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511900.8956ROCker models for reliable detection and typing of short-read sequences carrying mcr, erm, mph, and lnu antibiotic resistance genes. Quantitative monitoring of emerging antimicrobial resistance genes (ARGs) using short-read sequences remains challenging due to the high frequency of amino acid functional domains and motifs shared with related but functionally distinct (non-target) proteins. To facilitate ARG monitoring efforts using unassembled short reads, we present novel ROCker models for mcr, mph, erm, and lnu ARG families, as well as models for variants of special public health concern within these families, including mcr-1, mphA, ermB, lnuF, lnuB, and lnuG genes. For this, we curated target gene sequence sets for model training and built these models using the recently updated ROCker V2 pipeline (Gerhardt et al., in review). To validate our models, we simulated reads from the whole genome of ARG-carrying isolates spanning a range of common read lengths and used them to challenge the filtering efficacy of ROCker versus common static filtering approaches, such as similarity searches using BLASTx with various e-value thresholds or hidden Markov models. ROCker models consistently showed F1 scores up to 10× higher (31% higher on average) and lower false-positive (by 30%, on average) and false-negative (by 16%, on average) rates based on 250 bp reads compared to alternative methods. The ROCker models and all related reference materials and data are freely available through http://enve-omics.ce.gatech.edu/rocker/models, further expanding the available model collection previously developed for other genes. Their application to short-read metagenomes, metatranscriptomes, and PCR amplicon data should facilitate more accurate classification and quantification of unassembled short-read sequences for these ARG families and specific genes.IMPORTANCEAntimicrobial resistance gene families encoding erm and mph genes confer resistance to the macrolide class of antimicrobials, which are used to treat a wide range of infections. Similarly, the mcr gene family confers resistance to polymyxin E (colistin), a drug of last resort for many serious drug-resistant bacterial infections, and the lnu gene family confers resistance to lincomycin, which is reserved for patients allergic to penicillin or where bacteria have developed resistance to other antimicrobials. Assessing the prevalence of these genes in clinical or environmental samples and monitoring their spread to new pathogens are thus important for quantifying the associated public health risk. However, detecting these and other resistance genes in short-read sequence data is technically challenging. Our ROCker bioinformatic pipeline achieves reliable detection and typing of broad-range target gene sequences in complex data sets, thus contributing toward solving an important problem in ongoing surveillance efforts of antimicrobial resistance.202541143534
523610.8939Genome characterization of a multi-drug resistant Escherichia coli strain, L1PEag1, isolated from commercial cape gooseberry fruits (Physalis peruviana L.). INTRODUCTION: Foodborne infections, which are frequently linked to bacterial contamination, are a serious concern to public health on a global scale. Whether agricultural farming practices help spread genes linked to antibiotic resistance in bacteria associated with humans or animals is a controversial question. METHODS: This study applied a long-read Oxford Nanopore MinION-based sequencing to obtain the complete genome sequence of a multi-drug resistant Escherichia coli strain (L1PEag1), isolated from commercial cape gooseberry fruits (Physalis peruviana L.) in Ecuador. Using different genome analysis tools, the serotype, Multi Locus Sequence Typing (MLST), virulence genes, and antimicrobial resistance (AMR) genes of the L1PEag1 isolate were determined. Additionally, in vitro assays were performed to demonstrate functional genes. RESULTS: The complete genome sequence of the L1PEag1 isolate was assembled into a circular chromosome of 4825.722 Kbp and one plasmid of 3.561 Kbp. The L1PEag1 isolate belongs to the B2 phylogroup, sequence type ST1170, and O1:H4 serotype based on in silico genome analysis. The genome contains 4,473 genes, 88 tRNA, 8 5S rRNA, 7 16S rRNA, and 7 23S rRNA. The average GC content is 50.58%. The specific annotation consisted of 4,439 and 3,723 genes annotated with KEEG and COG respectively, 3 intact prophage regions, 23 genomic islands (GIs), and 4 insertion sequences (ISs) of the ISAs1 and IS630 families. The L1PEag1 isolate carries 25 virulence genes, and 4 perfect and 51 strict antibiotic resistant gene (ARG) regions based on VirulenceFinder and RGI annotation. Besides, the in vitro antibiotic profile indicated resistance to kanamycin (K30), azithromycin (AZM15), clindamycin (DA2), novobiocin (NV30), amikacin (AMK30), and other antibiotics. The L1PEag1 isolate was predicted as a human pathogen, matching 464 protein families (0.934 likelihood). CONCLUSION: Our work emphasizes the necessity of monitoring environmental antibiotic resistance, particularly in commercial settings to contribute to develop early mitigation techniques for dealing with resistance diffusion.202439104589
