ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.

BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract.

Low-frequency transmission and persistence of antimicrobial-resistant bacteria and genes from livestock to agricultural soil and crops through compost application.

Livestock excrement is composted and applied to agricultural soils. If composts contain antimicrobial-resistant bacteria (ARB), they may spread to the soil and contaminate cultivated crops. Therefore, we investigated the degree of transmission of ARB and related antimicrobial resistance genes (ARGs) and, as well as clonal transmission of ARB from livestock to soil and crops through composting. This study was conducted at Rakuno Gakuen University farm in Hokkaido, Japan. Samples of cattle feces, solid and liquid composts, agricultural soil, and crops were collected. The abundance of Escherichia coli, coliforms, ß-lactam-resistant E. coli, and ß-lactam-resistant coliforms, as well as the copy numbers of ARG (specifically the bla gene related to ß-lactam-resistant bacteria), were assessed using qPCR through colony counts on CHROMagar ECC with or without ampicillin, respectively, 160 days after compost application. After the application of the compost to the soil, there was an initial increase in E. coli and coliform numbers, followed by a subsequent decrease over time. This trend was also observed in the copy numbers of the bla gene. In the soil, 5.0 CFU g-1 E. coli was detected on day 0 (the day post-compost application), and then, E. coli was not quantified on 60 days post-application. Through phylogenetic analysis involving single nucleotide polymorphisms (SNPs) and using whole-genome sequencing, it was discovered that clonal blaCTX-M-positive E. coli and blaTEM-positive Escherichia fergusonii were present in cattle feces, liquid compost, and soil on day 0 as well as 7 days post-application. This showed that livestock-derived ARB were transmitted from compost to soil and persisted for at least 7 days in soil. These findings indicate a potential low-level transmission of livestock-associated bacteria to agricultural soil through composts was observed at low frequency, dissemination was detected. Therefore, decreasing ARB abundance during composting is important for public health.

Co-localization of antibiotic resistance genes is widespread in the infant gut microbiome and associates with an immature gut microbial composition.

BACKGROUND: In environmental bacteria, the selective advantage of antibiotic resistance genes (ARGs) can be increased through co-localization with genes such as other ARGs, biocide resistance genes, metal resistance genes, and virulence genes (VGs). The gut microbiome of infants has been shown to contain numerous ARGs, however, co-localization related to ARGs is unknown during early life despite frequent exposures to biocides and metals from an early age. RESULTS: We conducted a comprehensive analysis of genetic co-localization of resistance genes in a cohort of 662 Danish children and examined the association between such co-localization and environmental factors as well as gut microbial maturation. Our study showed that co-localization of ARGs with other resistance and virulence genes is common in the early gut microbiome and is associated with gut bacteria that are indicative of low maturity. Statistical models showed that co-localization occurred mainly in the phylum Proteobacteria independent of high ARG content and contig length. We evaluated the stochasticity of co-localization occurrence using enrichment scores. The most common forms of co-localization involved tetracycline and fluoroquinolone resistance genes, and, on plasmids, co-localization predominantly occurred in the form of class 1 integrons. Antibiotic use caused a short-term increase in mobile ARGs, while non-mobile ARGs showed no significant change. Finally, we found that a high abundance of VGs was associated with low gut microbial maturity and that VGs showed even higher potential for mobility than ARGs. CONCLUSIONS: We found that the phenomenon of co-localization between ARGs and other resistance and VGs was prevalent in the gut at the beginning of life. It reveals the diversity that sustains antibiotic resistance and therefore indirectly emphasizes the need to apply caution in the use of antimicrobial agents in clinical practice, animal husbandry, and daily life to mitigate the escalation of resistance. Video Abstract.

Transmission of antibiotic resistance through organic amendments in arable land: A 3-year field study with pigeonpea-wheat cropping system.

