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471500.9959Genomic and stress resistance characterization of Lactiplantibacillus plantarum GX17, a potential probiotic for animal feed applications. Lactobacilli, recognized as beneficial bacteria within the human body, are celebrated for their multifaceted probiotic functions, including the regulation of intestinal flora, enhancement of body immunity, and promotion of nutrient absorption. This study comprehensively analyzed the genotypic and phenotypic characteristics of Lactiplantibacillus plantarum (L. plantarum) strains isolated from the intestines of healthy chicks and assessed their potential as probiotics. The assembled genome consists of 29,521,986 bp, and a total of 1,771 coding sequences (CDSs) were predicted. Based on the entire genome sequence analysis, 50 stress resistance genes and seven virulence factors were identified. The results of the phenotypic experiments showed that the strain had good resistance to high temperature, low temperature, acid, alkali, salt, artificial gastrointestinal fluid, and strong antioxidant capacity. Additionally, transcriptomic analysis confirmed that under stress conditions, the expression levels of key genes were significantly upregulated. Therefore, the phenotypic characteristics of L. plantarum GX17 align well with its genotypic features, demonstrating promising probiotic properties. This strain holds great potential as a probiotic candidate, and further investigation into its beneficial effects on human health is warranted. IMPORTANCE: In humans, Lactiplantibacillus plantarum may synergize with host microbiota to ameliorate dysbiosis-related pathologies, enhance immunomodulation, and facilitate micronutrient bioavailability. For livestock, its application could improve feed conversion ratios, suppress enteric pathogens through competitive exclusion, and mitigate antibiotic overuse, "a critical strategy in One Health frameworks." Further investigations into strain-specific mechanisms (e.g., postbiotic metabolites, quorum sensing regulation) are warranted to translate these genomic-phenotypic advantages into sustainable health solutions across species.202540919934
766810.9958Taxonomic and functional profiling of microbial community in municipal solid waste dumpsite. Understanding the microbial ecology of landfills is crucial for improving waste management strategies and utilizing the potential of these microbial communities for biotechnological applications. This study aimed to conduct a comprehensive taxonomic and functional profiling of the microbial community present in the Addis Ababa municipal solid waste dumpsite using a shotgun metagenomics sequencing approach. The taxonomic analysis of the sample revealed the significant presence of bacteria, with the Actinomycetota (56%), Pseudomonadota (23%), Bacillota (3%), and Chloroflexota (3%) phyla being particularly abundant. The most abundant KEGG categories were carbohydrates metabolism, membrane transport, signal transduction, and amino acid metabolism. The biodegradation and metabolism of xenobiotics, as well as terpenoids and polyketides, were also prevalent. Moreover, the Comprehensive Antibiotic Resistance Database (CARD) identified 52 antibiotic resistance gene (ARG) subtypes belonging to 14 different drug classes, with the highest abundances observed for glycopeptide, phosphonic acid, and multidrug resistance genes. Actinomycetota was the dominant phylum harboring ARGs, followed by Pseudomonadota and Chloroflexota. This study offers valuable insights into the taxonomic and functional diversity of the microbial community in the Addis Ababa municipal solid waste dumpsite. It sheds light on the widespread presence of metabolically versatile microbes, antibiotic resistance genes, mobile genetic elements, and pathogenic bacteria. This understanding can contribute to the creation of efficient waste management strategies and the investigation of possible biotechnological uses for these microbial communities.202439551884
855820.9958Mitigating the vertical migration and leaching risks of antibiotic resistance genes through insect fertilizer application. The leaching and vertical migration risks of antibiotic resistance genes (ARGs) from fertilized soil to groundwater poses a significant threat to ecological and public safety. Insect fertilizer, particularly black soldier fly organic fertilizer (BOF), renowned for its minimal antibiotic resistance, emerge as a promising alternative for sustainable agricultural fertilization. This study employs soil-column leaching experiments to evaluate the impact of BOF on the leaching behavior of ARGs. Our results reveal that BOF significantly reduces the leaching risks of ARGs by 22.1 %-49.3 % compared to control organic fertilizer (COF). Moreover, BOF promotes the leaching of beneficial Bacillus and, according to random forest analysis, is the most important factor in predicting ARG profiles (3.02 % increase in the MSE). Further network