Comprehensive Pathogen Identification, Antibiotic Resistance, and Virulence Genes Prediction Directly From Simulated Blood Samples and Positive Blood Cultures by Nanopore Metagenomic Sequencing

Short turnaround time of seven to nine hours from sample collection until informed decision for sepsis treatment using nanopore sequencing

Bloodstream infections (BSIs) and sepsis are major health problems, annually claiming millions of lives. Traditional blood culture techniques, employed to identify sepsis-causing pathogens and assess antibiotic susceptibility, usually take 2-4 days. Early and accurate antibiotic prescription is vital in sepsis to mitigate mortality and antibiotic resistance. This study aimed to reduce the wait time for sepsis diagnosis by employing shorter blood culture incubation times for BD BACTEC™ bottles using standard laboratory incubators, followed by real-time nanopore sequencing and data analysis. The method was tested on nine blood samples spiked with clinical isolates from the six most prevalent sepsis-causing pathogens. The results showed that pathogen identification was possible at as low as 102-104 CFU/mL, achieved after just 2 h of incubation and within 40 min of nanopore sequencing. Moreover, all the antimicrobial resistance genes were identified at 103-107 CFU/mL, achieved after incubation for 5 h and only 10 min to 3 h of sequencing. Therefore, the total turnaround time from sample collection to the information required for an informed decision on the right antibiotic treatment was between 7 and 9 h. These results hold significant promise for better clinical management of sepsis compared with current culture-based methods.

Metagenomic identification of pathogens and antimicrobial-resistant genes in bacterial positive blood cultures by nanopore sequencing

Nanopore sequencing workflows have attracted increasing attention owing to their fast, real-time, and convenient portability. Positive blood culture samples were collected from patients with bacterial bloodstream infection and tested by nanopore sequencing. This study compared the sequencing results for pathogen taxonomic profiling and antimicrobial resistance genes to those of species identification and phenotypic drug susceptibility using traditional microbiology testing. A total of 37 bacterial positive blood culture results of strain genotyping by nanopore sequencing were consistent with those of mass spectrometry. Among them, one mixed infection of bacteria and fungi was identified using nanopore sequencing and confirmatory quantitative polymerase chain reaction. The amount of sequencing data was 21.89 ± 8.46 MB for species identification, and 1.0 MB microbial strain data enabled accurate determination. Data volumes greater than or equal to 94.6 MB nearly covered all the antimicrobial resistance genes of the bacteria in our study. In addition, the results of the antimicrobial resistance genes were compared with those of phenotypic drug susceptibility testing for Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. Therefore, the nanopore sequencing platform for rapid identification of causing pathogens and relevant antimicrobial resistance genes complementary to conventional blood culture outcomes may optimize antimicrobial stewardship management for patients with bacterial bloodstream infection.

Integration of phenotypic, qPCR and genome sequencing methodologies for the detection of antimicrobial resistance and virulence in clinical isolates of a tertiary hospital, India

The emergence of antimicrobial resistance (AMR) and virulence in clinical isolates is a significant public health concern. The rapid and accurate detection of these traits in clinical isolates is essential for effective infection control and treatment. We demonstrated the integration of multiple detection methodologies, including phenotypic testing, quantitative polymerase chain reaction (qPCR), and genome sequencing, to detect AMR and virulence in clinical isolates. One hundred sixty-two gram-negative bacterial clinical isolates were selected for this study from the Shri Vinoba Bhave Civil Hospital, Silvassa, a tertiary government hospital. Antimicrobial susceptibility was detected by determining the Minimum Inhibitory Concentration (MIC) using Vitek-2, whereas the combined disk (CD) method was used for phenotypic detection of carbapenemase activity. The highest sensitivity rates were obtained for antibiotics colistin 87.93%, amikacin 67.52%, tigecycline 63.39%, nitrofurantoin 60.87%, and gentamycin 56.08%. The most resistant antibiotics were ceftazidime (71.93%), ciprofloxacin (67.95%) and trimethoprim/sulfamethoxazole (65.56%). Approximately 46.91% (76) of all the isolates were MBL isolates. The qPCR results confirmed the presence of blaNDM-1 in 29.01% of the isolates. The blaNDM-1 harbouring isolates in descending order, were Acinetobacter, Enterobacter cloacae, and Klebsiella pneumoniae. Klebsiella and Acinetobacter isolates were extensively drug-resistant. Whole genome sequencing performed on one of the Klebsiella pneumoniae isolates revealed the presence of many virulence factors, which increased the pathogenicity of the clinical isolates. The results showed that antimicrobial resistance, including carbapenem resistance, blaNDM-1, and virulence factors, was highly prevalent among isolates from tertiary clinical hospitals. The integration of multiple detection methodologies can potentially improve the detection of AMR and virulence in clinical isolates, leading to better patient outcomes and a reduced spread of these essential traits.