252220.8924Identification and specificity validation of unique and antimicrobial resistance genes to trace suspected pathogenic AMR bacteria and to monitor the development of AMR in non-AMR strains in the environment and clinical settings. The detection of developing antimicrobial resistance (AMR) has become a global issue. The detection of developing antimicrobial resistance has become a global issue. The growing number of AMR bacteria poses a new threat to public health. Therefore, a less laborious and quick confirmatory test becomes important for further investigations into developing AMR in the environment and in clinical settings. This study aims to present a comprehensive analysis and validation of unique and antimicrobial-resistant strains from the WHO priority list of antimicrobial-resistant bacteria and previously reported AMR strains such as Acinetobacter baumannii, Aeromonas spp., Anaeromonas frigoriresistens, Anaeromonas gelatinfytica, Bacillus spp., Campylobacter jejuni subsp. jejuni, Enterococcus faecalis, Escherichia coli, Haemophilus influenzae, Helicobacter pylori, Klebsiella pneumonia subsp. pneumoniae, Pseudomonas aeruginosa, Salmonella enterica subsp. enterica serovar Typhimurium, Thermanaeromonas toyohensis, and Vibrio proteolyticus. Using in-house designed gene-specific primers, 18 different antibiotic resistance genes (algJ, alpB, AQU-1, CEPH-A3, ciaB, CMY-1-MOX-7, CMY-1-MOX-9, CMY-1/MOX, cphA2, cphA5, cphA7, ebpA, ECP_4655, fliC, OXA-51, RfbU, ThiU2, and tolB) from 46 strains were selected and validated. Hence, this study provides insight into the identification of strain-specific, unique antimicrobial resistance genes. Targeted amplification and verification using selected unique marker genes have been reported. Thus, the present detection and validation use a robust method for the entire experiment. Results also highlight the presence of another set of 18 antibiotic-resistant and unique genes (Aqu1, cphA2, cphA3, cphA5, cphA7, cmy1/mox7, cmy1/mox9, asaI, ascV, asoB, oxa-12, acr-2, pepA, uo65, pliI, dr0274, tapY2, and cpeT). Of these sets of genes, 15 were found to be suitable for the detection of pathogenic strains belonging to the genera Aeromonas, Pseudomonas, Helicobacter, Campylobacter, Enterococcus, Klebsiella, Acinetobacter, Salmonella, Haemophilus, and Bacillus. Thus, we have detected and verified sets of unique and antimicrobial resistance genes in bacteria on the WHO Priority List and from published reports on AMR bacteria. This study offers advantages for confirming antimicrobial resistance in all suspected AMR bacteria and monitoring the development of AMR in non-AMR bacteria, in the environment, and in clinical settings.202338058762
843930.8923Comparative genomics analysis and virulence-related factors in novel Aliarcobacter faecis and Aliarcobacter lanthieri species identified as potential opportunistic pathogens. BACKGROUND: Emerging pathogenic bacteria are an increasing threat to public health. Two recently described species of the genus Aliarcobacter, A. faecis and A. lanthieri, isolated from human or livestock feces, are closely related to Aliarcobacter zoonotic pathogens (A. cryaerophilus, A. skirrowii, and A. butzleri). In this study, comparative genomics analysis was carried out to examine the virulence-related, including virulence, antibiotic, and toxin (VAT) factors in the reference strains of A. faecis and A. lanthieri that may enable them to become potentially opportunistic zoonotic pathogens. RESULTS: Our results showed that the genomes of the reference strains of both species have flagella genes (flaA, flaB, flgG, flhA, flhB, fliI, fliP, motA and cheY1) as motility and export apparatus, as well as genes encoding the Twin-arginine translocation (Tat) (tatA, tatB and tatC), type II (pulE and pulF) and III (fliF, fliN and ylqH) secretory pathways, allowing them to secrete proteins into the periplasm and host cells. Invasion and immune evasion genes (ciaB, iamA, mviN, pldA, irgA and fur2) are found in both species, while adherence genes (cadF and cj1349) are only found in A. lanthieri. Acid (clpB), heat (clpA and clpB), osmotic (mviN), and low-iron (irgA and fur2) stress resistance genes were observed in both species, although urease genes were not found in them. In addition, arcB, gyrA and gyrB were found in both species, mutations of which may mediate the resistance to quaternary ammonium compounds (QACs). Furthermore, 11 VAT genes including six virulence (cadF, ciaB, irgA, mviN, pldA, and tlyA), two antibiotic resistance [tet(O) and tet(W)] and three cytolethal distending toxin (cdtA, cdtB, and cdtC) genes were validated with the PCR assays. A. lanthieri tested positive for all 11 VAT genes. By contrast, A. faecis showed positive for ten genes except for cdtB because no PCR assay for this gene was available for this species. CONCLUSIONS: The identification of the virulence, antibiotic-resistance, and toxin genes in the genomes of A. faecis and A. lanthieri reference strains through comparative genomics analysis and PCR assays highlighted the potential zoonotic pathogenicity of these two species. However, it is necessary to extend this study to include more clinical and environmental strains to explore inter-species and strain-level genetic variations in virulence-related genes and assess their potential to be opportunistic pathogens for animals and humans.202235761183