The worldwide emergence of antimicrobial resistance (AMR) poses a substantial risk to human health and environmental stability. In agriculture, organic amendments (derived from organic sources such as manure, and plant residues) are beneficial in restoring soil properties and providing essential nutrients to crops but raise concerns about harboring antibiotic resistance, which emphasizes the need for vigilant monitoring and strategic interventions in their application. The current study assessed the impact of farming practices (organic and conventional) in a three-year field experiment with pigeonpea-wheat cropping system, focusing on the transmission of AMR using culture-dependent and -independent approaches, and soil nutrient content. Markers for antibiotic resistance genes (ARGs) (aminoglycoside-aacA, ß-lactam-blaTEM, chloramphenicol-cmlA1, macrolide-ermB, sulfonamides-sul1, sul2, and tetracycline-tetO) and integrons (intl1 and intl2) were targeted using qPCR. Manure amendments, particularly FYM1, exhibited a higher abundance of copies of ARGs compared to the rhizospheric soil. Organic farming was associated with higher copies of intl2, sul1, blaTEM, and tetO genes, while conventional farming showed increased copies of sul2 and ermB genes in the rhizosphere. Significant positive correlations were observed among soil nutrient contents, ARGs, and MGEs. The notable prevalence of ARGs linked to manure amendments serves as a cautionary note, demanding responsible management practices.

Detection of Vancomycin Resistant Genes in Intrinsically Antibiotic Resistant Bacteria from the Gut Microbiota of Indonesian Individuals.

Background: Antibiotic resistance is a global public health concern that has been exacerbated by the overuse and misuse of antibiotics, leading to the emergence of resistant bacteria. The gut microbiota, often influenced by antibiotic usage, plays a crucial role in overall health. Therefore, this study aimed to investigate the prevalence of antibiotic resistant genes in the gut microbiota of Indonesian coastal and highland populations, as well as to identify vancomycin-resistant bacteria and their resistant genes. Methods: Stool samples were collected from 22 individuals residing in Pacet, Mojokerto, and Kenjeran, Surabaya Indonesia in 2022. The read count of antibiotic resistant genes was analyzed in the collected samples, and the bacterium concentration was counted by plating on the antibiotic-containing agar plate. Vancomycin-resistant strains were further isolated, and the presence of vancomycin-resistant genes was detected using a multiplex polymerase chain reaction (PCR). Results: The antibiotic resistant genes for tetracycline, aminoglycosides, macrolides, beta-lactams, and vancomycin were found in high frequency in all stool samples (100%) of the gut microbiota. Meanwhile, those meant for chloramphenicol and sulfonamides were found in 86% and 16% of the samples, respectively. Notably, vancomycin-resistant genes were found in 16 intrinsically resistant Gram-negative bacterial strains. Among the detected vancomycin-resistant genes, vanG was the most prevalent (27.3%), while vanA was the least prevalent (4.5%). Conclusion: The presence of multiple vancomycin resistance genes in intrinsically resistant Gram-negative bacterial strains demonstrated the importance of the gut microbiota as a reservoir and hub for the horizontal transfer of antibiotic resistant genes.

Design and Validation of Primer Sets for the Detection and Quantification of Antibiotic Resistance Genes in Environmental Samples by Quantitative PCR.

The high prevalence of antibiotic resistant bacteria (ARB) in several environments is a great concern threatening human health. Particularly, wastewater treatment plants (WWTP) become important contributors to the dissemination of ARB to receiving water bodies, due to the inefficient management or treatment of highly antibiotic-concentrated wastewaters. Hence, it is vital to develop molecular tools that allow proper monitoring of the genes encoding resistances to these important therapeutic compounds (antibiotic resistant genes, ARGs). For an accurate quantification of ARGs, there is a need for sensitive and robust qPCR assays supported by a good design of primers and validated protocols. In this study, eleven relevant ARGs were selected as targets, including aadA and aadB (conferring resistance to aminoglycosides); ampC, blaTEM, blaSHV, and mecA (resistance to beta-lactams); dfrA1 (resistance to trimethoprim); ermB (resistance to macrolides); fosA (resistance to fosfomycin); qnrS (resistance to quinolones); and tetA(A) (resistance to tetracyclines). The in silico design of the new primer sets was performed based on the alignment of all the sequences of the target ARGs (orthology grade > 70%) deposited in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, allowing higher coverages of the ARGs' biodiversity than those of several primers described to date. The adequate design and performance of the new molecular tools were validated in six samples, retrieved from both natural and engineered environments related to wastewater treatment. The hallmarks of the optimized qPCR assays were high amplification efficiency (> 90%), good linearity of the standard curve (R2 > 0.980), repeatability and reproducibility across experiments, and a wide linear dynamic range. The new primer sets and methodology described here are valuable tools to upgrade the monitorization of the abundance and emergence of the targeted ARGs by qPCR in WWTPs and related environments.