Multi-Clinical Factors Combined with an Artificial Intelligence Algorithm Diagnosis Model for HIV-Infected People with Bloodstream Infection

Purpose

Although highly active antiretroviral therapy (HA-ART) can effectively suppress the disease process in patients with acquired immunodeficiency syndrome (AIDS), opportunistic infections, mainly bloodstream infections (BSI), are still the main cause of death in people living with HIV. There is no effective diagnostic strategy for HIV-infected people with BSI. This study aimed to develop an AI diagnostic model with high sensitivity to improve the early detection of HIV-infected people with BSI.

Patients and Methods

This study retrospectively analyzed the 40 clinical factors of 498 HIV-infected people (171 with BSI positive and 327 with BSI negative) who admitted to Wenzhou Central Hospital from September 2014 to July 2021. This study used the hospital information management system to collect the clinical characteristics, laboratory and imaging examination results, and clinical diagnosis of the two groups. The diagnostic results of all patients were in line with the diagnostic criteria of the Chinese Guidelines for the Diagnosis and Treatment of AIDS (2021 Edition), and the BSI diagnosis was in line with the diagnostic criteria of sepsis and bacteremia in Practical Internal Medicine (13th Edition). On this basis, various risk prediction models were established by combining 8 artificial intelligence (AI) algorithms in the training set and validating the diagnosis performance in the testing set. The model with the best diagnostic performance was selected as the final diagnostic model.

Results

The clinical characteristics of HIV-infected people with BSI are atypical, and the pathogens in this area are mainly fungi. Ten risk factors were selected: low level of hemoglobin, CD4+T cell and platelets, high level of lactate dehydrogenase and blood urea nitrogen, splenomegaly, without ART treatment, strip shadow, nodular shadow, and shock. The combination of the ten risk factors, age, gender and the "svmRadial" model can identify the HIV-infected people with BSI from the HIV-infected people without BSI with an area under the curve of 0.916 and a sensitivity and specificity of 0.824 and 0.855, respectively.

Conclusion

The model showed excellent performance in diagnosing HIV-infected people with BSI. Internal and external validation showed that the diagnosis model had high clinical application value.

Improved targeting of the 16S rDNA nanopore sequencing method enables rapid pathogen identification in bacterial pneumonia in children

Objectives

To develop a rapid and low-cost method for 16S rDNA nanopore sequencing.

Methods

This was a prospective study on a 16S rDNA nanopore sequencing method. We developed this nanopore barcoding 16S sequencing method by adding barcodes to the 16S primer to reduce the reagent cost and simplify the experimental procedure. Twenty-one common pulmonary bacteria (7 reference strains, 14 clinical isolates) and 94 samples of bronchoalveolar lavage fluid from children with severe pneumonia were tested. Results indicating low-abundance pathogenic bacteria were verified with the polymerase chain reaction (PCR). Further, the results were compared with those of culture or PCR.

Results

The turnaround time was shortened to 6~8 hours and the reagent cost of DNA preparation was reduced by employing a single reaction adding barcodes to the 16S primer in advance. The accuracy rate for the 21 common pulmonary pathogens with an abundance ≥ 99% was 100%. Applying the culture or PCR results as the gold standard, 71 (75.5%) of the 94 patients were positive, including 25 positive cultures (26.6%) and 52 positive quantitative PCRs (55.3%). The median abundance in the positive culture and qPCR samples were 29.9% and 6.7%, respectively. With an abundance threshold increase of 1%, 5%, 10%, 15% and 20%, the test sensitivity decreased gradually to 98.6%, 84.9%, 72.6%, 67.1% and 64.4%, respectively, and the test specificity increased gradually to 33.3%, 71.4%, 81.0%, 90.5% and 100.0%, respectively.

