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507000.9939Sequence-specific DNA solid-phase extraction in an on-chip monolith: Towards detection of antibiotic resistance genes. Antibiotic resistance of bacteria is a growing problem and presents a challenge for prompt treatment in patients with sepsis. Currently used methods rely on culturing or amplification; however, these steps are either time consuming or suffer from interference issues. A microfluidic device was made from black polypropylene, with a monolithic column modified with a capture oligonucleotide for sequence selective solid-phase extraction of a complementary target from a lysate sample. Porous properties of the monolith allow flow and hybridization of a target complementary to the probe immobilized on the column surface. Good flow-through properties enable extraction of a 100μL sample and elution of target DNA in 12min total time. Using a fluorescently labeled target oligonucleotide related to Verona Integron-Mediated Metallo-β-lactamase it was possible to extract and detect a 1pM sample with 83% recovery. Temperature-mediated elution by heating above the duplex melting point provides a clean extract without any agents that interfere with base pairing, allowing various labeling methods or further downstream processing of the eluent. Further integration of this extraction module with a system for isolation and lysis of bacteria from blood, as well as combining with single-molecule detection should allow rapid determination of antibiotic resistance.201728734608
907610.9936ResiDB: 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
507420.9934Cas14VIDet: A visual instant method free from PAM restriction for antibiotic resistance bacteria detection. A personalized treatment strategy that selects sensitive antibiotics based on Helicobacter pylori (H. pylori) resistance genes is currently the most effective approach to address the challenge of H. pylori eradication. However, the widespread adoption of this strategy is hindered by the long processing times and high costs associated with traditional resistance gene detection methods. In this study, we combined ultra-fast PCR with CRISPR/Cas14 into a single reaction system, establishing a rapid, one-pot visual platform named Cas14VIDet (Cas14-based Visual Instant Detection) for detecting H. pylori resistance genes. Cas14VIDet does not require a PAM sequence and excels in identifying single nucleotide polymorphisms, with the detection sensitivity approaching the level of a single bacterial colony (10(0) CFU/mL). The entire detection process can be completed within 10 min, and results are directly observable with the naked eye. We validated Cas14VIDet by testing 50 clinical samples and compared it with Sanger sequencing. The results showed that Cas14VIDet achieved 100% sensitivity, 100% specificity, and 100% accuracy in detecting H. pylori resistance genes to levofloxacin. Therefore, we believe this method holds great potential for rapid detection of H. pylori resistance, potentially supporting personalized treatment of H. pylori infections in the future.202539527901
581930.9933Application of mNGS in the Etiological Analysis of Lower Respiratory Tract Infections and the Prediction of Drug Resistance. Lower respiratory tract infections (LRTIs) have high morbidity and mortality rates. However, traditional etiological detection methods have not been able to meet the needs for the clinical diagnosis and prognosis of LRTIs. The rapid development of metagenomic next-generation sequencing (mNGS) provides new insights for the diagnosis and treatment of LRTIs; however, little is known about how to interpret the application of mNGS results in LRTIs. In this study, lower respiratory tract specimens from 46 patients with suspected LRTIs were tested simultaneously using conventional microbiological detection methods and mNGS. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the logarithm of reads per kilobase per million mapped reads [lg(RPKM)], genomic coverage, and relative abundance of the organism in predicting the true-positive pathogenic bacteria. True-positive viruses were identified according to the lg(RPKM) threshold of bacteria. We also evaluated the ability to predict drug resistance genes using mNGS. Compared to that using conventional detection methods, the false-positive detection rate of pathogenic bacteria was significantly higher using mNGS. It was concluded from the ROC curves that the lg(RPKM) and genomic coverage contributed to the identification of pathogenic bacteria, with the performance of lg(RPKM) being the best (area under the curve [AUC] = 0.99). The corresponding lg(RPKM) threshold for identifying the pathogenic bacteria was -1.35. Thirty-five strains of true-positive virus were identified based on the lg(RPKM) threshold of bacteria, with the detection of human gammaherpesvirus 4 being the highest and prone to coinfection with Pseudomonas aeruginosa, Acinetobacter baumannii, and Stenotrophomonas maltophilia. Antimicrobial susceptibility tests (AST) revealed the resistance of bacteria containing drug resistance genes (detected by mNGS). However, the drug