feature analysis showed that MDs associated with lipophilicity, solubility, toxicity, and surface potential significantly influenced the relative abundance of ARGs in the plastispheres. The interpretable multiple linear regression (MLR) model, particularly notable, elucidated a linear relationship between bacterial genera and ARGs, offering promise for identifying potential ARG hosts. This study offers novel insights into ARG dynamics and ecological risks within aquatic plastispheres, highlighting the importance of comprehensive MP monitoring initiatives.

A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments

The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the antibiotic resistance genes and bacteria-related data collection via field sampling and laboratory testing is time-consuming and expensive, designing a rapid approach to quantify the burden of AMR in the natural aquatic environment has become a challenge. To cope with such a challenge, a new approach involving an integrated machine-learning framework was developed by investigating the associations between the relative burden of AMR and easily accessible variables (i.e., relevant environmental variables and adjacent land-use patterns). The results, based on a real-world case analysis, demonstrate that the quantification speed has been reduced from 3-7 days, which is typical for traditional measurement procedures with field sampling and laboratory testing, to approximately 0.5 hours using the new approach. Moreover, all five metrics for AMR relative burden quantification exceed the threshold level of 85%, with F1-score surpassing 0.92. Compared to logistic regression, decision trees, and basic random forest, the adaptive random forest model within the framework significantly improves quantification accuracy without sacrificing model interpretability. Two environmental variables, dissolved oxygen and resistivity, along with the proportion of green areas were identified as three key feature variables for the rapid quantification. This study contributes to the enrichment of burden analyses and management practices for rapid quantification of the relative burden of AMR without dose-response information.

Specialists regulate microbial network and community assembly in subtropical seagrass sediments under differing land use conditions

Microorganisms in the sediment play a pivotal role in the functioning and stability of seagrass ecosystems and their dynamics are influenced by the nutrient acquisition strategies of host plants. While the distinct impacts of microbial generalists and specialists on community dynamics are recognized, their distribution patterns and ecological roles within seagrass ecosystems remain largely unexplored. To address this issue, we conducted an analysis of community assembly processes and co-occurrence relationships of both microbial generalists and specialists within sediment profiles (0-100 cm) from seagrass habitats subjected to differing land use conditions. The results revealed that seagrasses in Yifeng Estuary experienced the large proportion of cultivated land and exhibited higher organic carbon content in the 0-20 cm surface sediment layer. Nitrogen-cycling bacteria were predominantly associated with seagrasses from Yifeng Estuary, whereas Vibrio spp. was more prevalent in seagrasses from Liusha Bay. Notably, seagrass Halophia beccarii (YHB) in Yifeng Estuary harbored higher niche breadths for both microbial generalist and specialist compared to Halodule uninervis (LHU) and Halophia ovalis (LHO) from Liusha Bay. Stochastic processes were pivotal in shaping seagrass sediment microbial communities, with a higher immigration rate observed in YHB, suggesting greater microbial turnover in this area. Additionally, YHB sediment presented lower drift and higher dispersal limitation among generalists compared to LHU and LHO, whereas the pattern was reversed among specialists. Specialists were found to play a crucial role in shaping microbial interactions within YHB sediment, with genera Halioglobus identified as keystone species in the network. The specialists were further found to significantly influence microbial β-diversity in seagrass sediment directly. Overall, our findings illustrated how microbial generalists and specialists were distributed in seagrass sediments in response to land use changes and provided new insights into the potential roles of microbial regulation in degraded seagrass ecosystems.

Chlorinated organophosphorus flame retardants induce the propagation of antibiotic resistance genes in sludge fermentation systems: Insight of chromosomal mutation and microbial traits

Waste activated sludge (WAS) is a critical reservoir for antibiotic resistance genes (ARGs) due to the prevalent misuse of antibiotics. Horizontal gene transfer (HGT) is the primary mechanism for ARGs spread through mobile genetic elements (MGEs). However, the role of non-antibiotic organophosphorus flame retardants (Cl-OFRs) in ARG transmission in the WAS fermentation system remains unclear. This study examines the effects of tris(2-chloroethyl) phosphate (TCEP), a representative Cl-OFR, on ARG dynamics in WAS fermentation using molecular docking and metagenomic analysis. The results showed a 33.4 % increase in ARG abundance in the presence of TCEP. Interestingly, HGT did not appear to be the primary mechanism of ARG dissemination under TCEP stress, as evidenced by a 2.51 % decrease in MGE abundance. TCEP binds to sludge through hydrogen bonds with a binding energy of - 3.6 kJ/mol, leading to microbial damage and an increase in the proportion of non-viable cells. This interaction prompts a microbial shift toward Firmicutes with thick cell walls, which are significant ARG carriers. Additionally, TCEP induces chromosomal mutations through oxidative stress and the SOS response, contributing to ARG formation. Microorganisms also develop multidrug resistance mechanisms to expel TCEP and mitigate its toxicity. This study provides a comprehensive understanding of Cl-OFRs effects on the ARGs fates in WAS fermentation system and offers guidance for the safe and efficient treatment of Cl-OFRs and WAS.

Assessing the removal of heavy metals and polycyclic aromatic hydrocarbons and occurrence of metal resistance genes and antibiotic resistance genes in a stormwater bioretention system

Bioretention basins are extensively used in urban areas to manage stormwater by reducing peak flows and pollution. This study evaluated the performance of a bioretention basin in removing heavy metals, polycyclic aromatic hydrocarbons (PAHs), and oil and grease. Using droplet digital PCR (ddPCR), the presence of metal resistance genes (MRGs) and antibiotic resistance genes (ARGs) in the basin's soil was analyzed. The results indicated effective removal of Zn (67%), but higher concentration of Mg was observed at the outlet. Cu, Fe and Pb showed no significant differences in the in- and outflow concentrations. The system successfully removed 82% of influent PAHs. Soil samples collected in summer and fall revealed higher MRG abundance in summer, with copA being the most prevalent MRG (1.2-4.8 log10 copies/g soil). Among the ARGs, sul1 was consistently found throughout the basin (2.5-6.7 log10 copies/g soil), while tetW was detected primarily at the basin's start and end in the topsoil layer. Rubellimicrobium and Geobacter were identified as potential carriers of ARGs/MRGs. Although the concentration of metals in soil was not measured in the current study, these findings emphasize the need to understand heavy metal distribution and the occurrence of MRGs and ARGs in stormwater control systems to improve their design and effectiveness.

