Clinical Metagenomic Sequencing for Species Identification and Antimicrobial Resistance Prediction in Orthopedic Device Infection

Street, Teresa L; Sanderson, Nicholas; Kolenda, Camille; Kavanagh, James; Pickford, Hayleah; Hoosdally, Sarah; Cregan, Jack; Taunt, Carol; Jones, Emma; Oakley, Sarah; Atkins, Bridget L.; Dudareva, Maria; McNally, Martin; O’Grady, Justin; Crook, Derrick W.; Eyre, David W

doi:10.1128/jcm.02156-21

Cited by 24 publications

(9 citation statements)

References 37 publications

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“…Illumina reads were simulated using ART_Illumina v2.3.7 [ 19 ] (art_illumina -i reference.fna -p -l 250 f 20 m 999 s 1 -o -nf 5 -na -ss MS -na) generating reads at a 20× coverage for each reference genome (Fig. S5) (available in the online version of this article), to simulate a realistic MiSeq throughput, which is around the minimum depth previously shown to be adequate for variant identification and antimicrobial resistance prediction [ 20 ]. Oxford Nanopore reads were simulated using NanoSim-H v1.1.0.4 [ 21, 22 ] (nanosim-h reference.fna -p ecoli_R9_1D -o -n 11000), generating reads at approximately a 20× coverage per genome with a median read length of 5921 bp (IQR 2745–11709) [range 125–120218] (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…fna -p -l 250 f 20 m 999 s 1 -o -nf 5 -na -ss MS -na) generating reads at a 20× coverage for each reference genome (Fig. S5) (available in the online version of this article), to simulate a realistic MiSeq throughput, which is around the minimum depth previously shown to be adequate for variant identification and antimicrobial resistance prediction [20]. Oxford Nanopore reads were simulated using NanoSim-H v1.1.0.4 [21,22] (nanosim-h reference.…”

Section: Impact Statementmentioning

confidence: 99%

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Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications

Govender

Eyre

2022

Microbial Genomics

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Culture-independent metagenomic detection of microbial species has the potential to provide rapid and precise real-time diagnostic results. However, it is potentially limited by sequencing and taxonomic classification errors. We use simulated and real-world data to benchmark rates of species misclassification using 100 reference genomes for each of the ten common bloodstream pathogens and six frequent blood-culture contaminants (n=1568, only 68 genomes were available for Micrococcus luteus ). Simulating both with and without sequencing error for both the Illumina and Oxford Nanopore platforms, we evaluated commonly used classification tools including Kraken2, Bracken and Centrifuge, utilizing mini (8 GB) and standard (30–50 GB) databases. Bracken with the standard database performed best, the median percentage of reads across both sequencing platforms identified correctly to the species level was 97.8% (IQR 92.7:99.0) [range 5:100]. For Kraken2 with a mini database, a commonly used combination, median species-level identification was 86.4% (IQR 50.5:93.7) [range 4.3:100]. Classification performance varied by species, with Escherichia coli being more challenging to classify correctly (probability of reads being assigned to the correct species: 56.1–96.0%, varying by tool used). Human read misclassification was negligible. By filtering out shorter Nanopore reads we found performance similar or superior to Illumina sequencing, despite higher sequencing error rates. Misclassification was more common when the misclassified species had a higher average nucleotide identity to the true species. Our findings highlight taxonomic misclassification of sequencing data occurs and varies by sequencing and analysis workflow. To account for ‘bioinformatic contamination’ we present a contamination catalogue that can be used in metagenomic pipelines to ensure accurate results that can support clinical decision making.

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Section: Methodsmentioning

confidence: 99%

Section: Impact Statementmentioning

confidence: 99%

Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications

Govender

Eyre

2022

Microbial Genomics

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“…Detection of AMR determinants in the S. aureus positive clinical sample was conducted by sequencing them in Oxford Nanopore, a high throughput sequencing model. The reads were analyzed using ML‐based Clair, which implements an RF model classifier [96]. In a study involving NGS‐based species prevalence, an effective screening approach was developed to assess the risk of microbial pathogens in the sputum of individuals with pulmonary infections.…”

Section: Incorporating Ngs and ML For Quick Identification Of S Aureu...mentioning

confidence: 99%

Emerging strategies and therapeutic innovations for combating drug resistance in Staphylococcus aureus strains: A comprehensive review