analysis and mantel tests suggest that enhanced nitrogen metabolism in BOF leachates could foster Bacillus biofilm formation, thereby countering antibiotic-resistant bacteria (ARB) and mitigating antibiotic resistance. In addition, linear regression analysis revealed that Bacillus biofilm-associated genes pgaD (biofilm PGA synthesis protein), slrR (biofilm formation regulator), and kpsC (capsular polysaccharide export protein) were identified as pivotal in the elimination of ARGs, which can serve as effective indicators for assessing antibiotic resistance in groundwater. Collectively, this study demonstrates that BOF as an environmentally friendly fertilizer could markedly reduce the vertical migration risks of ARGs and proposes Bacillus biofilm formation related genes as reliable indicators for monitoring antibiotic resistance in groundwater.202540086570
477330.9958Draft genome analysis for Enterobacter kobei, a promising lead bioremediation bacterium. Lead pollution of the environment poses a major global threat to the ecosystem. Bacterial bioremediation offers a promising alternative to traditional methods for removing these pollutants, that are often hindered by various limitations. Our research focused on isolating lead-resistant bacteria from industrial wastewater generated by heavily lead-containing industries. Eight lead-resistant strains were successfully isolated, and subsequently identified through molecular analysis. Among these, Enterobacter kobei FACU6 emerged as a particularly promising candidate, demonstrating an efficient lead removal rate of 83.4% and a remarkable lead absorption capacity of 571.9 mg/g dry weight. Furthermore, E. kobei FACU6 displayed a remarkable a maximum tolerance concentration (MTC) for lead reaching 3,000 mg/L. To further investigate the morphological changes in E. kobei FACU6 in response to lead exposure, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were employed. These analyses revealed significant lead adsorption and intracellular accumulation in treated bacteria in contrast to the control bacterium. Whole-genome sequencing was performed to gain deeper insights into E. kobei's lead resistance mechanisms. Structural annotation revealed a genome size of 4,856,454 bp, with a G + C content of 55.06%. The genome encodes 4,655 coding sequences (CDS), 75 tRNA genes, and 4 rRNA genes. Notably, genes associated with heavy metal resistance and their corresponding regulatory elements were identified within the genome. Furthermore, the expression levels of four specific heavy metal resistance genes were evaluated. Our findings revealed a statistically significant upregulation in gene expression under specific environmental conditions, including pH 7, temperature of 30°C, and high concentrations of heavy metals. The outstanding potential of E. kobei FACU6 as a source of diverse genes related to heavy metal resistance and plant growth promotion makes it a valuable candidate for developing safe and effective strategies for heavy metal disposal.202338260751
769740.9957Impact of sample multiplexing on detection of bacteria and antimicrobial resistance genes in pig microbiomes using long-read sequencing. The effects of sample multiplexing on the detection sensitivity of antimicrobial resistance genes (ARGs) and pathogenic bacteria in metagenomic sequencing remain underexplored in newer sequencing technologies such as Oxford Nanopore Technologies (ONT), despite its critical importance for surveillance applications. Here, we evaluate how different multiplexing levels (four and eight samples per flowcell) on two ONT platforms, GridION and PromethION, influence the detection of ARGs, bacterial taxa and pathogens. While overall resistome and bacterial community profiles remained comparable across multiplexing levels, ARG detection was more comprehensive in the four-plex setting with low-abundance genes. Similarly, pathogen detection was more sensitive in the four-plex, identifying a broader range of low abundant bacterial taxa compared to the eight-plex. However, triplicate sequencing of the same microbiomes revealed that these differences were primarily due to sequencing variability rather than multiplexing itself, as similar inconsistencies were observed across replicates. Given that eight-plex sequencing is more cost-effective while still capturing the overall resistome and bacterial community composition, it may be the preferred option for general surveillance. Lower multiplexing levels may be advantageous for applications requiring enhanced sensitivity, such as detailed pathogen research. These findings highlight the trade-off between multiplexing efficiency, sequencing depth, and cost in metagenomic studies.202540611965