An inclusive study to elucidation the heavy metals-derived ecological risk nexus with antibiotic resistome functional shape of niche microbial community and their carbon substrate utilization ability in serpentine soil

Heavy metals (HMs) contained terrestrial ecosystems are often significantly display the antibiotic resistome in the pristine area due to increasing pressure from anthropogenic activity, is complex and emerging research interest. This study investigated that impact of chromium (Cr), nickel (Ni), cobalt (Co) concentrations in serpentine soil on the induction of antibiotic resistance genes and antimicrobial resistance within the native bacterial community as well as demonstrated their metabolic fingerprint. The full-length 16S-rRNA amplicon sequencing observed an increased abundance of Firmicutes, Actinobacteriota, and Acidobacteriota in serpentine soil. The microbial community in serpentine soil displayed varying preferences for different carbon sources, with some, such as carbohydrates and carboxylic acids, being consistently favored. Notably, 27 potential antibiotic resistance opportunistic bacterial genera have been identified in different serpentine soils. Among these, Lapillicoccus, Rubrobacter, Lacibacter, Chloroplast, Nitrospira, Rokubacteriales, Acinetobacter, Pseudomonas were significantly enriched in high and medium HMs concentrated serpentine soil samples. Functional profiling results illustrated that vancomycin resistance pathways were prevalent across all groups. Additionally, beta-lactamase, aminoglycoside, tetracycline, and vancomycin resistance involving specific bio-maker genes (ampC, penP, OXA, aacA, strB, hyg, aph, tet(A/B), otr(C), tet(M/O/Q), van(A/B/D), and vanJ) were the most abundant and enriched in the HMs-contaminated serpentine soil. Overall, this study highlighted that heavy-metal enriched serpentine soil is potential to support the proliferation of bacterial antibiotic resistance in native microbiome, and might able to spread antibiotic resistance to surrounding environment.

Meta-analysis of wastewater microbiome for antibiotic resistance profiling

The microbial composition and stress molecules are main drivers influencing the development and spread of antibiotic resistance bacteria (ARBs) and genes (ARGs) in the environment. A reliable and rapid method for identifying associations between microbiome composition and resistome remains challenging. In the present study, secondary metagenome data of sewage and hospital wastewaters were assessed for differential taxonomic and ARG profiling. Subsequently, Random Forest (RF)-based ML models were used to predict ARG profiles based on taxonomic composition and model validation on hospital wastewaters. Total ARG abundance was significantly higher in hospital wastewaters (15 ppm) than sewage (5 ppm), while the resistance towards methicillin, carbapenem, and fluoroquinolone were predominant. Although, Pseudomonas constituted major fraction, Streptomyces, Enterobacter, and Klebsiella were characteristic of hospital wastewaters. Prediction modeling showed that the relative abundance of pathogenic genera Escherichia, Vibrio, and Pseudomonas contributed most towards variations in total ARG count. Moreover, the model was able to identify host-specific patterns for contributing taxa and related ARGs with >90% accuracy in predicting the ARG subtype abundance. More than >80% accuracy was obtained for hospital wastewaters, demonstrating that the model can be validly extrapolated to different types of wastewater systems. Findings from the study showed that the ML approach could identify ARG profile based on bacterial composition including 16S rDNA amplicon data, and can serve as a viable alternative to metagenomic binning for identification of potential hosts of ARGs. Overall, this study demonstrates the promising application of ML techniques for predicting the spread of ARGs and provides guidance for early warning of ARBs emergence.

Achieving high nitrogen and antibiotics removal efficiency by nZVI-C in partial nitritation/anammox system with a single-stage membrane-aerated biofilm reactor

This study innovated constructed an activated carbon-loaded nano-zero-valent iron (nZVI-C) enhanced membrane aerated biofilm reactor (MABR) coupled partial nitritation/anammox (PN/A) system for optimizing nitrogen and antibiotics removal. Results showed that nitrogen and antibiotic removal efficiencies of 88.45 ± 0.14% and 89.90 ± 3.07% were obtained by nZVI-C, respectively. nZVI-C hastened Nitrosomonas enrichment (relative abundance raised from 2.85% to 12.28%) by increasing tryptophan content in EPS. Furthermore, nZVI-C proliferated amo gene by 3.92 times and directly generated electrons, stimulating Ammonia monooxygenase (AMO) co-metabolism activity. Concurrently, via antibiotic resistance genes (ARGs) horizontal transfer, Nitrosomonas synergized with Arenimonas and Comamonadaceae for efficient antibiotic removal. Moreover, nZVI-C mitigated antibiotics inhibition of electron transfer by proliferating genes for PN and anammox electron production (hao, hdh) and utilization (amo, hzs, nir). That facilitated electron transfer and synergistic substrate conversion between ammonia oxidizing bacteria (AOB) and anaerobic ammonia oxidizing bacteria (AnAOB). Finally, the high nitrogen removal efficiency of the MABR-PN/A system was achieved.

Integron-associated genes are reliable indicators of antibiotic resistance in wastewater despite treatment- and seasonality-driven fluctuations

The present study aims to characterize the bacterial community, resistome and integron abundance of a municipal wastewater treatment plant (WWTP) over the course of 12 months and evaluate the year-long performance of integron-related genes as potential indicators of antibiotic resistance mechanisms in influents and effluents. For that, total DNA was extracted and subjected to 16S rRNA-targeted metabarcoding, high-throughput (HT) qPCR (48 targets) and standard qPCR (5 targets). Targets included integrase genes, antibiotic resistance genes (ARGs) and putative pathogenic groups. A total of 16 physicochemical parameters determined in the wastewater samples were also considered. Results revealed that the WWTP treatment significantly impacted the bacterial community, as well as the content in ARGs and integrase genes. Indeed, there was a relative enrichment from influent to effluent of 13 pathogenic groups (e.g., Legionella and Mycobacterium) and genes conferring resistance to sulphonamides, aminoglycosides and disinfectants. Effluent samples (n = 25) also presented seasonal differences, with an increase of the total ARGs' concentration in summer, and differences between winter and summer on relative abundance of sulphonamide and disinfectant resistance mechanisms. From the eight putative integron-related genes selected, all were positively correlated with the total ARGs' content in wastewater and the relative abundance of resistance to most of the specific antibiotic classes. The genes intI1, blaGES and qacE∆1 were the most strongly correlated with the total concentration of ARGs. Genes blaGES and blaVIM, were better correlated to resistance to beta-lactams, aminoglycosides and tetracyclines. This study supports the use of integron-related genes as powerful indicators of antibiotic resistance in wastewater, being robust despite the variability caused by wastewater treatment and seasonality.