523540.8923Draft genome sequences of rare Lelliottia nimipressuralis strain MEZLN61 and two Enterobacter kobei strains MEZEK193 and MEZEK194 carrying mobile colistin resistance gene mcr-9 isolated from wastewater in South Africa. OBJECTIVES: Antimicrobial-resistant bacteria of the order Enterobacterales are emerging threats to global public and animal health, leading to morbidity and mortality. The emergence of antimicrobial-resistant, livestock-associated pathogens is a great public health concern. The genera Enterobacter and Lelliottia are ubiquitous, facultatively anaerobic, motile, non-spore-forming, rod-shaped Gram-negative bacteria belonging to the Enterobacteriaceae family and include pathogens of public health importance. Here, we report the first draft genome sequences of a rare Lelliottia nimipressuralis strain MEZLN61 and two Enterobacter kobei strains MEZEK193 and MEZEK194 in Africa. METHODS: The bacteria were isolated from environmental wastewater samples. Bacteria were cultured on nutrient agar, and the pure cultures were subjected to whole-genome sequencing. Genomic DNA was sequenced using an Illumina MiSeq platform. Generated reads were trimmed and subjected to de novo assembly. The assembled contigs were analysed for virulence genes, antimicrobial resistance genes, and extra-chromosomal plasmids, and multilocus sequence typing was performed. To compare the sequenced strains with other, previously sequenced E. kobei and L. nimipressuralis strains, available raw read sequences were downloaded, and all sequence files were treated identically to generate core genome bootstrapped maximum likelihood phylogenetic trees. RESULTS: Whole-genome sequencing analyses identified strain MEZLN61 as L. nimipressuralis and strains MEZEK193 and MEZEK194 as E. kobei. MEZEK193 and MEZEK194 carried genes encoding resistance to fosfomycin (fosA), beta-lactam antibiotics (bla(ACT-9)), and colistin (mcr-9). Additionally, MEZEK193 harboured nine different virulence genes, while MEZEK194 harboured eleven different virulence genes. The phenotypic analysis showed that L. nimipressuralis strain MEZLN61 was susceptible to colistin (2 μg/mL), while E. kobei MEZEK193 (64 μg/mL) and MEZEK194 (32 μg/mL) were resistant to colistin. CONCLUSION: The genome sequences of strains L. nimipressuralis MEZLN6, E. kobei MEZEK193, and E. kobei MEZEK194 will serve as a reference point for molecular epidemiological studies of L. nimipressuralis and E. kobei in Africa. In addition, this study provides an in-depth analysis of the genomic structure and offers important information that helps clarify the pathogenesis and antimicrobial resistance of L. nimipressuralis and E. kobei. The detection of mcr-9, which is associated with very low-level colistin resistance in Enterobacter species, is alarming and may indicate the undetected dissemination of mcr genes in bacteria of the order Enterobacterales. Continuous monitoring and surveillance of the prevalence of mcr genes and their associated phenotypic changes in clinically important pathogens and environmentally associated bacteria is necessary to control and prevent the spread of colistin resistance.202336948496
908350.8908ARGNet: 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.202438725076
906860.8901TnCentral: a Prokaryotic Transposable Element Database and Web Portal for Transposon Analysis. We describe here the structure and organization of TnCentral (https://tncentral.proteininformationresource.org/ [or the mirror link at https://tncentral.ncc.unesp.br/]), a web resource for prokaryotic transposable elements (TE). TnCentral currently contains ∼400 carefully annotated TE, including transposons from the Tn3, Tn7, Tn402, and Tn554 families; compound transposons; integrons; and associated insertion sequences (IS). These TE carry passenger genes, including genes conferring resistance to over 25 classes of antibiotics and nine types of heavy metal, as well as genes responsible for pathogenesis in plants, toxin/antitoxin gene pairs, transcription factors, and genes involved in metabolism. Each TE has its own entry page, providing details about its transposition genes, passenger genes, and other sequence features required for transposition, as well as a graphical map of all features. TnCentral content can be browsed and queried through text- and sequence-based searches with a graphic output. We describe three use cases, which illustrate how the search interface, results tables, and entry pages can be used to explore and compare TE. TnCentral also includes downloadable software to facilitate user-driven identification, with manual annotation, of certain types of TE in genomic sequences. Through the TnCentral homepage, users can also access TnPedia, which provides comprehensive reviews of the major TE families, including an extensive general section and specialized sections with descriptions of insertion sequence and transposon families. TnCentral and TnPedia are intuitive resources that can be used by clinicians and scientists to assess TE diversity in clinical, veterinary, and environmental samples. IMPORTANCE The ability of bacteria to undergo rapid evolution and adapt to changing environmental circumstances drives the public health crisis of multiple antibiotic resistance, as well as outbreaks of disease in economically important agricultural crops and animal husbandry. Prokaryotic transposable elements (TE) play a critical role in this. Many carry "passenger genes" (not required for the transposition process) conferring resistance to antibiotics or heavy metals or causing disease in plants and animals. Passenger genes are spread by normal TE transposition activities and by insertion into plasmids, which then spread via conjugation within and across bacterial populations. Thus, an understanding of TE composition and transposition mechanisms is key to developing strategies to combat bacterial pathogenesis. Toward this end, we have developed TnCentral, a bioinformatics resource dedicated to describing and exploring the structural and functional features of prokaryotic TE whose use is intuitive and accessible to users with or without bioinformatics expertise.202134517763