resistance genes of some multidrug-resistant bacteria were not detected. As an emerging technology, mNGS has shown many advantages for the unbiased etiological detection and the prediction of antibiotic resistance. However, a correct understanding of mNGS results is a prerequisite for its clinical application, especially for LRTIs. IMPORTANCE LRTIs are caused by hundreds of pathogens, and they have become a great threat to human health due to the limitations of traditional etiological detection methods. As an unbiased approach to detect pathogens, mNGS overcomes such etiological diagnostic challenges. However, there is no unified standard on how to use mNGS indicators (the sequencing reads, genomic coverage, and relative abundance of each organism) to distinguish between pathogens and colonizing microorganisms or contaminant microorganisms. Here, we selected the mNGS indicator with the best identification performance and established a cutoff value for the identification of pathogens in LRTIs using ROC curves. In addition, we also evaluated the accuracy of antibiotic resistance prediction using mNGS.202235171007
974240.9933BOCS: 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
974350.9933Simultaneous Detection of Antibiotic Resistance Genes on Paper-Based Chip Using [Ru(phen)(2)dppz](2+) Turn-on Fluorescence Probe. Antibiotic resistance, the ability of some bacteria to resist antibiotic drugs, has been a major global health burden due to the extensive use of antibiotic agents. Antibiotic resistance is encoded via particular genes; hence the specific detection of these genes is necessary for diagnosis and treatment of antibiotic resistant cases. Conventional methods for monitoring antibiotic resistance genes require the sample to be transported to a central laboratory for tedious and sophisticated tests, which is grueling and time-consuming. We developed a paper-based chip, integrated with loop-mediated isothermal amplification (LAMP) and the "light switch" molecule [Ru(phen)(2)dppz](2+), to conduct turn-on fluorescent detection of antibiotic resistance genes. In this assay, the amplification reagents can be embedded into test spots of the chip in advance, thus simplifying the detection procedure. [Ru(phen)(2)dppz](2+) was applied to intercalate into amplicons for product analysis, enabling this assay to be operated in a wash-free format. The paper-based detection device exhibited a limit of detection (LOD) as few as 100 copies for antibiotic resistance genes. Meanwhile, it could detect antibiotic resistance genes from various bacteria. Noticeably, the approach can be applied to other genes besides antibiotic resistance genes by simply changing the LAMP primers. Therefore, this paper-based chip has the potential for point-of-care (POC) applications to detect various gene samples, especially in resource-limited conditions.201829323478
974160.9932ARGai 1.0: A GAN augmented in silico approach for identifying resistant genes and strains in E. coli using vision transformer. The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational techniques for identifying essential genes that contribute to resistance. The task of identifying resistant strains and multi-drug patterns in E. coli is a major challenge with whole genome sequencing (WGS) and next-generation sequencing (NGS) data. To address this issue, we suggest ARGai 1.0 a deep learning architecture enhanced with generative adversarial networks (GANs). We mitigate data scarcity difficulties by augmenting limited experimental datasets with synthetic data generated by GANs. Our in-silico method (augmentation with feature selection) improves the identification of resistance genes in E. coli by using feature extraction techniques to identify valuable features from actual and GAN-generated data. Employing comprehensive validation, we exhibit the effectiveness of our ARGai 1.0 in precisely identifying the informative and resistant genes. In addition, our ARGai 1.0 identifies the resistant strains with a classification accuracy of 98.96 % on Deep Convolutional Generative Adversarial Network (DCGAN) augmented data. Additionally, ARGai 1.0 achieves more than 98 % of sensitivity and specificity. We also benchmark our ARGai 1.0 with several state-of-the-art AI models for resistant strain classification. In the fight against antibiotic resistance, ARGai 1.0 offers a promising avenue for computational genomics. With implications for research and clinical practice, this work shows the potential of deep networks with GAN augmentation as a practical and successful method for gene identification in E. coli.202539813877