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

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

Deciphering and predicting changes in antibiotic resistance genes during pig manure aerobic composting via machine learning model

Livestock manure is one of the most important pools of antibiotic resistance genes (ARGs) in the environment. Aerobic composting can effectively reduce the spread of antibiotic resistance risk in livestock manure. Understanding the effect of aerobic composting process parameters on manure-sourced ARGs is important to control their spreading risk. In this study, the effects of process parameters on ARGs during aerobic composting of pig manure were explored through data mining based on 191 valid data collected from literature. Machine learning (ML) models (XGBoost and Random Forest) were utilized to predict the rate of ARGs changes during pig manure composting. The model evaluation index of the XGBoost model (R2 = 0.651) was higher than that of the Random Forest (R2 = 0.490), indicating that XGBoost had better prediction performance. Feature importance was further calculated for the XGBoost model, and the XGBoost black box model was interpreted by Shapley additive explanations analysis. Results indicated that the influencing factors on the ARGs variation in pig manure were sequentially divided into thermophilic period, total composting period, composting real time, and thermophilic stage average temperature. The findings gave an insight into the application of ML models to predict and decipher the ARG changes during manure composting and provided suggestions for better composting manipulation and optimization of process parameters.

Composting reduces the risks of resistome in beef cattle manure at the transcriptional level

Transcriptomic evidence is needed to determine whether composting is more effective than conventional stockpiling in mitigating the risk of resistome in livestock manure. The objective of this study is to compare composting and stockpiling for their effectiveness in reducing the risk of antibiotic resistance in beef cattle manure. Samples collected from the center and the surface of full-size manure stockpiling and composting piles were subject to metagenomic and metatranscriptomic analyses. While the distinctions in resistome between stockpiled and composted manure were not evident at the DNA level, the advantages of composting over stockpiling were evident at the transcriptomic level in terms of the abundance of antibiotic resistance genes (ARGs), the number of ARG subtypes, and the prevalence of high-risk ARGs (i.e., mobile ARGs associated with zoonotic pathogens). DNA and transcript contigs show that the pathogen hosts of high-risk ARGs included Escherichia coli O157:H7 and O25b:H4, Klebsiella pneumoniae, and Salmonella enterica. Although the average daily temperatures for the entire composting pile exceeded 55°C throughout the field study, more ARG and ARG transcripts were removed at the center of the composting pile than at the surface. This work demonstrates the advantage of composting over stockpiling in reducing ARG risk in active populations in beef cattle manure.IMPORTANCEProper treatment of manure before land application is essential to mitigate the spread of antibiotic resistance in the environment. Stockpiling and composting are two commonly used methods for manure treatment. However, the effectiveness of composting in reducing antibiotic resistance in manure has been debated. This work compared the ability of these two methods to reduce the risk of antibiotic resistance in beef cattle manure. Our results demonstrate that composting reduced more high-risk resistance genes at the transcriptomic level in cattle manure than conventional stockpiling. This finding not only underscores the effectiveness of composting in reducing antibiotic resistance in manure but also highlights the importance of employing RNA analyses alongside DNA analyses.

Mutation-driven resistance development in wastewater E. coli upon low-level cephalosporins: Pharmacophore contribution and novel mechanism

Cephalosporins have been widely applied in clinical and veterinary settings and detected at increasing concentrations in water environments. They potentially induce high-level antibiotic resistance at environmental concentrations. This study characterized how typical wastewater bacteria developed heritable antibiotic resistance under exposure to different cephalosporins, including pharmacophore-resistance correlation, resistance mechanism, and occurrence of resistance-relevant mutations in different water environments. Wastewater-isolated E. coli JX1 was exposed to eight cephalosporins individually at 25 µg/L for 60 days. Multidrug resistance developed and diverse mutations arose in selected mutants, where a single mutation in ATP phosphoribosyltransferase encoding gene (hisG) resulted in up to 128-fold increase in resistance to meropenem. Molprint2D pharma RQSAR analysis revealed that hydrogen-bond acceptors and hydrophobic groups in the R1 and R2 substituents of cephalosporins contributed positively to antibiotic resistance. Some of these pharmacophores may persist during bio- or photo-degradation in the environment. hisG mutation confers a novel resistance mechanism by inhibiting fatty acid degradation, and its variants were more abundant in water-related E. coli (especially in the effluent of wastewater treatment plants) compared with those in non-water environments. These results suggest that specific degradation of particular pharmacophores in cephalosporins could be useful for controlling resistance development, and mutations in previously unreported resistance genes (e.g., hisG) can lead to overlooked antibiotic resistance risks in water environments.

Deterministic effect of oxygen level variation on shaping antibiotic resistome

An increase in acquisition of antibiotic resistance genes (ARGs) by pathogens under antibiotic selective pressure poses public health threats. Sub-inhibitory antibiotics induce bacteria to generate reactive oxygen species (ROS) dependent on dissolved oxygen (DO) levels, while molecular connection between ROS-mediated ARG emergence through DNA damage and metabolic changes remains elusive. Thus, the study investigates antibiotic resistome dynamics, microbiome shift, and pathogen distribution in hyperoxic (5-7 mg L-1), normoxic (2-4 mg L-1), and hypoxic (0.5-1 mg L-1) conditions using lab-scale bioreactor. Composite inoculums in the reactor were designed to represent comprehensive microbial community and AR profile from selected activated sludge. RT-qPCR and metagenomic analysis showed an increase in ARG count (100.98 ppm) with enrichment of multidrug efflux pumps (acrAB, mexAB) in hyperoxic condition. Conversely, total ARGs decreased (0.11 ppm) under hypoxic condition marked by a major decline in int1 abundance. Prevalence of global priority pathogens increased in hyperoxic (22.5%), compared to hypoxic (0.9%) wherein major decrease were observed in Pseudomonas, Shigella, and Borrelia. The study observed an increase in superoxide dismutase (sodA, sodB), DNA repair genes (nfo, polA, recA, recB), and ROS (10.4 µmol L-1) in adapted biomass with spiked antibiotics. This suggests oxidative damage that facilitates stress-induced mutagenesis providing evidence for observed hyperoxic enrichment of ARGs. Moreover, predominance of catalase (katE, katG) likely limit oxidative damage that deplete ARG breeding in hypoxic condition. The study proposes a link between oxygen levels and AR development that offers insights into mitigation and intervention of AR by controlling oxygen-related stress and strategic selection of bacterial communities.