506970.8896MC-PRPA-HLFIA Cascade Detection System for Point-of-Care Testing Pan-Drug-Resistant Genes in Urinary Tract Infection Samples. Recently, urinary tract infection (UTI) triggered by bacteria carrying pan-drug-resistant genes, including carbapenem resistance gene bla(NDM) and bla(KPC), colistin resistance gene mcr-1, and tet(X) for tigecycline resistance, have been reported, posing a serious challenge to the treatment of clinical UTI. Therefore, point-of-care (POC) detection of these genes in UTI samples without the need for pre-culturing is urgently needed. Based on PEG 200-enhanced recombinase polymerase amplification (RPA) and a refined Chelex-100 lysis method with HRP-catalyzed lateral flow immunoassay (LFIA), we developed an MCL-PRPA-HLFIA cascade assay system for detecting these genes in UTI samples. The refined Chelex-100 lysis method extracts target DNA from UTI samples in 20 min without high-speed centrifugation or pre-incubation of urine samples. Following optimization, the cascade detection system achieved an LOD of 10(2) CFU/mL with satisfactory specificity and could detect these genes in both simulated and actual UTI samples. It takes less than an hour to complete the process without the use of high-speed centrifuges or other specialized equipment, such as PCR amplifiers. The MCL-PRPA-HLFIA cascade assay system provides new ideas for the construction of rapid detection methods for pan-drug-resistant genes in clinical UTI samples and provides the necessary medication guidance for UTI treatment.202337047757
509780.8894Comparing Graph Sample and Aggregation (SAGE) and Graph Attention Networks in the Prediction of Drug-Gene Associations of Extended-Spectrum Beta-Lactamases in Periodontal Infections and Resistance. INTRODUCTION: Gram-negative bacteria exhibit more antibiotic resistance than gram-positive bacteria due to their cell wall structure and composition differences. Porins, or protein channels in these bacteria, can allow small, hydrophilic antibiotics to diffuse, affecting their susceptibility. Mutations in porin protein genes can also impair antibiotic entry. Predicting drug-gene associations of extended-spectrum beta-lactamases (ESBLs) is crucial as they confer resistance to beta-lactam antibiotics, challenging the treatment of infections. This aids clinicians in selecting suitable treatments, optimizing drug usage, enhancing patient outcomes, and controlling antibiotic resistance in healthcare settings. Graph-based neural networks can predict drug-gene associations in periodontal infections and resistance. The aim of the study was to predict drug-gene associations of ESBLs in periodontal infections and resistance. METHODS: The study focuses on analyzing drug-gene associations using probes and drugs. The data was converted into graph language, assigning nodes and edges for drugs and genes. Graph neural networks (GNNs) and similar algorithms were implemented using Google Colab and Python. Cytoscape and CytoHubba are open-source software platforms used for network analysis and visualization. GNNs were used for tasks like node classification, link prediction, and graph-level prediction. Three graph-based models were used: graph convolutional network (GCN), Graph SAGE, and graph attention network (GAT). Each model was trained for 200 epochs using the Adam optimizer with a learning rate of 0.01 and a weight decay of 5e-4. RESULTS: The drug-gene association network has 57 nodes, 79 edges, and a 2.730 characteristic path length. Its structure, organization, and connectivity are analyzed using the GCN and Graph SAGE, which show high accuracy, precision, recall, and an F1-score of 0.94. GAT's performance metrics are lower, with an accuracy of 0.68, precision of 0.47, recall of 0.68, and F1-score of 0.56, suggesting that it may not be as effective in capturing drug-gene relationships. CONCLUSION: Compared to ESBLs, both GCN and Graph SAGE demonstrate excellent performance with accuracy, precision, recall, and an F1-score of 0.94. These results indicate that GCN and Graph SAGE are highly effective in predicting drug-gene associations related to ESBLs. GCN and Graph SAGE outperform GAT in predicting drug-gene associations for ESBLs. Improvements include data augmentation, regularization, and cross-validation. Ethical considerations, fairness, and open-source implementations are crucial for future research in precision periodontal treatment.202439347119