Streptococcus hohhotensis sp. nov., isolated from the breast milk of a healthy woman.

Human breast milk contains lactic acid bacteria (LAB), which have an important influence on the composition of the intestinal microbia of infants. In this study, one strain of an α-hemolytic species of the genus Streptococcus, IMAU99199T, isolated from the breast milk of a healthy nursing mother in Hohhot city PR China, was studied to characterise its taxonomic status using phenotypic and molecular taxonomic methods. The results indicated that it represented a member of the mitis-suis clade, pneumoniae subclade of the genus Streptococcus. It is a Gram-stain-positive, catalase-negative and oxidase-negative bacterium, and the cells are globular, paired or arranged in short chains. The results of a phylogenetic analysis of its 16S rRNA gene and two housekeeping genes (gyrB and rpoB) placed it in the genus Streptococcus. A phylogenetic tree based on 135 single-copy genes sequences indicated that IMAU99199T formed a closely related branch well separated from 'Streptococcus humanilactis' IMAU99125, 'Streptococcus bouchesdurhonensis' Marseille Q6994, Streptococcus mitis NCTC 12261T, 'Streptococcus vulneris' DM3B3, Streptococcus toyakuensis TP1632T, Streptococcus pseudopneumoniae ATCC BAA-960T and Streptococcus pneumoniae NCTC 7465T. IMAU99199T and 'S. humanilactis' IMAU99125 had the highest average nucleotide identity (93.7 %) and digital DNA-DNA hybridisation (55.3 %) values, which were below the accepted thresholds for novel species. The DNA G+C content of the draft genome of IMAU99199T was 39.8 %. The main cellular fatty acids components of IMAU99199T were C16 : 0 and C16 : 1ω7. It grew at a temperature range of 25-45 °C (the optimum growth temperature was 37 °C) and a pH range of 5.0-8.0 (the optimum growth pH was 7.0). These data indicate that strain IMAU99199T represents a novel species in the genus Streptococcus, for which the name Streptococcus hohhotensis sp. nov. is proposed. The type strain is IMAU99199T (=GDMCC 1.1874T=KCTC 21155T).

Characterization of Neowestiellopsis persica A1387 (Hapalosiphonaceae) based on the cpcA, psbA, rpoC1, nifH and nifD gene sequences.

BACKGROUND: Complex descriptions of new strains of cyanobacteria appear very frequently. The main importance of these descriptions concerns potential new substances that they could synthesise, as well as their different properties as a result of their different ecological niches. The main gene used for these descriptions is 16 S with ITS or whole genome sequencing. Neowestiellopsis persica represents a unique example of the influence of ecology on morphological changes, with almost identical 16 S identity. Although our previously described Neowestiellopsis persica strain A1387 was characterized by 16 S analysis, we used different molecular markers to provide a way to separate strains of this genus that are closely related at the genetic level. MATERIALS AND METHODS: In order to conduct an in-depth study, several molecular markers, namely psbA, rpoC1, nifD, nifH and cpcA were sequenced and studied in Neowestiellopsis persica strain A1387. RESULTS: The results of the phylogenetic analysis, based on cpcA, showed that the studied strain A 1387 falls into a separate clade than N. persica, indicating that this signature sequence could be a useful molecular marker for phylogenetic separation of similar strains isolated in the future. CONCLUSIONS: Analysis of strain A1387 based on gene differences confirmed that it is a Neowestiellopsis strain. The morphological changes observed in the previous study could be due to different ecological and cultivation conditions compared to the type species. At the same time, the sequences obtained have increased our understanding of this species and will help in the future to better identify strains belonging to the genus Neowestiellopsis.

Promoter recruitment drives the emergence of proto-genes in a long-term evolution experiment with Escherichia coli.