435750.9957Comparative genomic analysis of 255 Oenococcus oeni isolates from China: unveiling strain diversity and genotype-phenotype associations of acid resistance. Oenococcus oeni, the only species of lactic acid bacteria capable of fully completing malolactic fermentation under challenging wine conditions, continues to intrigue researchers owing to its remarkable adaptability, particularly in combating acid stress. However, the mechanism underlying its superior adaptation to wine stresses still remains elusive due to the lack of viable genetic manipulation tools for this species. In this study, we conducted genomic sequencing and acid resistance phenotype analysis of 255 O. oeni isolates derived from diverse wine regions across China, aiming to elucidate their strain diversity and genotype-phenotype associations of acid resistance through comparative genomics. A significant correlation between phenotypes and evolutionary relationships was observed. Notably, phylogroup B predominantly consisted of acid-resistant isolates, primarily originating from Shandong and Shaanxi wine regions. Furthermore, we uncovered a noteworthy linkage between prophage genomic islands and acid resistance phenotype. Using genome-wide association studies, we identified key genes correlated with acid resistance, primarily involved in carbohydrates and amino acid metabolism processes. This study offers profound insights into the genetic diversity and genetic basis underlying adaptation mechanisms to acid stress in O. oeni.IMPORTANCEThis study provides valuable insights into the genetic basis of acid resistance in Oenococcus oeni, a key lactic acid bacterium in winemaking. By analyzing 255 isolates from diverse wine regions in China, we identified significant correlations between strain diversity, genomic islands, and acid resistance phenotypes. Our findings reveal that certain prophage-related genomic islands and specific genes are closely linked to acid resistance, offering a deeper understanding of how O. oeni adapts to acidic environments. These discoveries not only advance our knowledge of microbial stress responses but also pave the way for selecting and engineering acid-resistant strains, enhancing malolactic fermentation efficiency and wine quality. This research underscores the importance of genomics in improving winemaking practices and addressing challenges posed by high-acidity wines.202540261018
471060.9957Gene Co-Expression Network Analysis Reveals the Hub Genes and Key Pathways Associated with Resistance to Salmonella Enteritidis Colonization in Chicken. Salmonella negatively impacts the poultry industry and threatens animals' and humans' health. The gastrointestinal microbiota and its metabolites can modulate the host's physiology and immune system. Recent research demonstrated the role of commensal bacteria and short-chain fatty acids (SCFAs) in developing resistance to Salmonella infection and colonization. However, the complex interactions among chicken, Salmonella, host-microbiome, and microbial metabolites remain unelucidated. Therefore, this study aimed to explore these complex interactions by identifying the driver and hub genes highly correlated with factors that confer resistance to Salmonella. Differential gene expression (DEGs) and dynamic developmental genes (DDGs) analyses and weighted gene co-expression network analysis (WGCNA) were performed using transcriptome data from the cecum of Salmonella Enteritidis-infected chicken at 7 and 21 days after infection. Furthermore, we identified the driver and hub genes associated with important traits such as the heterophil/lymphocyte (H/L) ratio, body weight post-infection, bacterial load, propionate and valerate cecal contents, and Firmicutes, Bacteroidetes, and Proteobacteria cecal relative abundance. Among the multiple genes detected in this study, EXFABP, S100A9/12, CEMIP, FKBP5, MAVS, FAM168B, HESX1, EMC6, and others were found as potential candidate gene and transcript (co-) factors for resistance to Salmonella infection. In addition, we found that the PPAR and oxidative phosphorylation (OXPHOS) metabolic pathways were also involved in the host's immune response/defense against Salmonella colonization at the earlier and later stage post-infection, respectively. This study provides a valuable resource of transcriptome profiles from chicken cecum at the earlier and later stage post-infection and mechanistic understanding of the complex interactions among chicken, Salmonella, host-microbiome, and associated metabolites.202336902251