907690.8890ResiDB: An automated database manager for sequence data. The amount of publicly available DNA sequence data is drastically increasing, making it a tedious task to create sequence databases necessary for the design of diagnostic assays. The selection of appropriate sequences is especially challenging in genes affected by frequent point mutations such as antibiotic resistance genes. To overcome this issue, we have designed the webtool resiDB, a rapid and user-friendly sequence database manager for bacteria, fungi, viruses, protozoa, invertebrates, plants, archaea, environmental and whole genome shotgun sequence data. It automatically identifies and curates sequence clusters to create custom sequence databases based on user-defined input sequences. A collection of helpful visualization tools gives the user the opportunity to easily access, evaluate, edit, and download the newly created database. Consequently, researchers do no longer have to manually manage sequence data retrieval, deal with hardware limitations, and run multiple independent software tools, each having its own requirements, input and output formats. Our tool was developed within the H2020 project FAPIC aiming to develop a single diagnostic assay targeting all sepsis-relevant pathogens and antibiotic resistance mechanisms. ResiDB is freely accessible to all users through https://residb.ait.ac.at/.202133495705
9742100.8890BOCS: DNA k-mer content and scoring for rapid genetic biomarker identification at low coverage. A single, inexpensive diagnostic test capable of rapidly identifying a wide range of genetic biomarkers would prove invaluable in precision medicine. Previous work has demonstrated the potential for high-throughput, label-free detection of A-G-C-T content in DNA k-mers, providing an alternative to single-letter sequencing while also having inherent lossy data compression and massively parallel data acquisition. Here, we apply a new bioinformatics algorithm - block optical content scoring (BOCS) - capable of using the high-throughput content k-mers for rapid, broad-spectrum identification of genetic biomarkers. BOCS uses content-based sequence alignment for probabilistic mapping of k-mer contents to gene sequences within a biomarker database, resulting in a probability ranking of genes on a content score. Simulations of the BOCS algorithm reveal high accuracy for identification of single antibiotic resistance genes, even in the presence of significant sequencing errors (100% accuracy for no sequencing errors, and >90% accuracy for sequencing errors at 20%), and at well below full coverage of the genes. Simulations for detecting multiple resistance genes within a methicillin-resistant Staphylococcus aureus (MRSA) strain showed 100% accuracy at an average gene coverage of merely 0.515, when the k-mer lengths were variable and with 4% sequencing error within the k-mer blocks. Extension of BOCS to cancer and other genetic diseases met or exceeded the results for resistance genes. Combined with a high-throughput content-based sequencing technique, the BOCS algorithm potentiates a test capable of rapid diagnosis and profiling of genetic biomarkers ranging from antibiotic resistance to cancer and other genetic diseases.201931173943
3069110.8888The hospital sink drain biofilm resistome is independent of the corresponding microbiota, the environment and disinfection measures. In hospitals, the transmission of antibiotic-resistant bacteria (ARB) may occur via biofilms present in sink drains, which can lead to infections. Despite the potential role of sink drains in the transmission of ARB in nosocomial infections, routine surveillance of these drains is lacking in most hospitals. As a result, there is currently no comprehensive understanding of the transmission of ARB and the dissemination of antimicrobial resistance genes (ARGs) and associated mobile genetic elements (MGEs) via sink drains. This study employed a multifaceted approach to monitor the total aerobic bacteria as well as the presence of carbapenemase-producing Enterobacterales (CPEs), the microbiota and the resistome of sink drain biofilms (SDBs) and hospital wastewater (WW) of two separate intensive care units (ICUs) in the same healthcare facility in France. Samples of SDB and WW were collected on a monthly basis, from January to April 2023, in the neonatal (NICU) and the adult (AICU) ICUs of Grenoble Alpes University Hospital. In the NICU, sink drain disinfection with surfactants was performed routinely. In the AICU, routine disinfection is not carried out. Culturable aerobic bacteria were quantified on non-selective media, and CPEs were screened using two selective agars. Isolates were identified by MALDI-TOF MS, and antibiotic susceptibility testing (AST) was performed on Enterobacterales and P. aeruginosa. The resistome was analyzed by high-throughput qPCR targeting >80 ARGs and MGEs. The overall bacterial microbiota was assessed via full-length 16S rRNA sequencing. No CPEs were isolated from SDBs in either ICU by bacterial culture. Culture-independent approaches revealed an overall distinct microbiota composition of the SDBs in the two ICUs. The AICU SDBs were dominated by pathogens containing Gram-negative bacterial genera including Pseudomonas, Stenotrophomona, Klebsiella, and Gram-positive Staphylococcus, while the NICU SDBs were dominated by the Gram-negative genera Achromobacter, Serratia, and Acidovorax, as well as the Gram-positive genera Weisella and Lactiplantibacillus. In contrast, the resistome of the SDBs exhibited no significant differences between the two ICUs, indicating that the abundance of ARGs and MGEs is independent of microbiota composition and disinfection practices. The AICU WW exhibited more distinct aerobic bacteria than the NICU WW. In addition, the AICU WW yielded 15 CPEs, whereas the NICU WW yielded a single CPE. All the CPEs were characterized at the species level. The microbiota of the NICU