582670.9932Rapid and accurate sepsis diagnostics via a novel probe-based multiplex real-time PCR system. Sepsis is a critical clinical emergency that requires prompt diagnosis and intervention. Its prevalence has increased due to the aging population and increased antibiotic resistance. Early identification and the use of innovative technologies are crucial for improving patient outcomes. Modern methodologies are needed to minimize the turnaround time for diagnosis and improve outcomes. Rapid diagnostic tests and multiplex PCR are effective but have limitations in identifying a range of pathogens and target genes. Our study evaluated two novel probe-based multiplex real-time PCR systems: the SEPSI ID and SEPSI DR panels. These systems can quickly identify bacterial and fungal pathogens, alongside antibiotic resistance genes. The assays cover 29 microorganisms (gram-negative bacteria, gram-positive bacteria, yeast, and mold species), alongside 23 resistance genes and four virulence factors. A streamlined workflow uses 2 µL of broth from positive blood cultures (BCs) without nucleic acid extraction and provides results in approximately 1 h. We present the results from an evaluation of 228 BCs and 22 isolates previously characterized by whole-genome sequencing. In comparison to the reference methods, the SEPSI ID panel demonstrated a sensitivity of 96.88%, a specificity of 100%, and a PPV of 100%, whereas the SEPSI DR panel showed a sensitivity of 97.8%, a PPV of 89.7%, and a specificity of 96.7%. Both panels also identified additional pathogens and resistance-related targets not detected by conventional methods. This assay shows promise for rapidly and accurately diagnosing sepsis. Future studies should validate its performance in various clinical settings to enhance sepsis management and improve patient outcomes.IMPORTANCEWe present a new diagnostic method that enables the quick and precise identification of pathogens and resistance genes from positive blood cultures, eliminating the need for nucleic acid extraction. This technique can also be used on fresh pathogen cultures. It has the potential to greatly improve treatment protocols, leading to better patient outcomes, more responsible antibiotic use, and more efficient management of healthcare resources.202541025980
582880.9932Target-enriched sequencing enables accurate identification of bloodstream infections in whole blood. Bloodstream infections are within the top ten causes of death globally, with a mortality rate of up to 70%. Gold standard blood culture testing is time-consuming, resulting in delayed, but accurate, treatment. Molecular methods, such as RT-qPCR, have limited targets in one run. We present a new Ampliseq detection system (ADS) combining target amplification and next-generation sequencing for accurate identification of bacteria, fungi, and antimicrobial resistance determinants directly from blood samples. In this study, we included removal of human genomic DNA during nucleic acid extraction, optimized the target sequence set and drug resistance genes, performed antimicrobial resistance profiling of clinical isolates, and evaluated mock specimens and clinical samples by ADS. ADS successfully identified pathogens at the species-level in 36 h, from nucleic acid extraction to results. Besides pathogen identification, ADS can also present drug resistance profiles. ADS enabled detection of all bacteria and accurate identification of 47 pathogens. In 20 spiked samples and 8 clinical specimens, ADS detected at least 92.81% of reads mapped to pathogens. ADS also showed consistency with the three culture-negative samples, and correctly identified pathogens in four of five culture-positive clinical blood specimens. This Ampliseq-based technology promises broad coverage and accurate pathogen identification, helping clinicians to accurately diagnose and treat bloodstream infections.202234915067
354090.9931Microbial contents of vacuum cleaner bag dust and emitted bioaerosols and their implications for human exposure indoors. Vacuum cleaners can release large concentrations of particles, both in their exhaust air and from resuspension of settled dust. However, the size, variability, and microbial diversity of these emissions are unknown, despite evidence to suggest they may contribute to allergic responses and infection transmission indoors. This study aimed to evaluate bioaerosol emission from various vacuum cleaners. We sampled the air in an experimental flow tunnel where vacuum cleaners were run, and their airborne emissions were sampled with closed-face cassettes. Dust samples were also collected from the dust bag. Total bacteria, total archaea, Penicillium/Aspergillus, and total Clostridium cluster 1 were quantified with specific quantitative PCR protocols, and emission rates were calculated. Clostridium botulinum and antibiotic resistance genes were detected in each sample using endpoint PCR. Bacterial diversity was also analyzed using denaturing gradient gel electrophoresis (DGGE), image analysis, and band sequencing. We demonstrated that emission of bacteria and molds (Penicillium/Aspergillus) can reach values as high as 1E5 cell equivalents/min and that those emissions are not related to each other. The bag dust bacterial and mold content was also consistent across the vacuums we assessed, reaching up to 1E7 bacterial or mold cell equivalents/g. Antibiotic resistance genes were detected in several samples. No archaea or C. botulinum was detected in any air samples. Diversity analyses showed that most bacteria are from human sources, in keeping with other recent results. These results highlight the potential capability of vacuum cleaners to disseminate appreciable quantities of molds and human-associated bacteria indoors and their role as a source of exposure to bioaerosols.201323934489