Conclusions

The nanopore barcoding 16S sequencing method can rapidly identify the pathogens causing bacterial pneumonia in children.

Optimized Method for Bacterial Nucleic Acid Extraction from Positive Blood Culture Broth for Whole-Genome Sequencing, Resistance Phenotype Prediction, and Downstream Molecular Applications

The application of direct metagenomic sequencing from positive blood culture broth may solve the challenges of sequencing from low-bacterial-load blood samples in patients with sepsis. Forty prospectively collected blood culture broth samples growing Gram-negative bacteria were extracted using commercially available kits to achieve high-quality DNA.

Evaluating the potential of third generation metagenomic sequencing for the detection of BRD pathogens and genetic determinants of antimicrobial resistance in chronically ill feedlot cattle

Background

Bovine respiratory disease (BRD) is an important cause of morbidity and mortality and is responsible for most of the injectable antimicrobial use in the feedlot industry. Traditional bacterial culture can be used to diagnose BRD by confirming the presence of causative pathogens and to support antimicrobial selection. However, given that bacterial culture takes up to a week and early intervention is critical for treatment success, culture has limited utility for informing rapid therapeutic decision-making. In contrast, metagenomic sequencing has the potential to quickly resolve all nucleic acid in a sample, including pathogen biomarkers and antimicrobial resistance genes. In particular, third-generation Oxford Nanopore Technology sequencing platforms provide long reads and access to raw sequencing data in real-time as it is produced, thereby reducing the time from sample collection to diagnostic answer. The purpose of this study was to compare the performance of nanopore metagenomic sequencing to traditional culture and sensitivity methods as applied to nasopharyngeal samples from segregated groups of chronically ill feedlot cattle, previously treated with antimicrobials for nonresponsive pneumonia or lameness.

Results

BRD pathogens were isolated from most samples and a variety of different resistance profiles were observed across isolates. The sequencing data indicated the samples were dominated by Moraxella bovoculi, Mannheimia haemolytica, Mycoplasma dispar, and Pasteurella multocida, and included a wide range of antimicrobial resistance genes (ARGs), encoding resistance for up to seven classes of antimicrobials. Genes conferring resistance to beta-lactams were the most commonly detected, while the tetH gene was detected in the most samples overall. Metagenomic sequencing detected the BRD pathogens of interest more often than did culture, but there was limited concordance between phenotypic resistance to antimicrobials and the presence of relevant ARGs.

Conclusions

Metagenomic sequencing can reduce the time from sampling to results, detect pathogens missed by bacterial culture, and identify genetically encoded determinants of resistance. Increasing sequencing coverage of target organisms will be an essential component of improving the reliability of this technology, such that it can be better used for the surveillance of pathogens of interest, genetic determinants of resistance, and to inform diagnostic decisions.

Widespread of Potential Pathogen-Derived Extracellular Vesicles Carrying Antibiotic Resistance Genes in Indoor Dust

Extracellular vesicles (EVs) are newly recognized as important vectors for carrying and spreading antibiotic resistance genes (ARGs). However, the ARGs harbored by EVs in ambient environments and the transfer potential are still unclear. In this study, the prevalence of ARGs and mobile genetic elements (MGEs) in EVs and their microbial origins were studied in indoor dust from restaurants, kindergarten, dormitories, and vehicles. The amount of EVs ranged from 3.40 × 10⁷ to 1.09 × 10¹¹ particles/g dust. The length of EV-associated DNA fragments was between 21 bp and 9.7 kb. Metagenomic sequencing showed that a total of 241 antibiotic ARG subtypes encoding resistance to 16 common classes were detected in the EVs from all four fields. Multidrug, quinolone, and macrolide resistance genes were the dominant types. 15 ARG subtypes were exclusively carried and even enriched in EVs compared to the indoor microbiome. Moreover, several ARGs showed co-occurrence with MGEs. The EVs showed distinct taxonomic composition with their original dust microbiota. 30.23% of EV-associated DNA was predicted to originate from potential pathogens. Our results indicated the widespread of EVs carrying ARGs and virulence genes in daily life indoor dust, provided new insights into the status of extracellular DNA, and raised risk concerns on their gene transfer potential.