and AICU WW samples differed from their respective SDBs and exhibited distinct variations over the four-month period:the AICU WW contained a greater number of genes conferring resistance to quinolones and integron integrase genes, whereas the NICU WW exhibited a higher abundance of streptogramin resistance genes. Our study demonstrated that the resistome of the hospital SDBs in the two ICUs of the investigated healthcare institute is independent of the microbiota, the environment, and the local disinfection measures. However, the prevalence of CPEs in the WW pipes collecting the waste from the investigated drains differed. These findings offer valuable insights into the resilience of resistance genes in SDBs in ICUs, underscoring the necessity for innovative strategies to combat antimicrobial resistance in clinical environments.202540483807
5035120.8887Colistin and tigecycline resistance in carbapenemase-producing Gram-negative bacteria: emerging resistance mechanisms and detection methods. A literature review was undertaken to ascertain the molecular basis for tigecycline and colistin resistance mechanisms and the experimental basis for the detection and delineation of this resistance particularly in carbapenemase-producing Gram-negative bacteria. Pubmed, Google Scholar and Science Direct were searched with the keywords colistin, tigecycline, resistance mechanisms and detection methods. Trans-complementation and comparative MIC studies, mass spectrometry, chromatography, spectrofluorometry, PCR, qRT-PCR and whole genome sequencing (WGS) were commonly used to determine tigecycline and colistin resistance mechanisms, specifically modifications in the structural and regulatory efflux (acrAB, OqxAB, kpgABC adeABC-FGH-IJK, mexAB-XY-oprJM and soxS, rarA robA, ramRAB marRABC, adeLRS, mexRZ and nfxb) and lipid A (pmrHFIJFKLM, lpxA, lpxC lpxD and mgrB, pmrAB, phoPQ,) genes respectively. Mutations in the ribosomal 16S rRNA operon rrnBC, also yielded resistance to tigecycline through target site modifications. The mcr-1 gene conferring resistance to colistin was identified via WGS, trans-complementation and a murine thigh infection model studies. Common detection methods are mainly antibiotic sensitivity testing with broth microdilution while molecular identification tools are mostly PCR and WGS. Spectrofluorometry, MALDI-TOF MS, micro-array and real-time multiplex PCR hold much promise for the future as new detection tools.201627153928
5068130.8885Ultrasensitive Label-Free Detection of Unamplified Multidrug-Resistance Bacteria Genes with a Bimodal Waveguide Interferometric Biosensor. Infections by multidrug-resistant bacteria are becoming a major healthcare emergence with millions of reported cases every year and an increasing incidence of deaths. An advanced diagnostic platform able to directly detect and identify antimicrobial resistance in a faster way than conventional techniques could help in the adoption of early and accurate therapeutic interventions, limiting the actual negative impact on patient outcomes. With this objective, we have developed a new biosensor methodology using an ultrasensitive nanophotonic bimodal waveguide interferometer (BiMW), which allows a rapid and direct detection, without amplification, of two prevalent and clinically relevant Gram-negative antimicrobial resistance encoding sequences: the extended-spectrum betalactamase-encoding gene blaCTX-M-15 and the carbapenemase-encoding gene blaNDM-5 We demonstrate the extreme sensitivity and specificity of our biosensor methodology for the detection of both gene sequences. Our results show that the BiMW biosensor can be employed as an ultrasensitive (attomolar level) and specific diagnostic tool for rapidly (less than 30 min) identifying drug resistance. The BiMW nanobiosensor holds great promise as a powerful tool for the control and management of healthcare-associated infections by multidrug-resistant bacteria.202033086716
3548140.8882From flagellar assembly to DNA replication: CJSe's role in mitigating microbial antibiotic resistance genes. The emergence of Antibiotic Resistance Genes (ARGs) in Campylobacter jejuni (CJ) poses a severe threat to food safety and human health. However, the specific impact of CJ and its variants on ARGs and other related factors remains to be further elucidated. Herein, integrated metagenomic sequencing and co-occurrence network analysis approach were employed to investigate the impact of CJ and CJ incorporated with biogenic selenium (CJSe) on ARGs, flagellar assembly pathways, microbial communities, and DNA replication pathways in chicken manure. Compared to the Control (CON) and CJ groups, the CJSe group exhibited 2.4-fold increase selenium levels (P < 0.01) in chicken manure. Notable differences were also observed between the CJ and CJSe groups, with sequence results showing a CJ > CJSe > CON trend in total ARG copy numbers. Furthermore, the CJSe group showed 31.6 % fewer flagellar assembly genes compared to the CJ group. Additionally, compared to the CJ group, CJSe inhibited pathways such as basal body/hook (e.g., FliH, FliO, FliQ reduced by 25-52 %) and stator (MotB downregulated by 42.3 %), suppressing flagellar assembly. We also found that both CJ and CJSe influenced bacterial DNA replication pathways, with the former increasing ARG-carrying bacteria and the latter, under selenium-induced selective pressure, reducing ARG-carrying bacteria. Moreover, compared to the