5115100.9931Search Engine for Antimicrobial Resistance: A Cloud Compatible Pipeline and Web Interface for Rapidly Detecting Antimicrobial Resistance Genes Directly from Sequence Data. BACKGROUND: Antimicrobial resistance remains a growing and significant concern in human and veterinary medicine. Current laboratory methods for the detection and surveillance of antimicrobial resistant bacteria are limited in their effectiveness and scope. With the rapidly developing field of whole genome sequencing beginning to be utilised in clinical practice, the ability to interrogate sequencing data quickly and easily for the presence of antimicrobial resistance genes will become increasingly important and useful for informing clinical decisions. Additionally, use of such tools will provide insight into the dynamics of antimicrobial resistance genes in metagenomic samples such as those used in environmental monitoring. RESULTS: Here we present the Search Engine for Antimicrobial Resistance (SEAR), a pipeline and web interface for detection of horizontally acquired antimicrobial resistance genes in raw sequencing data. The pipeline provides gene information, abundance estimation and the reconstructed sequence of antimicrobial resistance genes; it also provides web links to additional information on each gene. The pipeline utilises clustering and read mapping to annotate full-length genes relative to a user-defined database. It also uses local alignment of annotated genes to a range of online databases to provide additional information. We demonstrate SEAR's application in the detection and abundance estimation of antimicrobial resistance genes in two novel environmental metagenomes, 32 human faecal microbiome datasets and 126 clinical isolates of Shigella sonnei. CONCLUSIONS: We have developed a pipeline that contributes to the improved capacity for antimicrobial resistance detection afforded by next generation sequencing technologies, allowing for rapid detection of antimicrobial resistance genes directly from sequencing data. SEAR uses raw sequencing data via an intuitive interface so can be run rapidly without requiring advanced bioinformatic skills or resources. Finally, we show that SEAR is effective in detecting antimicrobial resistance genes in metagenomic and isolate sequencing data from both environmental metagenomes and sequencing data from clinical isolates.201526197475
9083110.9930ARGNet: 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
9745120.9930Analysis of Identification Method for Bacterial Species and Antibiotic Resistance Genes Using Optical Data From DNA Oligomers. Bacterial antibiotic resistance is becoming a significant health threat, and rapid identification of antibiotic-resistant bacteria is essential to save lives and reduce the spread of antibiotic resistance. This paper analyzes the ability of machine learning algorithms (MLAs) to process data from a novel spectroscopic diagnostic device to identify antibiotic-resistant genes and bacterial species by comparison to available bacterial DNA sequences. Simulation results show that the algorithms attain from 92% accuracy (for genes) up to 99% accuracy (for species). This novel approach identifies genes and species by optically reading the percentage of A, C, G, T bases in 1000s of short 10-base DNA oligomers instead of relying on conventional DNA sequencing in which the sequence of bases in long oligomers provides genetic information. The identification algorithms are robust in the presence of simulated random genetic mutations and simulated random experimental errors. Thus, these algorithms can be used to identify bacterial species, to reveal antibiotic resistance genes, and to perform other genomic analyses. Some MLAs evaluated here are shown to be better than others at accurate gene identification and avoidance of false negative identification of antibiotic resistance.202032153541
5827130.9929Duplex dPCR System for Rapid Identification of Gram-Negative Pathogens in the Blood of Patients with Bloodstream Infection: A Culture-Independent Approach. Early and accurate detection of pathogens is important to improve clinical outcomes of bloodstream infections (BSI), especially in the case of drug-resistant pathogens. In this study, we aimed to develop a culture-independent digital PCR (dPCR) system for multiplex detection of major sepsiscausing gram-negative pathogens and antimicrobial resistance genes using plasma DNA from BSI patients. Our duplex dPCR system successfully detected nine targets (five bacteria-specific targets and four antimicrobial resistance genes) through five reactions within 3 hours. The minimum detection limit was 50 ag of bacterial DNA, suggesting that 1 CFU/ml of bacteria in the blood can be detected. To validate the clinical applicability, cell-free DNA samples from febrile patients were tested with our system and confirmed high consistency with conventional blood culture. This system can support early identification of some drug-resistant gram-negative pathogens, which can help improving treatment outcomes of BSI.202134528911