770070.9957Rapid identification of antibiotic resistance gene hosts by prescreening ARG-like reads. Effective risk assessment and control of environmental antibiotic resistance depend on comprehensive information about antibiotic resistance genes (ARGs) and their microbial hosts. Advances in sequencing technologies and bioinformatics have enabled the identification of ARG hosts using metagenome-assembled contigs and genomes. However, these approaches often suffer from information loss and require extensive computational resources. Here we introduce a bioinformatic strategy that identifies ARG hosts by prescreening ARG-like reads (ALRs) directly from total metagenomic datasets. This ALR-based method offers several advantages: (1) it enables the detection of low-abundance ARG hosts with higher accuracy in complex environments; (2) it establishes a direct relationship between the abundance of ARGs and their hosts; and (3) it reduces computation time by approximately 44-96% compared to strategies relying on assembled contigs and genomes. We applied our ALR-based strategy alongside two traditional methods to investigate a typical human-impacted environment. The results were consistent across all methods, revealing that ARGs are predominantly carried by Gammaproteobacteria and Bacilli, and their distribution patterns may indicate the impact of wastewater discharge on coastal resistome. Our strategy provides rapid and accurate identification of antibiotic-resistant bacteria, offering valuable insights for the high-throughput surveillance of environmental antibiotic resistance. This study further expands our knowledge of ARG-related risk management in future.202540059905
696480.9957Metagenomic approach reveals the role of bioagents in the environmental dissemination risk of rhizosphere soil antibiotic resistance genes pollution. Antibiotic resistance genes (ARGs) have been identified as emerging contaminants, raising concerns around the world. As environmentally friendly bioagents (BA), plant growth-promoting rhizobacteria (PGPR) have been used in agricultural systems. The introduction of BA will lead to the turnover of the microbial communities structure. Nevertheless, it is still unclear how the colonization of the invaded microorganisms could affects the rhizosphere resistome. Consequently, 190 ARGs and 25 integrative and conjugative elements (ICEs) were annotated using the metagenomic approach in 18 samples from the Solanaceae crop rhizosphere soil under BA and conventional treatment (CK) groups. Our study found that, after 90 days of treatment, ARG abundance was lower in the CK group than in the BA group. The results showed that aminoglycoside antibiotic resistance (OprZ), phenicol antibiotic resistance (OprN), aminoglycoside antibiotic resistance (ceoA/B), aminocoumarin antibiotic resistance (mdtB) and phenicol antibiotic resistance (MexW) syntenic with ICEs. Moreover, in 11 sequences, OprN (phenicol antibiotic resistance) was observed to have synteny with ICEPaeLESB58-1, indicating that the ICEs could contribute to the spread of ARGs. Additionally, the binning result showed that the potential bacterial hosts of the ARGs were beneficial bacteria which could promote the nutrition cycle, such as Haliangium, Nitrospira, Sideroxydans, Burkholderia, etc, suggesting that bacterial hosts have a great influence on ARG profiles. According to the findings, considering the dissemination of ARGs, BA should be applied with caution, especially the use of beneficial bacteria in BA. In a nutshell, this study offers valuable insights into ARGs pollution control from the perspective of the development and application of BA, to make effective strategies for blocking pollution risk migration in the ecological environment.202439374754
509990.9956A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria. Microorganisms, crucial for environmental equilibrium, could be destructive, resulting in detrimental pathophysiology to the human host. Moreover, with the emergence of antibiotic resistance (ABR), the microbial communities pose the century's largest public health challenges in terms of effective treatment strategies. Furthermore, given the large diversity and number of known bacterial strains, describing treatment choices for infected patients using experimental methodologies is time-consuming. An alternative technique, gaining popularity as sequencing prices fall and technology advances, is to use bacterial genotype rather than phenotype to determine ABR. Complementing machine learning into clinical practice provides a data-driven platform for categorization and interpretation of bacterial datasets. In the present study, k-mers were generated from nucleotide sequences of pathogenic bacteria resistant to antibiotics. Subsequently, they were clustered into groups of bacteria sharing similar genomic features using the Affinity propagation algorithm with a Silhouette coefficient of 0.82. Thereafter, a prediction model based on Random Forest algorithm was developed to explore the prediction capability of the k-mers. It yielded an overall specificity of 0.99 and a sensitivity of 0.98. Additionally, the genes and ABR drivers related to the k-mers were identified to explore their biological relevance. Furthermore, a multilayer perceptron model with a hamming loss of 0.05 was built to classify the bacterial strains into resistant and non-resistant strains against various antibiotics. Segregating pathogenic bacteria based on genomic similarities could be a valuable approach for assessing the severity of diseases caused by new bacterial strains. Utilization of this strategy could aid in enhancing our understanding of ABR patterns, paving the way for more informed and effective treatment options.202439816256