CJ group, the CJSe group showed a significantly lower 9.72 % copy number of total archaeal DNA replication genes. Furthermore, through intricate co-occurrence network analysis, we discovered the complex interplay between changes in ARGs and bacterial and archaeal DNA replication dynamics within the microbial community. These findings indicate that CJSe mitigates the threat posed by CJ and reduces ARG prevalence, while its dual functionality enables applications in biofortified crop production and soil remediation in selenium-deficient regions, thereby advancing circular economy systems. While the current study demonstrates CJSe's dual functionality under controlled conditions, future work will implement a dedicated ecological risk assessment framework encompassing Se speciation/leaching tests and non-target organism assays to confirm environmental safety under field-relevant scenarios. This approach aligns with sustainable strategies for food security and public health safeguarding.202541108960
5203150.8881Draft genome sequence analysis of a novel MLST (ST5028) and multidrug-resistant Klebsiella quasipneumoniae subsp. similipneumoniae (Kp4) strain 456S1 isolated from a pig farm in China. OBJECTIVES: The avian breeding industry is an important element in exposing bacteria to antibiotics. As one of the major animal welfare and economic problems for the poultry industry, multidrug-resistant Klebsiella spp. have become a substantial source of antibiotic resistance genes. In the present work, we reported the draft genome sequence of a novel multilocus sequence type (MLST) (ST5028) Klebsiella quasipneumoniae subsp. similipneumoniae (Kp4) strain 456S1, which was isolated from a pig farm in China with broad-spectrum antimicrobial activities. METHODS: Classical microbiological methods were applied to isolate and identify the strain, genomic DNA was sequenced using an Illumina HiSeq platform, and the reads were de novo assembled into contigs using CLC Genomics Workbench. The assembled contigs were annotated, and whole-genome sequencing (WGS) analysis was performed. RESULTS: WGS analysis revealed that the genome of strain 456S1 comprised a circular chromosome of 5,419,059 bp (GC content, 57.8%), harbouring 12 important antibiotic resistance genes: aac(6')-ib-cr, aadA16, floR, dfrA27, fosA, tet(D), blaOKP-B-3, oqxA, oqxB, qnrB6, sul1 and arr-3. The Klebsiella quasipneumoniae subsp. similipneumoniae (Kp4) 456S1 was also found to belong to a novel sequence type (ST5028) determined by MLST. CONCLUSION: The genome sequence reported herein will provide useful information for antibiotic resistance and pathogenic mechanisms in Klebsiella quasipneumoniae and will be a reference for comparative analysis with genomic features among different sources of clinically important multidrug-resistant strains, especially among bacteria of animal and human origin.202133516893
1996160.8880Conjugation of plasmid harboring bla (NDM-1) in a clinical Providencia rettgeri strain through the formation of a fusion plasmid. Providencia rettgeri has recently gained increased importance owing to the New Delhi metallo-β-lactamase (NDM) and other β-lactamases produced by its clinical isolates. These enzymes reduce the efficiency of antimicrobial therapy. Herein, we reported the findings of whole-genome sequence analysis and a comprehensive pan-genome analysis performed on a multidrug-resistant P. rettgeri 18004577 clinical strain recovered from the urine of a hospitalized patient in Shandong, China, in 2018. Providencia rettgeri 18004577 was found to have a genome assembly size of 4.6 Mb with a G + C content of 41%; a circular plasmid p18004577_NDM of 273.3 Kb, harboring an accessory multidrug-resistant region; and a circular, stable IncT plasmid p18004577_Rts of 146.2 Kb. Additionally, various resistance genes were identified in its genome, including bla (NDM-1), bla (OXA-10), bla (PER-4), aph(3')-VI, ant(2'')-Ia, ant(3')-Ia, sul1, catB8, catA1, mph(E), and tet. Conjugation experiments and whole-genome sequencing revealed that the bla (NDM-1) gene could be transferred to the transconjugant via the formation of pJ18004577_NDM, a novel hybrid plasmid. Based on the genetic comparison, the main possible formation process for pJ18004577_NDM was the insertion of the [ΔISKox2-IS26-ΔISKox2]-aph(3')-VI-bla (NDM-1) translocatable unit module from p18004577_NDM into plasmid p18004577_Rts in the Russian doll insertion structure (ΔISKox2-IS26-ΔISKox2), which played a role similar to that of IS26 using the "copy-in" route in the mobilization of [aph(3')-VI]-bla (NDM-1). The array, multiplicity, and diversity of the resistance and virulence genes in this strain necessitate stringent infection control, antibiotic stewardship, and periodic resistance surveillance/monitoring policies to preempt further horizontal and vertical spread of the resistance genes. Roary analysis based on 30 P. rettgeri strains pan genome identified 415 core, 756 soft core, 5,744 shell, and 12,967 cloud genes, highlighting the "close" nature of P. rettgeri pan-genome. After a comprehensive pan-genome analysis, representative biological information was revealed that included phylogenetic distances, presence or absence of genes across the P. rettgeri bacteria clade, and functional distribution of proteins. Moreover, pan-genome analysis has been shown to be an effective approach to better understand P. rettgeri bacteria because it helps develop various tailored therapeutic strategies based on their biological similarities and differences.202236687647