4778140.9929DNA extraction of microbial DNA directly from infected tissue: an optimized protocol for use in nanopore sequencing. Identification of bacteria causing tissue infections can be comprehensive and, in the cases of non- or slow-growing bacteria, near impossible with conventional methods. Performing shotgun metagenomic sequencing on bacterial DNA extracted directly from the infected tissue may improve time to diagnosis and targeted treatment considerably. However, infected tissue consists mainly of human DNA (hDNA) which hampers bacterial identification. In this proof of concept study, we present a modified version of the Ultra-Deep Microbiome Prep kit for DNA extraction procedure, removing additional human DNA. Tissue biopsies from 3 patients with orthopedic implant-related infections containing varying degrees of Staphylococcus aureus were included. Subsequent DNA shotgun metagenomic sequencing using Oxford Nanopore Technologies' (ONT) MinION platform and ONTs EPI2ME WIMP and ARMA bioinformatic workflows for microbe and antibiotic resistance genes identification, respectively. The modified DNA extraction protocol led to an additional ~10-fold reduction of human DNA while preserving S. aureus DNA. Including the DNA sequencing and bioinformatics analyses, the presented protocol has the potential of identifying the infection-causing pathogen in infected tissue within 7 hours after biopsy. However, due to low number of S. aureus reads, positive identification of antibiotic resistance genes was not possible.202032076089
7130150.9928Microbial community structure and resistome dynamics on elevator buttons in response to surface disinfection practices. BACKGROUND: Disinfectants have been extensively used in public environments since the COVID-19 outbreak to help control the spread of the virus. This study aims to investigate whether disinfectant use influences the structure of bacterial communities and contributes to bacterial resistance to disinfectants and antibiotics. METHODS: Using molecular biology techniques-including metagenomic sequencing and quantitative PCR (qPCR)-we analyzed the bacterial communities on elevator button surfaces from two tertiary hospitals, one infectious disease hospital, two quarantine hotels (designated for COVID-19 control), and five general hotels in Nanjing, Jiangsu Province, during the COVID-19 pandemic. We focused on detecting disinfectant resistance genes (DRGs), antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs). RESULTS: Significant differences were observed in the bacterial community structures on elevator button surfaces across the four types of environments. Quarantine hotels, which implemented the most frequent disinfection protocols, exhibited distinct bacterial profiles at the phylum, genus, and species levels. Both α-diversity (within-sample diversity) and β-diversity (between-sample diversity) were lower and more distinct in quarantine hotels compared to the other environments. The abundance of DRGs, ARGs, and MGEs was also significantly higher on elevator button surfaces in quarantine hotels. Notably, antibiotic-resistant bacteria (ARBs), including Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa, were detected in all four settings. CONCLUSION: The structure of bacterial communities on elevator button surfaces varies across different environments, likely influenced by the frequency of disinfectant use. Increased resistance gene abundance in quarantine hotels suggests that disinfection practices may contribute to the selection and spread of resistant bacteria. Enhanced monitoring of disinfection effectiveness and refinement of protocols in high-risk environments such as hospitals and hotels are essential to limit the spread of resistant pathogens.202540520307
4939160.9928Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. OBJECTIVES: The introduction of metagenomic sequencing to diagnostic microbiology has been hampered by slowness, cost and complexity. We explored whether MinION nanopore sequencing could accelerate diagnosis and resistance profiling, using complicated urinary tract infections as an exemplar. METHODS: Bacterial DNA was enriched from clinical urines (n = 10) and from healthy urines 'spiked' with multiresistant Escherichia coli (n = 5), then sequenced by MinION. Sequences were analysed using external databases and bioinformatic pipelines or, ultimately, using integrated real-time analysis applications. Results were compared with Illumina data and resistance phenotypes. RESULTS: MinION correctly identified pathogens without culture and, among 55 acquired resistance genes detected in the cultivated bacteria by Illumina sequencing, 51 were found by MinION sequencing directly from the urines; with three of the four failures in an early run with low genome coverage. Resistance-conferring mutations and allelic variants were not reliably identified. CONCLUSIONS: MinION sequencing comprehensively identified pathogens and acquired resistance genes from urine in a timeframe similar to PCR (4 h from sample to result). Bioinformatic pipeline optimization is needed to better detect resistances conferred by point mutations. Metagenomic-sequencing-based diagnosis will enable clinicians to adjust antimicrobial therapy before the second dose of a typical (i.e. every 8 h) antibiotic.201727667325