Machine learning modeling for the prediction of phosphorus and nitrogen removal efficiency and screening of crucial microorganisms in wastewater treatment plants

The effectiveness of wastewater treatment plants (WWTPs) is largely determined by the microbial community structure in their activated sludge (AS). Interactions among microbial communities in AS systems and their indirect effects on water quality changes are crucial for WWTP performance. However, there is currently no quantitative method to evaluate the contribution of microorganisms to the operating efficiency of WWTPs. Traditional assessments of WWTP performance are limited by experimental conditions, methods, and other factors, resulting in increased costs and experimental pollutants. Therefore, an effective method is needed to predict WWTP efficiency based on AS community structure and quantitatively evaluate the contribution of microorganisms in the AS system. This study evaluated and compared microbial communities and water quality changes from WWTPs worldwide by meta-analysis of published high-throughput sequencing data. Six machine learning (ML) models were utilized to predict the efficiency of phosphorus and nitrogen removal in WWTPs; among them, XGBoost showed the highest prediction accuracy. Cross-entropy was used to screen the crucial microorganisms related to phosphorus and nitrogen removal efficiency, and the modeling confirmed the reasonableness of the results. Thirteen genera with nitrogen and phosphorus cycling pathways obtained from the screening were considered highly appropriate for the simultaneous removal of phosphorus and nitrogen. The results showed that the microbes Haliangium, Vicinamibacteraceae, Tolumonas, and SWB02 are potentially crucial for phosphorus and nitrogen removal, as they may be involved in the process of phosphorus and nitrogen removal in sewage treatment plants. Overall, these findings have deepened our understanding of the relationship between microbial community structure and performance of WWTPs, indicating that microbial data should play a critical role in the future design of sewage treatment plants. The ML model of this study can efficiently screen crucial microbes associated with WWTP system performance, and it is promising for the discovery of potential microbial metabolic pathways.

Identifying the drivers of chlorophyll-a dynamics in a landscape lake recharged by reclaimed water using interpretable machine learning

The water quality of lakes recharged by reclaimed water is affected by both the fluctuation of reclaimed water quality and the biochemical processes in the lakes, and therefore the main controlling factors of algal blooms are difficult to identify. Taking a typical landscape lake recharged by reclaimed water as an example and using the spatiotemporal distribution characteristics and correlation analysis of water quality indexes, we propose an interpretable machine learning framework based on random forest to predict chlorophyll-a (Chl-a). The model considered nutrient difference indexes between reclaimed water and lake water, and further used feature importance ranking and partial dependence plot to identify nutrient drivers. Results show that the NO3--N input from reclaimed water is the dominant nutrient driver for algal bloom especially at high temperatures, and the negative correlation between NO3--N and Chl-a in the lake water is the consequence of algal bloom rather than the cause. Our study provides new insights into the identification of eutrophication factors for lakes recharged by reclaimed water.

Model of shrimp pond-mediated spatiotemporal dynamic distribution of antibiotic resistance genes in the mangrove habitat of a subtropical gulf

Aquacultures are the main reason for the environmental selection of antibiotic resistance genes (ARGs), resulting in the enrichment of ARGs. As a filter, a marine mangrove ecosystem can reduce antimicrobial resistance (AMR) or eliminate ARGs; however, its elimination mechanism remains unclear. This study investigated the spatiotemporal dynamic distribution of ARGs in two different types of mangrove habitats (shrimp ponds and virgin forests), within a subtropical gulf located in the Beibu Gulf, China, during dry and wet seasons by using metagenomics and real time quantitative polymerase chain reaction (RT-qPCR) analysis. As the key environmental factors, sulfide, salinity, and mobile genetic elements significantly were found to contribute to ARGs distribution, respectively. Wet and dry seasons influenced the dispersal of ARGs but did not affect the microbial community structure. Three potential biomarkers, TEM-116, smeD, and smeE, played key roles in seasonal differences. The key different genes in the biological relevance of absolute abundance were demonstrated by RT-qPCR. Co-occurrence network analysis indicated that high-abundance ARGs were distributed in a modular manner. For the first time, a risk index weighted by risk rank (RIR) was proposed and used to quantify the human risk of ARGs in the mangrove metagenome. The shrimp ponds during the wet season showed the highest RIR detected. In addition to offering a perspective on reducing AMR in mangrove wetlands, this study constructed the first spatiotemporal dynamic model of ARGs in the Beibu Gulf, China and contributed to revealing the global spread of ARGs. Meanwhile, this study proposes a new pipeline for assessing the risk of ARGs, while also exploring the concept of "One Health."

Mapping the spread and mobility of antibiotic resistance in wastewater due to COVID-19 surge

Large amounts of antibiotics have been discharged into wastewater during the COVID-19 pandemic due to overuse and misuse of antibiotics to treat patients. Wastewater-based surveillance can be used as an early warning for antibiotic resistance (AR) emergence. The present study analyzed municipal wastewater corresponding to the major pandemic waves (WW1, WW2, and WW3) in India along with hospital wastewater (Ho) taken as a benchmark for AR. Commonly prescribed antibiotics during a pandemic, azithromycin and cefixime residues, were found in the range of 2.1-2.6 μg/L in Ho and WW2. Total residual antibiotic concentration was less in WW2; however, the total antibiotic resistance gene (ARG) count was 1065.6 ppm compared to 85.2 ppm in Ho. Metagenome and RT-qPCR analysis indicated a positive correlation between antibiotics and non-corresponding ARGs (blaOXA, aadA, cat, aph3, and ere), where 7.2-7.5% was carried by plasmid in the bacterial community of WW1 and WW2. Moreover, as the abundance of the dfrA and int1 genes varied most among municipal wastewater, they can be suggested as AR markers for the pandemic. The common pathogens Streptococcus, Escherichia, Shigella, and Aeromonas were putative ARG hosts in metagenome-assembled genomes. The ARG profile and antibiotic levels varied between municipal wastewaters but were fairly similar for WW2 and Ho, suggesting the impact of the pandemic in shaping the resistome pattern. The study provides insights into the resistome dynamic, AR markers, and host-ARG association in wastewater during the COVID-19 surge. Continued surveillance and identification of intervention points for AR beyond the pandemic are essential to curbing the environmental spread of ARGs in the near future.