5163100.9956Multi-omics data elucidate parasite-host-microbiota interactions and resistance to Haemonchus contortus in sheep. BACKGROUND: The integration of molecular data from hosts, parasites, and microbiota can enhance our understanding of the complex biological interactions underlying the resistance of hosts to parasites. Haemonchus contortus, the predominant sheep gastrointestinal parasite species in the tropics, causes significant production and economic losses, which are further compounded by the diminishing efficiency of chemical control owing to anthelmintic resistance. Knowledge of how the host responds to infection and how the parasite, in combination with microbiota, modulates host immunity can guide selection decisions to breed animals with improved parasite resistance. This understanding will help refine management practices and advance the development of new therapeutics for long-term helminth control. METHODS: Eggs per gram (EPG) of feces were obtained from Morada Nova sheep subjected to two artificial infections with H. contortus and used as a proxy to select animals with high resistance or susceptibility for transcriptome sequencing (RNA-seq) of the abomasum and 50 K single-nucleotide genotyping. Additionally, RNA-seq data for H. contortus were generated, and amplicon sequence variants (ASV) were obtained using polymerase chain reaction amplification and sequencing of bacterial and archaeal 16S ribosomal RNA genes from sheep feces and rumen content. RESULTS: The heritability estimate for EPG was 0.12. GAST, GNLY, IL13, MGRN1, FGF14, and RORC genes and transcripts were differentially expressed between resistant and susceptible animals. A genome-wide association study identified regions on chromosomes 2 and 11 that harbor candidate genes for resistance, immune response, body weight, and adaptation. Trans-expression quantitative trait loci were found between significant variants and differentially expressed transcripts. Functional co-expression modules based on sheep genes and ASVs correlated with resistance to H. contortus, showing enrichment in pathways of response to bacteria, immune and inflammatory responses, and hub features of the Christensenellaceae, Bacteroides, and Methanobrevibacter genera; Prevotellaceae family; and Verrucomicrobiota phylum. In H. contortus, some mitochondrial, collagen-, and cuticle-related genes were expressed only in parasites isolated from susceptible sheep. CONCLUSIONS: The present study identified chromosome regions, genes, transcripts, and pathways involved in the elaborate interactions between the sheep host, its gastrointestinal microbiota, and the H. contortus parasite. These findings will assist in the development of animal selection strategies for parasite resistance and interdisciplinary approaches to control H. contortus infection in sheep.202438429820
8409110.9956Comparative genomics reveals key adaptive mechanisms in pathogen host-niche specialization. INTRODUCTION: Understanding the key factors that enable bacterial pathogens to adapt to new hosts is crucial, as host-microbe interactions not only influence host health but also drive bacterial genome diversification, thereby enhancing pathogen survival in various ecological niches. METHODS: We conducted a comparative genomic analysis of 4,366 high-quality bacterial genomes isolated from various hosts and environments. Bioinformatics databases and machine learning approaches were used to identify genomic differences in functional categories, virulence factors, and antibiotic resistance genes across different ecological niches. RESULTS: Significant variability in bacterial adaptive strategies was observed. Human-associated bacteria, particularly from the phylum Pseudomonadota, exhibited higher detection rates of carbohydrate-active enzyme genes and virulence factors related to immune modulation and adhesion, indicating co-evolution with the human host. In contrast, bacteria from environmental sources, particularly those from the phyla Bacillota and Actinomycetota, showed greater enrichment in genes related to metabolism and transcriptional regulation, highlighting their high adaptability to diverse environments. Bacteria from clinical settings had higher detection rates of antibiotic resistance genes, particularly those related to fluoroquinolone resistance. Animal hosts were identified as important reservoirs of resistance genes. Key host-specific bacterial genes, such as hypB, were found to potentially play crucial roles in regulating metabolism and immune adaptation in human-associated bacteria. DISCUSSION: These findings highlight niche-specific genomic features and adaptive mechanisms of bacterial pathogens. This study provides valuable insights into the genetic basis of host-pathogen interactions and offers evidence to inform pathogen transmission control, infection management, and antibiotic stewardship.202540547794