The phenomenon of de novo gene birth-the emergence of genes from non-genic sequences-has received considerable attention due to the widespread occurrence of genes that are unique to particular species or genomes. Most instances of de novo gene birth have been recognized through comparative analyses of genome sequences in eukaryotes, despite the abundance of novel, lineage-specific genes in bacteria and the relative ease with which bacteria can be studied in an experimental context. Here, we explore the genetic record of the Escherichia coli long-term evolution experiment (LTEE) for changes indicative of "proto-genic" phases of new gene birth in which non-genic sequences evolve stable transcription and/or translation. Over the time span of the LTEE, non-genic regions are frequently transcribed, translated and differentially expressed, with levels of transcription across low-expressed regions increasing in later generations of the experiment. Proto-genes formed downstream of new mutations result either from insertion element activity or chromosomal translocations that fused preexisting regulatory sequences to regions that were not expressed in the LTEE ancestor. Additionally, we identified instances of proto-gene emergence in which a previously unexpressed sequence was transcribed after formation of an upstream promoter, although such cases were rare compared to those caused by recruitment of preexisting promoters. Tracing the origin of the causative mutations, we discovered that most occurred early in the history of the LTEE, often within the first 20,000 generations, and became fixed soon after emergence. Our findings show that proto-genes emerge frequently within evolving populations, can persist stably, and can serve as potential substrates for new gene formation.

Identification of an Enterococcus faecium strain isolated from raw bovine milk co-harbouring the oxazolidinone resistance genes optrA and poxtA in China.

Oxazolidinones are potent antimicrobial agents used to treat human infections caused by multidrug-resistant Gram-positive bacteria. The growing resistance to oxazolidinones poses a significant threat to public health. In August 2021, a linezolid-resistant Enterococcus faecium BN83 was isolated from a raw milk sample of cow in Inner Mongolia, China. This isolate exhibited a multidrug resistance phenotype and was resistant to most of drugs tested including linezolid and tedizolid. PCR detection showed that two mobile oxazolidinones resistance genes, optrA and poxtA, were present in this isolate. Whole genome sequencing analysis revealed that the genes optrA and poxtA were located on two different plasmids, designated as pBN83-1 and pBN83-2, belonging to RepA_N and Inc18 families respectively. Genetic context analysis suggested that optrA gene on plasmid pBN83-1 was located in transposon Tn6261 initially found in E. faecalis. Comprehensive analysis revealed that Tn6261 act as an important horizontal transmission vector for the spread of optrA in E. faecium. Additionally, poxtA-bearing pBN83-2 displayed high similarity to numerous plasmids from Enterococcus of different origin and pBN83-2-like plasmid represented a key mobile genetic element involved in movement of poxtA in enterococcal species. The presence of optrA- and poxtA-carrying E. faecium in raw bovine milk represents a public health concern and active surveillance is urgently warranted to investigate the prevalence of oxazolidinone resistance genes in animal-derived food products.

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.

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.

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.

Community coalescence and plant host filtering determine the spread of tetracycline resistance genes from pig manure into the microbiome continuum of the soil-plant system.

The spread of livestock manure-borne antibiotic resistance genes (ARGs) into agroecosystems through manure application poses a potential threat to human health. However, there is still a knowledge gap concerning ARG dissemination in coalescing manure, soil and plant microbiomes. Here, we examined the fate of tetracycline resistance genes (TRGs) originating from pig manure microbiomes and spread in the soil-A thaliana system and explored the effects of microbial functions on TRGs spread at different interfaces. Our results indicate that the TRGs abundances in all microbiome continuum of the soil-A. thaliana system were significantly increased with the application of a living manure microbiome, although the addition of manure with both an active and inactive microbiome caused a shift in the microbial community composition. This was attributed to the increasing relative abundances of tetA, tetL, tetM, tetO, tetW and tolC in the system. The application of living manure with DOX residues resulted in the highest relative abundance of total TRGs (3.30×10-3 copies/16S rRNA gene copies) in the rhizosphere soil samples. Community coalescence of the manure and soil microbiomes increased the abundance of Firmicutes in the soil and root microbiome, which directly explains the increase in TRG abundance observed in these interfaces. In contrast, the leaf microbiome differed markedly from that of the remaining samples, indicating strong plant host filtering effects on Firmicutes and TRGs from pig manure. The random forest machine learning model revealed microbial functions and their significant positive correlation with TRG abundance in the microbiome continuum of the system. Our findings revealed that community coalescence is the main driver of TRG spread from manure to the soil and root microbiomes. Plant host filtering effects play a crucial role in allowing certain microbial groups to occupy ecological niches in the leaves, thereby limiting the establishment of manure-borne TRGs in aboveground plant tissues.