Fecal-related anthropogenic sources are key determinants of lake microbiomes through microbial source tracking

Lake microbiomes are essential indicators of lake health and are strongly influenced by allochthonous microbial communities from various sources within the watershed. However, quantifying the contributions of multiple inputs to lake microbiomes is challenging because of the complex nature of river‒lake systems and the presence of many untraceable sources. Here, Jianhu Lake‒‒a geographically simple and closed plateau lake in southwestern China, was surveyed to disentangle the contributions of five distinct sources (three input rivers that receive town sewage, stormwater runoff, and creek spring water, as well as two nonpoint sources, duck ponds and dry farmland) to the overall lake microbiomes. We found that feces-loading sources, namely town sewage and duck aquaculture, accounted for 48.7% of the total variations in lake microbiomes. In contrast, the combined contribution of the remaining three sources amounted to 13.21%, despite these less-influential sources (e.g., stormwater runoff) may introduce an even larger volume of allochthonous materials into the lake. In addition, approximately 38.1% of the variations in the lake microbiomes were attributed to unknown sources. Sewage effluents also caused a significant loss of lake microbial diversity, and there was a tendency for large-scale microbial homogeneity in lake sediments that resembled those from duck ponds. We then used a targeted approach to track host-specific fecal pollution, and found that human feces were the primary source, followed by ruminant and chicken/duck feces, all of which can be successfully traced back to the feces-loading sources. In our further modelling of sediment transport from three rivers into the whole lake, we observed a significant relationship between sediment accumulation and adsorbed microorganisms only for the sewage-receiving river. Together, lines of evidence indicate that both point and nonpoint fecal-related anthropogenic sources possess discriminatory power for shaping microbial geographic patterns of the lake, posing threats to the survival of local indigenous lake microbiomes.

Effects of soil solarization combined with manure-amended on soil ARGs and microbial communities during summer fallow

Soil solarization (SS) is a technique for managing pathogens and weeds, which involves covering with transparent plastic to increase soil temperature during summer fallow (SF). However, SS also alters the diversity of bacterial communities. Therefore, during SF, various organic modifiers are used in combination with SS to improve its efficacy. Organic amendments may contain antibiotic resistance genes (ARGs). Greenhouse vegetable production (GVP) soils are vital to ensure food security and ecological balance. However, comprehensive study on the effects of SS combined with different types of manure on ARGs in GVP soils during SF remains unclear. Therefore, this study employed high-throughput qPCR to explore the effects of different organic amendments combined with SS on the abundance changes of ARGs and mobile genetic elements (MGEs) in GVP soils during SF. The abundance and diversity of ARGs and MGEs in GVP soils with different manure fertilization and SS decreased during SF. Horizontal gene transfer via MGEs (especially integrases 45.80%) induced by changes in environmental factors (NO3--N 14.7% and NH4+-N) was the main factor responsible for the changes in ARGs. Proteobacteria (14.3%) and Firmicutes were the main potential hosts of ARGs. Network analysis suggested that Ornithinimicrobium, Idiomarina and Corynebacterium had positive correlations with aminoglycosides, MLSB, and tetracycline resistance genes. These results provide new insights to understand the fate of ARGs in the GVP soils by manure-amended combined with SS during SF, which may help to reduce the spread of ARGs.

Predicting variable gene content in Escherichia coli using conserved genes

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%-90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943-0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including "hypothetical proteins" was accurately predicted (F1 = 0.902 [0.898-0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876-0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data. IMPORTANCE Having the ability to predict the protein-encoding gene content of a genome is important for assessing genome quality, binning genomes from shotgun metagenomic assemblies, and assessing risk due to the presence of antimicrobial resistance and other virulence genes. In this study, we built a set of binary classifiers for predicting the presence or absence of variable genes occurring in 10%-90% of all publicly available E. coli genomes. Overall, the results show that a large portion of the E. coli variable gene content can be predicted with high accuracy, including genes with functions relating to horizontal gene transfer. This study offers a strategy for predicting gene content using limited input sequence data.

The effect of manure-borne doxycycline combined with different types of oversized microplastic contamination layers on carbon and nitrogen metabolism in sandy loam

The different forms and properties of microplastics (MPs) have different effects on the elemental cycles in soil ecosystems, and this is further complicated when the soil contains antibiotics; meanwhile, oversized microplastic (OMP) in soil is always ignored in studies of environmental behavior. In the context of antibiotic action, the effects of OMP on soil carbon (C) and nitrogen (N) cycling have rarely been explored. In this study, we created four types of oversized microplastic (thick fibers, thin fibers, large debris, and small debris) composite doxycycline (DOX) contamination layers (5-10 cm) in sandy loam, hoping to reveal the effects on soil C and N cycling and potential microbial mechanisms when exposed to the combination of manure-borne DOX and different types of OMP from the perspective of metagenomics in the longitudinal soil layer (0-30 cm). The results showed that all different forms of OMP, when combined with DOX, reduced the soil C content in each layer, but only reduced the soil N content in the upper layer of the OMP contamination layer. The microbial structure of the surface soil (0-10 cm) was more noteworthy than that of the deeper soil (10-30 cm). The genera Chryseolinea and Ohtaekwangia were key microbes involved in C and N cycling in the surface layer and regulated carbon fixation in photosynthetic organisms (K00134), carbon fixation pathways in prokaryotes (K00031), methane metabolism (K11212 and K14941), assimilatory nitrate reduction (K00367), and denitrification (K00376 and K04561). The present study is the first to reveal the potential microbial mechanism of C and N cycling under OMP combined with DOX in different layers, mainly the OMP contamination layer and its upper layer, and the OMP shape plays an important role in this process.

The structure of biodegradable surfactants shaped the microbial community, antimicrobial resistance, and potential for horizontal gene transfer

While most household surfactants are biodegradable in aerobic conditions, their biodegradability may obscure their environmental risks. The presence of surfactants in a biological treatment process can lead to the proliferation of antimicrobial-resistance genes (ARG) in the biomass. Surfactants can be cationic, anionic, or zwitterionic, and these different classes may have different effects on the proliferation ARG. Cationic hexadecyltrimethyl-ammonium (CTAB), anionic sodium dodecyl sulfate (SDS), and zwitterionic 3-(decyldimethylammonio)-propanesulfonate inner salt (DAPS) were used to represent the three classes of surfactants in domestic household clean-up products. This study focused on the removal of these surfactants by the O₂-based Membrane Biofilm Reactor (O_2-MBfR) for hotspot scenarios (∼1 mM) and how the three classes of surfactants affected the microbial community's structure and ARG. Given sufficient O₂ delivery, the MBfR provided at least 98% surfactant removal. The presence and biodegradation for each surfactant uniquely shaped the biofilms' microbial communities and the presence of ARG. CTAB had by far the strongest impact and the higher ARG abundance. In particular, Pseudomonas and Stenotrophomonas, the two main genera in the biofilm treating CTAB, were highly correlated to the abundance of ARG for efflux pumps and antibiotic inactivation. CTAB also led to more functional genes relevant to the Type-IV secretion system and protection against oxidative stress, which also could encourage horizontal gene transfer. Our findings highlight that the biodegradation of quaternary ammonium surfactants, while beneficial, can pose public health concerns from its ability to promote the proliferation of ARG.