5039170.9928Analytical validation of a novel high multiplexing real-time PCR array for the identification of key pathogens causative of bacterial ventilator-associated pneumonia and their associated resistance genes. OBJECTIVES: Rapid diagnosis and appropriate empirical antimicrobial therapy before the availability of conventional microbiological results is of pivotal importance for the clinical outcome of ventilator-associated pneumonia (VAP). We evaluated the VAPChip, a novel, closed cartridge molecular tool aiming to identify directly from clinical samples and within a working day the principal bacteria causative of VAP as well as clinically relevant β-lactam resistance genes. METHODS: The Real-time Array PCR for Infectious Diseases (RAP-ID) is a novel technology that combines multiplex PCR with real-time microarray detection. The VAPChip is a closed cartridge kit adapted to the RAP-ID instrument that targets 13 key respiratory pathogens causative of VAP and 24 relevant antimicrobial resistance genes that mediate resistance to β-lactam agents, including extended-spectrum cephalosporins and carbapenems. Analytical validation of the VAPChip was carried out blindly on a collection of 292 genotypically characterized bacterial reference and clinical isolates, including 225 isolates selected on the basis of their species identification and antimicrobial resistance profiles and 67 bacterial isolates belonging to the oropharyngeal flora not targeted by the array. RESULTS: The limit of detection of the assay lies between 10 and 100 genome copies/PCR and the dynamic range is five orders of magnitude permitting at least semi-quantitative reporting of the results. Sensitivity, specificity and negative and positive predictive values ranged from 95.8% to 100% for species identification and detection of resistance genes. CONCLUSIONS: VAPChip is a novel diagnostic tool able to identify resistant bacterial isolates by RAP-ID technology. The results of this analytical validation have to be confirmed on clinical specimens.201323065698
5796180.9928Antibiotic treatment algorithm development based on a microarray nucleic acid assay for rapid bacterial identification and resistance determination from positive blood cultures. Rapid diagnosis of bloodstream infections remains a challenge for the early targeting of an antibiotic therapy in sepsis patients. In recent studies, the reliability of the Nanosphere Verigene Gram-positive and Gram-negative blood culture (BC-GP and BC-GN) assays for the rapid identification of bacteria and resistance genes directly from positive BCs has been demonstrated. In this work, we have developed a model to define treatment recommendations by combining Verigene test results with knowledge on local antibiotic resistance patterns of bacterial pathogens. The data of 275 positive BCs were analyzed. Two hundred sixty-three isolates (95.6%) were included in the Verigene assay panels, and 257 isolates (93.5%) were correctly identified. The agreement of the detection of resistance genes with subsequent phenotypic susceptibility testing was 100%. The hospital antibiogram was used to develop a treatment algorithm on the basis of Verigene results that may contribute to a faster patient management.201626712265
5073190.9927Parallel Detection of the Unamplified Carbapenem Resistance Genes bla(NDM-1) and bla(OXA-1) Using a Plasmonic Nano-Biosensor with a Field-Portable DNA Extraction Method. Antimicrobial resistance (AMR) is a rapidly growing global concern resulting from the overuse of antibiotics in agricultural and clinical settings. The challenge is exacerbated by the lack of rapid surveillance for resistant bacteria in clinical, environmental, and food supply settings. The increasing resistance to carbapenems, an important sub-class of beta-lactam antibiotics, is a major concern in the healthcare community. Carbapenem resistance (CR) has been found in the environment and food supply chain, where it has the potential to spread to pathogens, animals, and humans through direct or indirect contact. Rapid detection for preventative and control measures should be developed. This study utilized a gold nanoparticle-based plasmonic biosensor for the parallel detection of the CR genes bla(NDM-1) and bla(OXA-1). To explore the field portability, DNA was extracted using two methods: a commercial extraction kit and a boiling method. The results were compared between the two methods using a spectrophotometer and a cellphone application for RGB values to quantify the visual results. The results showed that the boiling method of extraction was more effective than extraction with a commercial kit for this analysis. The parallel detection of unamplified genes extracted via the boiling method is novel. When combined with other portable testing equipment, the approach has the potential to be an inexpensive, rapid, and simple on-site CR gene detection protocol.202539997014