7707120.9956Exploring the dynamics of gut microbiota, antibiotic resistance, and chemotherapy impact in acute leukemia patients: A comprehensive metagenomic analysis. Leukemia poses significant challenges to its treatment, and understanding its complex pathogenesis is crucial. This study used metagenomic sequencing to investigate the interplay between chemotherapy, gut microbiota, and antibiotic resistance in patients with acute leukemia (AL). Pre- and post-chemotherapy stool samples from patients revealed alterations in microbial richness, taxa, and antibiotic resistance genes (ARGs). The analysis revealed a decreased alpha diversity, increased dispersion in post-chemotherapy samples, and changes in the abundance of specific bacteria. Key bacteria such as Enterococcus, Klebsiella, and Escherichia coli have been identified as prevalent ARG carriers. Correlation analysis between gut microbiota and blood indicators revealed potential links between microbial species and inflammatory biomarkers, including C-reactive protein (CRP) and adenosine deaminase (ADA). This study investigated the impact of antibiotic dosage on microbiota and ARGs, revealing networks connecting co-occurring ARGs with microbial species (179 nodes, 206 edges), and networks associated with ARGs and antibiotic dosages (50 nodes, 50 edges). Antibiotics such as cephamycin and sulfonamide led to multidrug-resistant Klebsiella colonization. Our analyses revealed distinct microbial profiles with Salmonella enterica elevated post-chemotherapy in NF patients and Akkermansia muciniphila elevated pre-chemotherapy. These microbial signatures could inform strategies to modulate the gut microbiome, potentially mitigating the risk of neutropenic fever in patients undergoing chemotherapy. Finally, a comprehensive analysis of KEGG modules shed light on disrupted metabolic pathways after chemotherapy, providing insights into potential targets for managing side effects. Overall, this study revealed intricate relationships between gut microbiota, chemotherapy, and antibiotic resistance, providing new insights into improving therapy and enhancing patient outcomes.202439620486
8463130.9956Safety assessment of five candidate probiotic lactobacilli using comparative genome analysis. Micro-organisms belonging to the Lactobacillus genus complex are often used for oral consumption and are generally considered safe but can exhibit pathogenicity in rare and specific cases. Therefore, screening and understanding genetic factors that may contribute to pathogenicity can yield valuable insights regarding probiotic safety. Limosilactobacillus mucosae LM1, Lactiplantibacillus plantarum SK151, Lactiplantibacillus plantarum BS25, Limosilactobacillus fermentum SK152 and Lactobacillus johnsonii PF01 are current probiotics of interest; however, their safety profiles have not been explored. The genome sequences of LM1, SK151, SK152 and PF01 were downloaded from the NCBI GenBank, while that of L. plantarum BS25 was newly sequenced. These genomes were then annotated using the Rapid Annotation using Subsystem Technology tool kit pipeline. Subsequently, a command line blast was performed against the Virulence Factor Database (VFDB) and the Comprehensive Antibiotic Resistance Database (CARD) to identify potential virulence factors and antibiotic resistance (AR) genes. Furthermore, ResFinder was used to detect acquired AR genes. The query against the VFDB identified genes that have a role in bacterial survivability, platelet aggregation, surface adhesion, biofilm formation and immunoregulation; and no acquired AR genes were detected using CARD and ResFinder. The study shows that the query strains exhibit genes identical to those present in pathogenic bacteria with the genes matched primarily having roles related to survival and surface adherence. Our results contribute to the overall strategies that can be employed in pre-clinical safety assessments of potential probiotics. Gene mining using whole-genome data, coupled with experimental validation, can be implemented in future probiotic safety assessment strategies.202438361650
3220140.9956Metabolically-active bacteria in reclaimed water and ponds revealed using bromodeoxyuridine DNA labeling coupled with 16S rRNA and shotgun sequencing. Understanding the complex microbiota of agricultural irrigation water is vital to multiple sectors of sustainable agriculture and public health. To date, microbiome characterization methods have provided comprehensive profiles of aquatic microbiotas, but have not described which taxa are likely metabolically-active. Here, we combined 5‑bromo‑2'-deoxyuridine (BrdU) labeling with 16S rRNA and shotgun sequencing to identify metabolically-active bacteria in reclaimed and agricultural pond water samples (n = 28) recovered from the Mid-Atlantic United States between March 2017 and January 2018. BrdU-treated samples were significantly less diverse (alpha diversity) compared to non-BrdU-treated samples. The most abundant taxa in the metabolically-active fraction of water samples (BrdU-treated