Metagenomic insights into the distribution, mobility, and hosts of extracellular antibiotic resistance genes in activated sludge under starvation stress

Extracellular antibiotic resistance genes (eARGs) are important emerging environmental pollutants in wastewater treatment plants (WWTPs). Nutritional substrate deficiency (i.e., starvation) frequently occurs in WWTPs owing to annual maintenance, water quality fluctuation, and sludge storage; and it can greatly alter the antibiotic resistance and extracellular DNA content of bacteria. However, the fate and corresponding transmission risk of eARGs in activated sludge under starvation stress remain largely unknown. Herein, we used metagenomic sequencing to explore the effects of starvation scenarios (carbon, nitrogen, and/or phosphorus deficiency) and environmental conditions (alternating anaerobic-aerobic, anaerobic, anoxic, and aerobic) on the distribution, mobility, and hosts of eARGs in activated sludge. The results showed that 30 days of starvation reduced the absolute abundances of eARGs by 40.9%-88.2%, but high-risk dual and multidrug resistance genes persisted. Starvation, particularly the simultaneous lack of carbon, nitrogen, and phosphorus under aerobic conditions, effectively alleviated eARGs by reducing the abundance of extracellular mobile genetic elements (eMGEs). Starvation also altered the profile of bacterial hosts of eARGs and the bacterial community composition, the latter of which had an indirect positive effect on eARGs via changing eMGEs. Our findings shed light on the response patterns and mechanisms of eARGs in activated sludge under starvation conditions and highlight starvation as a potential strategy to mitigate the risk of previously neglected eARGs in WWTPs.

Metagenomic next generation sequencing for studying antibiotic resistance genes in the environment

Bacterial antimicrobial resistance (AMR) is a persisting and growing threat to human health. Characterization of antibiotic resistance genes (ARGs) in the environment is important to understand and control ARG-associated microbial risks. Numerous challenges exist in monitoring ARGs in the environment, due to the extraordinary diversity of ARGs, low abundance of ARGs with respect to the complex environmental microbiomes, difficulties in linking ARGs with bacterial hosts by molecular methods, difficulties in achieving quantification and high throughput simultaneously, difficulties in assessing mobility potential of ARGs, and difficulties in determining the specific AMR determinant genes. Advances in the next generation sequencing (NGS) technologies and related computational and bioinformatic tools are facilitating rapid identification and characterization ARGs in genomes and metagenomes from environmental samples. This chapter discusses NGS-based strategies, including amplicon-based sequencing, whole genome sequencing, bacterial population-targeted metagenome sequencing, metagenomic NGS, quantitative metagenomic sequencing, and functional/phenotypic metagenomic sequencing. Current bioinformatic tools for analyzing sequencing data for studying environmental ARGs are also discussed.

Elucidating nitrogen removal performance and response mechanisms of anammox under heavy metal stress using big data analysis and machine learning

In this study, machine learning algorithms and big data analysis were used to decipher the nitrogen removal rate (NRR) and response mechanisms of anammox process under heavy metal stresses. Spearman algorithm and Statistical analysis revealed that Cr⁶⁺ had the strongest inhibitory effect on NRR compared to other heavy metals. The established machine learning model (extreme gradient boost) accurately predicted NRR with an accuracy greater than 99%, and the prediction error for new data points was mostly less than 20%. Additionally, the findings of feature analysis demonstrated that Cu²⁺ and Fe³⁺ had the strongest effect on the anammox process, respectively. According to the new insights from this study, Cr⁶⁺ and Cu²⁺ should be removed preferentially in anammox processes under heavy metal stress. This study revealed the feasible application of machine learning and big data analysis for NRR prediction of anammox process under heavy metal stress.

A Review of Antibiotics, Antibiotic Resistant Bacteria, and Resistance Genes in Aquaculture: Occurrence, Contamination, and Transmission

Antibiotics are commonly used to prevent and control diseases in aquaculture. However, long-term/overuse of antibiotics not only leaves residues but results in the development of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs). Antibiotics, ARB, and ARGs are widespread in aquaculture ecosystems. However, their impacts and interaction mechanisms in biotic and abiotic media remain to be clarified. In this paper, we summarized the detection methods, present status, and transfer mechanisms of antibiotics, ARB, and ARGs in water, sediment, and aquaculture organisms. Currently, the dominant methods of detecting antibiotics, ARB, and ARGs are UPLC-MS/MS, 16S rRNA sequencing, and metagenomics, respectively. Tetracyclines, macrolides, fluoroquinolones, and sulfonamides are most frequently detected in aquaculture. Generally, antibiotic concentrations and ARG abundance in sediment are much higher than those in water. Yet, no obvious patterns in the category of antibiotics or ARB are present in organisms or the environment. The key mechanisms of resistance to antibiotics in bacteria include reducing the cell membrane permeability, enhancing antibiotic efflux, and structural changes in antibiotic target proteins. Moreover, horizontal transfer is a major pathway for ARGs transfer, including conjugation, transformation, transduction, and vesiculation. Identifying, quantifying, and summarizing the interactions and transmission mechanisms of antibiotics, ARGs, and ARB would provide useful information for future disease diagnosis and scientific management in aquaculture.

Addressing antibiotic resistance: computational answers to a biological problem?

The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance.

Sustainable on-farm strategy for the disposal of antibiotic fermentation residue: Co-benefits for resource recovery and resistance mitigation

Antibiotic fermentation residue is a key issue for the sustainable operation of pharmaceutical companies, and its improper disposal may cause antibiotic resistance transfer in the environment. However, little is known about the resource recycling strategy of this pharmaceutical waste. Herein, we used hydrothermal spray-dried (HT+SD) and multi-plate dryer (MD) methods to produce bio-organic fertilizers and applied them to an internal recycling model of a field trial. The concentrations of antibiotics (penicillin, cephalosporin, and erythromycin) in the bio-fertilizer, wastewater, and exhaust gas were in the range of 0.002-0.68 mg/kg, ≤ 0.35 ng/mL, and 0.03-0.89 ng/mL, respectively. The organic matter and total nitrogen, phosphorus, and potassium contents were approximately 80% and 10%, respectively. The soil bacterial community was similar among the fertilizer treatments in the same crop cultivation. A total of 233 antibiotic resistance genes (ARGs) and 43 mobile genetic elements (MGEs) were detected, including seven Rank I ARGs and five Rank II ARGs. Random forest analysis showed that gene acc(3)-Via and plasmid trb-C were biomarkers, for which the resistance and the transfer mechanisms were antibiotic inactivation and conjugation, respectively. The results imply that AFR recycling disposal mode is a promising prospect for pharmaceutical waste management.

Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density

Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (mean-squared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R² =0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection.

New insight into the microbiome, resistome, and mobilome on the dental waste water in the context of heavy metal environment

Object

Hospital sewage have been associated with incorporation of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) into microbes, which is considered as a key indicator for the spread of antimicrobial resistance (AMR). The compositions of dental waste water (DWW) contain heavy metals, the evolution of AMR and its effects on the water environment in the context of heavy metal environment have not been seriously investigated. Thus, our major aims were to elucidate the evolution of AMR in DWW.

Methods

DWW samples were collected from a major dental department. The presence of microbial communities, ARGs, and MGEs in untreated and treated (by filter membrane and ozone) samples were analyzed using metagenomics and bioinformatic methods.

Results

DWW-associated resistomes included 1,208 types of ARGs, belonging to 29 antibiotic types/subtypes. The most abundant types/subtypes were ARGs of multidrug resistance and of antibiotics that were frequently used in the clinical practice. Pseudomonas putida, Pseudomonas aeruginosa, Chryseobacterium indologenes, Sphingomonas laterariae were the main bacteria which hosted these ARGs. Mobilomes in DWW consisted of 93 MGE subtypes which belonged to 8 MGE types. Transposases were the most frequently detected MGEs which formed networks of communications. For example, ISCrsp1 and tnpA.5/4/11 were the main transposases located in the central hubs of a network. These significant associations between ARGs and MGEs revealed the strong potential of ARGs transmission towards development of antimicrobial-resistant (AMR) bacteria. On the other hand, treatment of DWW using membranes and ozone was only effective in removing minor species of bacteria and types of ARGs and MGEs.

Conclusion

DWW contained abundant ARGs, and MGEs, which contributed to the occurrence and spread of AMR bacteria. Consequently, DWW would seriously increase environmental health concerns which may be different but have been well-documented from hospital waste waters.

Antibiotic resistance genes in landfill leachates from seven municipal solid waste landfills: Seasonal variations, hosts, and risk assessment

Landfills are reservoir of antibiotics and antibiotic resistance. Antibiotic resistance would transport to the environment through landfill leachate, posing threaten to the environment. However, long term monitoring on antibiotic resistance genes in landfill leachate transportation is limited. Furthermore, antibiotic resistance gene hosts and their risk assessment are lacking. In this study, we investigated the seasonal variation of ARGs sulI, tetO and tetW in seven Chinese municipal solid waste landfill leachates over two years (2017-2018) by quantitative polymerase chain reaction. We also evaluated the associated bacterial hosts and their risk levels based on metagenomics and omics-based framework for assessing the health risk of antimicrobial resistance genes, respectively. Because sulI, tetO and tetW are abundant and the most frequently detected ARGs in global landfill system, they are selected as target ARGs. Results showed that the relative content of target ARGs in 2017 was 100 times higher than that in 2018, suggesting ARGs attenuation. The hosts of sulI were phyla of Lentisphaerae and Proteobacteria, whereas the hosts of tetO and tetW were Bacteroidetes and Firmicutes. Remarkably, the host species include pathogenic bacterium (Salmonella enterica, Labilibaculum filiforme, Bacteroidales bacterium, Anaeromassilibacillus senegalensis, and Pseudochrobactrum sp. B5). ARGs tetO and tetW belong to the Rank II level with characters of enrichment in the human-associated environment and gene mobility, and sulI ranked as Rank VI. In addition, among 1210 known ARGs in the landfill leachate, 78 ARGs belonged to risk Rank I (enrichment in human-associated environment, gene mobility and pathogenicity), demonstrating high health risk of landfill system. These results demonstrate that antibiotic resistance in landfill and landfill leachate have high health risk and the kind of ARGs with high abundance in human-associated environment, gene mobility and pathogenicity should be paid more attention.

Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion

The land application of digestate from anaerobic digestion (AD) is considered a significant route for transmitting antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) to ecosystems. To date, efforts towards understanding complex non-linear interactions between AD operating parameters with ARG/MGE abundances rely on experimental investigations due to a lack of mechanistic models. Herein, three different machine learning (ML) algorithms, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), were compared for their predictive capacities in simulating ARG/MGE abundance changes during AD. The models were trained and cross-validated using experimental data collected from 33 published literature. The comparison of model performance using coefficients of determination (R²) and root mean squared errors (RMSE) indicated that ANN was more reliable than RF and XGBoost. The mode of operation (batch/semi-continuous), co-digestion of food waste and sewage sludge, and residence time were identified as the three most critical features in predicting ARG/MGE abundance changes. Moreover, the trained ANN model could simulate non-linear interactions between operational parameters and ARG/MGE abundance changes that could be interpreted intuitively based on existing knowledge. Overall, this study demonstrates that machine learning can enable a reliable predictive model that can provide a holistic optimization tool for mitigating the ARG/MGE transmission potential of AD.

Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model

As a novel analytical method based on big data, machine learning model can explore the relationship between different parameters and draw universal conclusions, which was used to predict composting maturity and identify key parameters in this study. The results showed that the Stacking model exhibited excellent prediction accuracy. SHapley Additive exPlanations (SHAP) and Partial Dependence Analysis (PDA) were performed to evaluate the importance of different parameters as well as their optimal interval. Optimal starting conditions should be maintained in the mesophilic state (temperature: 30-45℃, moisture content: 55-65%, pH: 6.3-8.0), and nutrients (total nitrogen > 2.3%, total organic carbon > 35%) should be adjusted in the thermophilic state. Experiments revealed that model-based optimization strategies could improve composting maturity because they could optimize compost microbial flora and perform complex carbon cycle functions. In conclusion, this study provides new insights into the enhancement of the composting process.