Gopikrishnan,

Haryini,

2024

J Basic Microbiol

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In recent years, antibiotic therapy has encountered significant challenges due to the rapid emergence of multidrug resistance among bacteria responsible for life‐threatening illnesses, creating uncertainty about the future management of infectious diseases. The escalation of antimicrobial resistance in the post‐COVID era compared to the pre‐COVID era has raised global concern. The prevalence of nosocomial‐related infections, especially outbreaks of drug‐resistant strains of Staphylococcus aureus, have been reported worldwide, with India being a notable hotspot for such occurrences. Various virulence factors and mutations characterize nosocomial infections involving S. aureus. The lack of proper alternative treatments leading to increased drug resistance emphasizes the need to investigate and examine recent research to combat future pandemics. In the current genomics era, the application of advanced technologies such as next‐generation sequencing (NGS), machine learning (ML), and quantum computing (QC) for genomic analysis and resistance prediction has significantly increased the pace of diagnosing drug‐resistant pathogens and insights into genetic intricacies. Despite prompt diagnosis, the elimination of drug‐resistant infections remains unattainable in the absence of effective alternative therapies. Researchers are exploring various alternative therapeutic approaches, including phage therapy, antimicrobial peptides, photodynamic therapy, vaccines, host‐directed therapies, and more. The proposed review mainly focuses on the resistance journey of S. aureus over the past decade, detailing its resistance mechanisms, prevalence in the subcontinent, innovations in rapid diagnosis of the drug‐resistant strains, including the applicants of NGS and ML application along with QC, it helps to design alternative novel therapeutics approaches against S. aureus infection.

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“…Pathogen detection based on metagenomic sequencing can achieve unbiased identification of pathogens in clinical samples. Compared with traditional next-generation sequencing (NGS), nanopore sequencing has the advantages of real-time, long read length, portability, etc., and has been widely used in pathogen detection of clinical infectious diseases ( Gu et al., 2021 ; Jabeen et al., 2022 ; Street et al., 2022 ). Limited by the high host background in the clinical samples and low data yield of nanopore sequencing, it is critical to achieve the enrichment of target pathogens for nanopore metagenomic sequencing.…”

Section: Introductionmentioning

confidence: 99%

Application of nanopore adaptive sequencing in pathogen detection of a patient with Chlamydia psittaci infection

Lin

Dai

Zhang

et al. 2023

Front. Cell. Infect. Microbiol.

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IntroductionNanopore sequencing has been widely used in clinical metagenomic sequencing for pathogen detection with high portability and real-time sequencing. Oxford Nanopore Technologies has recently launched an adaptive sequencing function, which can enrich on-target reads through real-time alignment and eject uninteresting reads by reversing the voltage across the nanopore. Here we evaluated the utility of adaptive sequencing in clinical pathogen detection.MethodsNanopore adaptive sequencing and standard sequencing was performed on a same flow cell with a bronchoalveolar lavage fluid sample from a patient with Chlamydia psittacosis infection, and was compared with the previous mNGS results.ResultsNanopore adaptive sequencing identified 648 on-target stop receiving reads with the longest median read length(688bp), which account for 72.4% of all Chlamydia psittaci reads and 0.03% of total reads in enriched group. The read proportion matched to C. psittaci in the stop receiving group was 99.85%, which was much higher than that of the unblock (<0.01%) and fail to adapt (0.02%) groups. Nanopore adaptive sequencing generated similar data yield of C. psittaci compared with standard nanopore sequencing. The proportion of C. psittaci reads in adaptive sequencing is close to that of standard nanopore sequencing and mNGS, but generated lower genome coverage than mNGS.DiscussionNanopore adaptive sequencing can effectively identify target C. psittaci reads in real-time, but how to increase the targeted data of pathogens still needs to be further evaluated.

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Clinical Metagenomic Sequencing for Species Identification and Antimicrobial Resistance Prediction in Orthopedic Device Infection

Cited by 24 publications

References 37 publications

Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications

Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications

Emerging strategies and therapeutic innovations for combating drug resistance in Staphylococcus aureus strains: A comprehensive review

Application of nanopore adaptive sequencing in pathogen detection of a patient with Chlamydia psittaci infection

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