samples) were unclassified Actinobacteria, Flavobacterium spp., Pseudomonas spp. and Aeromonas spp. Interestingly, we also observed that antimicrobial resistance and virulence gene profiles seemed to be more diverse and more abundant in non-BrdU-treated water samples compared to BrdU-treated samples. These findings raise the possibility that these genes may be associated more with relic (inactive) DNA present in the tested water types rather than viable, metabolically-active microorganisms. Our study demonstrates that the coupled use of BrdU labeling and sequencing can enhance understanding of the metabolically-active fraction of bacterial communities in alternative irrigation water sources. Agricultural pond and reclaimed waters are vital to the future of sustainable agriculture, and thus, the full understanding of the pathogenic potential of these waters is important to guide mitigation strategies that ensure appropriate water quality for intended purposes.202032726735
7375150.9956Assessing microbial ecology and antibiotic resistance genes in river sediments. Anthropogenic activities greatly affect the Karon River leading to deterioration of water quality. This investigation utilizes environmental genomic techniques to delineate microbial populations, examine functional genomics, and evaluate the occurrence of virulence determinants and antibiotic resistance genes (ARGs) in fluvial sediment. Taxonomic assessment identified that Firmicutes were the predominant phyla, with Bacillus being the most abundant genus across samples. Functional analysis revealed the metabolic capabilities of sediment-associated bacteria, linking them to biogeochemical processes and potential health impacts. The S2 samples exhibited the highest virulence factor genes, while the S3 samples had the most ARGs (30), highlighting concerns about pathogenicity. Analyzing ARGs provides critical insights into environmental data collected, such as water quality parameters (e.g., nutrient concentrations, pH) or pollution levels, prevalence, and distribution of these resistance factors within the sediment samples, helping to identify potential hotspots of antibiotic resistance in the Karon River ecosystem. The study identified similar operational taxonomic units (OTUs) across sampling sites at the phylogenetic level, indicating a consistent presence of certain microbial taxa. However, the lack of variation in functional classification suggests that while these taxa may be present, they are not exhibiting significant differences in metabolic capabilities or functional roles. These findings emphasize the significance of metagenomic methods in understanding microbial ecology and antibiotic resistance in aquatic environments, suggesting a need for further research into the restoration of microbial functions related to ARGs and virulence factors.202540127879
8650160.9956Global Geographic Patterns of Soil Microbial Degradation Potential for Polycyclic Aromatic Hydrocarbons. Polycyclic aromatic hydrocarbons (PAHs) are toxic and persistent pollutants that are widely distributed in the environment. PAHs are toxic to microorganisms and pose ecological risks. Bacteria encode enzymes for PAH degradation through specific genes, thereby mitigating PAH pollution. However, due to PAHs' complexity, information on the global degradation potential, diversity, and associated risks of PAH-degrading microbes in soils is lacking. In this study, we analyzed 121 PAH-degrading genes and selected 33 as marker genes to predict the degradation potential within the soil microbiome. By constructing a Hidden Markov Model, we identified 4990 species carrying PAH-degrading genes in 40,039 soil metagenomic assembly genomes, with Burkholderiaceae and Stellaceae emerging as high-potential degraders. We demonstrated that the candidate PAH degraders predominantly emerged in artificial soil and farmland, with significantly fewer present in extreme environments, driven by factors such as average annual rainfall, organic carbon, and human modification of terrestrial systems. Furthermore, we comprehensively quantified the potential risks of each potential host in future practical applications using three indicators (antibiotic resistance genes, virulence factors, and pathogenic bacteria). We found that the degrader Stellaceae has significant application prospects. Our research will help determine the biosynthetic potential of PAH-degrading enzymes globally and further identify potential PAH-degrading bacteria at lower risk.202540223703