A comprehensive analysis of evolution and underlying connections of water research themes in the 21st century

This work aimed to reflect the advancements in water-related science, technology, and policy and shed light on future research opportunities related to water through a systematic overview of Water Research articles published in the first 21.5 years of the 21st century. Specific bibliometric analyses were performed to i) reveal the temporal and spatial trends of water-related research themes and ii) identify the underlying connections between research topics. The results showed that while top topics including wastewater (treatment), drinking water, adsorption, model, biofilm, and bioremediation remained constantly researched, there were clear shifts in topics over the years, leading to the identification of trending-up and emerging research topics. Compared to the first decade of the 21st century, the second decade not only experienced significant uptrends of disinfection by-products, anaerobic digestion, membrane bioreactor, advanced oxidation processes, and pharmaceuticals but also witnessed the emerging popularity of PFAS, anammox, micropollutants, emerging contaminants, desalination, waste activated sludge, microbial community, forward osmosis, antibiotic resistance genes, resource recovery, and transformation products. On top of the temporal evolution, distinct spatial evolution existed in water-related research topics. Microplastics and Covid-19 causing global concerns were hot topics detected, while metagenomics and machine learning were two technical approaches emerging in recent years. These consistently popular, trending-up and emerging research topics would most likely attract continuous/increasing research input and therefore constitute a major part of the prospective water-related research publications.

Predicting 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 (R² > 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.

Risk of horizontal transfer of intracellular, extracellular, and bacteriophage antibiotic resistance genes during anaerobic digestion of cow manure

The fate of intracellular antibiotic resistance genes (iARGs), extracellular ARGs (eARGs) and bacteriophage ARGs (bARGs) during anaerobic digestion (AD) of cow manure is unclear. Thus, the characteristics of iARGs, eARGs and bARGs during mesophilic AD (MAD) and thermophilic AD (TAD) of cow manure were investigated. The absolute abundances of iARGs decreased by 69.82% after TAD. After MAD and TAD, the total absolute abundances of eARGs increased by 63.5 times and 67.6 times, respectively, whereas those of the bARGs increased by 47.60% and 59.22%. eARGs were mainly derived from the non-specific lysis of Firmicutes, Bacteroidetes, while bacteriophages had a wide range of hosts. The variations in iARGs, eARGs and bARGs were affected by the microbial hosts but also directly driven by physicochemical factors (e.g., pH). Overall, the findings of this study revealed that there may be a risk of eARGs and bARGs disseminating during the AD of cow manure.

Propagation of antibiotic resistance genes during anaerobic digestion of thermally hydrolyzed sludge and their correlation with extracellular polymeric substances

The positive impact of the thermal hydrolysis process (THP) of sewage sludge on antibiotic resistance genes (ARGs) removal during anaerobic digestion (AD) has been reported in the literature. However, little information is available on how changes in different extracellular polymeric substances (EPS) due to THP can influence ARG propagation during AD. This study focused on systematically correlating EPS components and ARG abundance in AD of sewage sludge pretreated with THP (80 °C, 110 °C, 140 °C, 170 °C). THP under different conditions improved sludge solubilization followed by improved methane yields in the biochemical methane potential (BMP) test. The highest methane yield of 275 ± 11.5 ml CH₄/g COD was observed for THP-140 °C, which was 40.5 ± 2.5% higher than the control. Increasing THP operating temperatures showed a non-linear response of ARG propagation in AD due to the rebound effect. The highest ARGs removal in AD was achieved with THP at 140 °C. The multivariate analysis showed that EPS polysaccharides positively correlated with most ARGs and integrons, except for macrolides resistance genes. In contrast, EPS protein was only strongly correlated with β-lactam resistance genes. These results suggest that manipulating THP operating conditions targeting specific EPS components will be critical to effectively mitigating the dissemination of particular ARG types in AD.

Characterization of antibiotic resistance across Earth's microbial genomes

Widespread antibiotic resistance across Earth's habitats has become a critical health concern. However, large-scale investigation on the distribution of antibiotic resistance genes (ARGs) in the microbiomes from most types of ecosystem is still lacking. In this study, we provide a comprehensive characterization of ARGs for 52,515 microbial genomes covering various Earth's ecosystems, and conduct the risk assessment for ARG-carrying species based on further identification of mobile genetic elements (MGEs) and virulence factor genes (VFGs). We identify a total of 6159 ARG-carrying metagenome-assembled genomes (ACMs), and most of them are recovered from human gut and city subway. Our results show that efflux pump is the most common mechanism for bacteria to acquire multidrug resistance genes in Earth's microbiomes. Enterobacteriaceae species are the largest hosts of ARGs, accounting for 14% of total ACMs with 64% of the total ARG hits. Most of ARG-carrying species are unique in the different ecosystem categories, while 33 potential background ARGs are commonly shared by all ecosystem categories. We then detect 36 high-risk ARGs that likely threat public health in all ACMs. Based on ranking the importance of ARG-carrying species in the different ecosystem categories, several bacterial taxa such as Escherichia coli, Enterococcus faecalis, and Pseudomonas_A stutzeri are recognized as priority species for surveillance and control. Overall, our study gives a broad view of ARG-host associations in the environments.

Nitrogen Regulates the Distribution of Antibiotic Resistance Genes in the Soil-Vegetable System

The increasing antibiotic resistance genes (ARGs) in fertilizer-amended soils can potentially enter food chains through their transfer in a soil-vegetable system, thus, posing threats to human health. As nitrogen is an essential nutrient in agricultural production, the effect of nitrogen (in the forms NH₄ ⁺-N and NO₃ ^--N) on the distribution of ARGs (blaTEM-1, sul1, cmlA, str, and tetO) and a mobile genetic element (MGE; tnpA-4) in a soil-Chinese cabbage system was investigated. Not all the tested genes could transfer from soil to vegetable. For transferable ones (blaTEM-1, sul1, and tnpA-4), nitrogen application influenced their abundances in soil and vegetable but did not impact their distribution patterns (i.e., preference to either leaf or root tissues). For ARGs in soil, effects of nitrogen on their abundances varied over time, and the positive effect of NH₄ ⁺-N was more significant than that of NO₃ ^--N. The ARG accumulation to vegetables was affected by nitrogen application, and the nitrogen form was no longer a key influencing factor. In most cases, ARGs were found to prefer being enriched in roots, and nitrogen application may slightly affect their migration from root to leaf. The calculated estimated human intake values indicated that both children and adults could intake 10⁶-10⁷ copies of ARGs per day from Chinese cabbage consumption, and nitrogen application affected ARG intake to varying degrees. These results provided a new understanding of ARG distribution in vegetables under the agronomic measures such as nitrogen application, which may offer knowledge for healthy vegetable cultivation in future.

Machine learning in natural and engineered water systems

Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.