5200170.8879Whole genome sequencing of the multidrug-resistant Chryseobacterium indologenes isolated from a patient in Brazil. Chryseobacterium indologenes is a non-glucose-fermenting Gram-negative bacillus. This emerging multidrug resistant opportunistic nosocomial pathogen can cause severe infections in neonates and immunocompromised patients. This study aimed to present the first detailed draft genome sequence of a multidrug-resistant C. indologenes strain isolated from the cerebrospinal fluid of an infant hospitalized at the Neonatal Intensive Care Unit of Brazilian Tertiary Hospital. We first analyzed the susceptibility of C. indologenes strain to different antibiotics using the VITEK 2 system. The strain demonstrated an outstanding resistance to all the antibiotic classes tested, including β-lactams, aminoglycosides, glycylcycline, and polymyxin. Next, C. indologenes was whole-genome-sequenced, annotated using Prokka and Rapid Annotation using Subsystems Technology (RAST), and screened for orthologous groups (EggNOG), gene ontology (GO), resistance genes, virulence genes, and mobile genetic elements using different software tools. The draft genome contained one circular chromosome of 4,836,765 bp with 37.32% GC content. The genomic features of the chromosome present numerous genes related to cellular processes that are essential to bacteria. The MDR C. indologenes revealed the presence of genes that corresponded to the resistance phenotypes, including genes to β-lactamases (bla (IND-13), bla (CIA-3), bla (TEM-116), bla (OXA-209), bla (VEB-15)), quinolone (mcbG), tigecycline (tet(X6)), and genes encoding efflux pumps which confer resistance to aminoglycosides (RanA/RanB), and colistin (HlyD/TolC). Amino acid substitutions related to quinolone resistance were observed in GyrA (S83Y) and GyrB (L425I and K473R). A mutation that may play a role in the development of colistin resistance was detected in lpxA (G68D). Chryseobacterium indologenes isolate harbored 19 virulence factors, most of which were involved in infection pathways. We identified 13 Genomic Islands (GIs) and some elements associated with one integrative and conjugative element (ICEs). Other elements linked to mobile genetic elements (MGEs), such as insertion sequence (ISEIsp1), transposon (Tn5393), and integron (In31), were also present in the C. indologenes genome. Although plasmids were not detected, a ColRNAI replicon type and the most resistance genes detected in singletons were identified in unaligned scaffolds. We provided a wide range of information toward the understanding of the genomic diversity of C. indologenes, which can contribute to controlling the evolution and dissemination of this pathogen in healthcare settings.202235966843
5118180.8876Automated extraction of genes associated with antibiotic resistance from the biomedical literature. The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction.202235134132
5117190.8876Metagenomic sequencing of mpox virus clade Ib lesions identifies possible bacterial and viral co-infections in hospitalized patients in eastern DRC. Mpox is an emerging zoonotic disease that caused two public health emergencies of international concern within two years. Less is known about the interplay of microbial organisms in mpox lesions which could result in superinfections that exacerbate outcomes or delay recovery. We utilized a unified metagenomic sequencing approach involving slow-speed centrifugation and differential lysis on 19 mpox lesion swabs of hospitalized patients in South Kivu province (eastern DRC) to characterize bacteria, antimicrobial resistance genes, mpox virus (MPXV), and viral co-infections. High-quality MPXV whole-genome sequences were obtained until a Ct value of 27. Furthermore, co-infections with other clinically relevant viruses, such as varicella zoster virus and herpes simplex virus-2, were detected and confirmed by real-time PCR. In addition, metagenomic sequence analysis of the bacterial content showed the presence of bacteria associated with skin and soft tissue infection in 10 of the 19 samples analyzed. These bacteria had a high abundance of resistance genes, with possible implications for antimicrobial treatment based on the predicted antimicrobial resistance. In conclusion, we report the presence of bacterial and viral pathogens in mpox lesions and detection of widespread resistance genes to the standard antibiotic treatment. The possibility of a co-infection, including antimicrobial resistance, should be considered when discussing treatment options, along with the determination of the case-fatality ratio.IMPORTANCEThe mpox virus clade Ib lineage emerged in the eastern Democratic Republic of the Congo owing to continuous human-to-human transmission in a vulnerable patient population. A major challenge of this ongoing outbreak is its occurrence in regions with severely limited healthcare infrastructure. As a result, less is known about co-infections in affected patients. Identifying and characterizing pathogens, including their antimicrobial resistance, is crucial for reducing infection-related complications and improving antimicrobial stewardship. In this study, we applied a unified metagenomics approach to detect and characterize bacterial and viral co-infections in mpox lesions of hospitalized mpox patients in the eastern DRC.202540445195