Application and Challenge of 3rd Generation Sequencing for Clinical Bacterial Studies

Over the past 25 years, the powerful combination of genome sequencing and bioinformatics analysis has played a crucial role in interpreting information encoded in bacterial genomes. High-throughput sequencing technologies have paved the way towards understanding an increasingly wide range of biological questions. This revolution has enabled advances in areas ranging from genome composition to how proteins interact with nucleic acids. This has created unprecedented opportunities through the integration of genomic data into clinics for the diagnosis of genetic traits associated with disease. Since then, these technologies have continued to evolve, and recently, long-read sequencing has overcome previous limitations in terms of accuracy, thus expanding its applications in genomics, transcriptomics and metagenomics. In this review, we describe a brief history of the bacterial genome sequencing revolution and its application in public health and molecular epidemiology. We present a chronology that encompasses the various technological developments: whole-genome shotgun sequencing, high-throughput sequencing, long-read sequencing. We mainly discuss the application of next-generation sequencing to decipher bacterial genomes. Secondly, we highlight how long-read sequencing technologies go beyond the limitations of traditional short-read sequencing. We intend to provide a description of the guiding principles of the 3rd generation sequencing applications and ongoing improvements in the field of microbial medical research.

OUP accepted manuscript

MOTIVATION

Nanopore sequencers allow targeted sequencing of interesting nucleotide sequences by rejecting other sequences from individual pores. This feature facilitates the enrichment of low-abundant sequences by depleting overrepresented ones in-silico. Existing tools for adaptive sampling either apply signal alignment, which cannot handle human-sized reference sequences, or apply read mapping in sequence space relying on fast graphical processing units (GPU) base callers for real-time read rejection. Using nanopore long-read mapping tools is also not optimal when mapping shorter reads as usually analyzed in adaptive sampling applications.

RESULTS

Here, we present a new approach for nanopore adaptive sampling that combines fast CPU and GPU base calling with read classification based on Interleaved Bloom Filters. ReadBouncer improves the potential enrichment of low abundance sequences by its high read classification sensitivity and specificity, outperforming existing tools in the field. It robustly removes even reads belonging to large reference sequences while running on commodity hardware without GPUs, making adaptive sampling accessible for in-field researchers. Readbouncer also provides a user-friendly interface and installer files for end-users without a bioinformatics background.

AVAILABILITY AND IMPLEMENTATION

The C++ source code is available at https://gitlab.com/dacs-hpi/readbouncer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Shotgun-metagenomics based prediction of antibiotic resistance and virulence determinants in Staphylococcus aureus from periprosthetic tissue on blood culture bottles

Shotgun-metagenomics may give valuable clinical information beyond the detection of potential pathogen(s). Identification of antimicrobial resistance (AMR), virulence genes and typing directly from clinical samples has been limited due to challenges arising from incomplete genome coverage. We assessed the performance of shotgun-metagenomics on positive blood culture bottles (n = 19) with periprosthetic tissue for typing and prediction of AMR and virulence profiles in Staphylococcus aureus. We used different approaches to determine if sequence data from reads provides more information than from assembled contigs. Only 0.18% of total reads was derived from human DNA. Shotgun-metagenomics results and conventional method results were consistent in detecting S. aureus in all samples. AMR and known periprosthetic joint infection virulence genes were predicted from S. aureus. Mean coverage depth, when predicting AMR genes was 209 ×. Resistance phenotypes could be explained by genes predicted in the sample in most of the cases. The choice of bioinformatic data analysis approach clearly influenced the results, i.e. read-based analysis was more accurate for pathogen identification, while contigs seemed better for AMR profiling. Our study demonstrates high genome coverage and potential for typing and prediction of AMR and virulence profiles in S. aureus from shotgun-metagenomics data.