5101170.9956Identification of Key Features Pivotal to the Characteristics and Functions of Gut Bacteria Taxa through Machine Learning Methods. BACKGROUND: Gut bacteria critically influence digestion, facilitate the breakdown of complex food substances, aid in essential nutrient synthesis, and contribute to immune system balance. However, current knowledge regarding intestinal bacteria remains insufficient. OBJECTIVE: This study aims to discover essential differences for different intestinal bacteria. METHODS: This study was conducted by investigating a total of 1478 gut bacterial samples comprising 235 Actinobacteria, 447 Bacteroidetes, and 796 Firmicutes, by utilizing sophisticated machine learning algorithms. By building on the dataset provided by Chen et al., we engaged sophisticated machine learning techniques to further investigate and analyze the gut bacterial samples. Each sample in the dataset was described by 993 unique features associated with gut bacteria, including 342 features annotated by the Antibiotic Resistance Genes Database, Comprehensive Antibiotic Research Database, Kyoto Encyclopedia of Genes and Genomes, and Virulence Factors of Pathogenic Bacteria. We employed incremental feature selection methods within a computational framework to identify the optimal features for classification. RESULTS: Eleven feature ranking algorithms selected several key features as pivotal to the characteristics and functions of gut bacteria. These features appear to facilitate the identification of specific gut bacterial species. Additionally, we established quantitative rules for identifying Actinobacteria, Bacteroidetes, and Firmicutes. CONCLUSION: This research underscores the significant potential of machine learning in studying gut microbes and enhances our understanding of the multifaceted roles of gut bacteria.202540671232
7699180.9955Effects of different assembly strategies on gene annotation in activated sludge. Activated sludge comprises diverse bacteria, fungi, and other microorganisms, featuring a rich repertoire of genes involved in antibiotic resistance, pollutant degradation, and elemental cycling. In this regard, hybrid assembly technology can revolutionize metagenomics by detecting greater gene diversity in environmental samples. Nonetheless, the optimal utilization and comparability of genomic information between hybrid assembly and short- or long-read technology remain unclear. To address this gap, we compared the performance of the hybrid assembly, short- and long-read technologies, abundance and diversity of annotated genes, and taxonomic diversity by analysing 46, 161, and 45 activated sludge metagenomic datasets, respectively. The results revealed that hybrid assembly technology exhibited the best performance, generating the most contiguous and longest contigs but with a lower proportion of high-quality metagenome-assembled genomes than short-read technology. Compared with short- or long-read technologies, hybrid assembly technology can detect a greater diversity of microbiota and antibiotic resistance genes, as well as a wider range of potential hosts. However, this approach may yield lower gene abundance and pathogen detection. Our study revealed the specific advantages and disadvantages of hybrid assembly and short- and long-read applications in wastewater treatment plants, and our approach could serve as a blueprint to be extended to terrestrial environments.202438734289
3224190.9955Assessing phenotypic and genotypic antibiotic resistance in bacillus-related bacteria isolated from biogas digestates. Antibiotic resistance poses a significant public health challenge, with biogas digestate, a byproduct of anaerobic digestion (AD), presenting potential risks when applied as a biofertilizer. Understanding the actual resistance levels in digestate is crucial for its safe application. While many studies have investigated antibiotic resistance in AD processes using culture-independent molecular methods, these approaches are limited by their reliance on reference databases and inability to account for gene expression, leading to potential inaccuracies in resistance assessment. This study addresses these limitations by combining culture-independent whole-genome sequencing (WGS) with culture-dependent phenotypic testing to provide a more accurate understanding of antibiotic resistance in digestate. We investigated the phenotypic and genotypic resistance profiles of 18 antibiotic-resistant bacteria (ARB) isolated from digestates produced from food waste and animal manure. Resistance was assessed using WGS and Estrip testing across 12 antibiotics from multiple classes. This is the first study to directly compare phenotypic and genotypic resistance in bacteria isolated from digestate, revealing significant discrepancies between the two methods. Approximately 30 % of resistance levels were misinterpreted when relying solely on culture-independent methods, with both over- and underestimation observed. These findings highlight the necessity of integrating both methods for reliable resistance assessments. Additionally, our WGS analysis indicated low potential for transferability of detected ARGs among the isolated ARB, suggesting a limited risk of environmental dissemination. This study provides new insights into antibiotic resistance in digestate and underscores the importance of integrating methodological approaches to achieve accurate evaluations of resistance risks.202539947064