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Wantia, N.

Publications and source records attributed to Wantia, N..

3 recordsLinked to original sources

Nanopore metagenomic sequencing links clinically relevant resistance determinants to pathogens

Culture-independent metagenomics enables the detection of plasmid-encoded antimicrobial resistance (AMR) genes directly from clinical samples; however, the clinical significance of these genes depends on their bacterial host and genomic context, which metagenomics cannot fully infer. Nanopore sequencing technology intrinsically encodes epigenetic modifications such as methylation, which can be leveraged for plasmid-host associations from metagenomic data. Existing methods rely on the recovery of metagenome-assembled genomes (MAGs), which can introduce bias toward abundant taxa and leave clinically relevant, low-abundance pathogens unassociated. To address this limitation, we extended methylation-based plasmid-host association from the MAG level to individual assembly contigs and sequencing reads. The CUPID pipeline implements the calculation of contig and read similarity scores, which compare weighted mean methylation rates across motifs genetically shared between any contig or read pair. We validated this approach on a mock metagenomic community composed of ten carbapenem-resistant Enterobacterales isolates, where we achieved 93.8% accuracy at the contig level and 100% at the read level for carbapenemase plasmid-host associations. When applied to metagenomic and quasimetagenomic data of sixteen patient rectal swabs collected during routine hospital surveillance, our approach assigned every detected plasmid-encoded carbapenemase to its correct bacterial host at the contig level, using matched culture-based diagnostics and whole-genome sequencing as a ground truth. Read-level analysis identified additional associations that were missed at the contig level, including a multi-host plasmid confirmed by established diagnostics. These findings demonstrate a pathway from rapid AMR gene detection using metagenomics to actionable surveillance for infection prevention, transmission tracing, and outbreak investigation. Impact statementCulture-independent metagenomics can detect antimicrobial resistance genes, but their clinical significance depends on the bacterial host and genomic context. Here, we show that nanopore-derived bacterial DNA methylation patterns can link carbapenemase genes to pathogenic hosts and plasmid context directly from patient samples. This provides a route from rapid antimicrobial resistance gene detection to actionable public health surveillance. Data summaryAll sequencing data after human content filtering have been deposited at the European Nucleotide Archive (ENA, BioProject accession PRJEB108076, with all isolate sequencing data for mock community generation available under the sample accession numbers SAMEA121375149-58, all isolate sequencing data from the rectal swabs available at SAMEA121334008-24, all metagenomic data from the rectal swabs available at SAMEA121325220-27, and all quasimetagenomic data available at SAMEA122914816-23, SAMEA122920068-74). All code is available at GitHub: https://github.com/harikaurel/cupid. All other supporting data are provided in the article and supplementary tables.

genomics↗

Resolving plasmid-encoded carbapenem resistance dynamics and reservoirs in a hospital setting through nanopore sequencing

The growing resistance of Enterobacterales to last-resort antibiotics such as carbapenems puts a significant burden on healthcare systems, also due to plasmids driving a rapid spread of carbapenem resistance. We here evaluate the use of long-read nanopore sequencing to investigate carbapenem resistance dynamics and the role of plasmid transfers and environmental reservoirs in the hospital setting. Over 13 months, routine clinical diagnostics identified recurring isolates of carbapenem-resistant Citrobacter species carrying Klebsiella pneumoniae carbapenemases (KPC) and/or OXA-48-like carbapenemases from patient screening and hospital drain samples. While routine diagnostic approaches provided limited insights into the carbapenem resistance dynamics, we show that near-complete de novo assembly of chromosomes and plasmids by long-read nanopore sequencing allowed for high-resolution strain identification, plasmid profiling, and antibiotic resistance gene detection. Notably, genomically nearly indistinguishable Citrobacter freundii of the high-risk sequence type ST91 genomes were recovered from screening samples collected in the same hospital room one year apart. We further provide evidence of a KPC-2 encoding IncN plasmid that is likely to have spread across bacterial species and between patient and drain isolates, which emphasizes the role of contaminated drains in the persistence and dissemination of resistances within the hospital environment. Overall, this study demonstrates the value of long-read nanopore sequencing for uncovering the complex dynamics of carbapenem resistance spread and persistence in the hospital setting, and its potential implications for Infection Prevention and Control. Impact statementThis study demonstrates how long-read nanopore sequencing can resolve the complex dynamics of plasmid-mediated antimicrobial resistance in clinical and environmental samples within the hospital setting. By linking patient- and drain-derived isolates through near-complete de novo assemblies, we reveal hidden reservoirs and dynamics behind the persistence of cabapenem resistance over extended time periods. This work shows how long-read sequencing approaches can uncover resistance dynamics that are missed using standard diagnostic methods, with implications for infection control and surveillance. Data SummaryThe study sequences are available at the National Center for Biotechnology Information (NCBI) under BioProject accession number PRJNA1297122. The raw sequence read data is available at NCBI sequence read archive (SRA (https://www.ncbi.nlm.nih.gov/sra)) under accession numbers SRR34727947-59. The chromosomal assemblies of all ST91 strains are available at NCBI GenBank under the Biosample accession numbers SAMN50449475-80. All other supporting data are provided in the article and supplementary data files.

microbiology↗

Early detection of ampicillin susceptibility in Enterococcus faecium with MALDI-TOF MS and machine learning

BackgroundEnterococcus faecium can cause severe infections and is often resistant to the first-line antibiotic ampicillin. Consequently, clinicians usually prescribe broad-spectrum antibiotics, promoting the selection of multidrug-resistant bacteria. In this study, we investigate the application of machine learning techniques to detect ampicillin susceptibility directly from MALDI-TOF mass spectrometry. This technique could enable an earlier optimised treatment in infections with ampicillin-susceptible E. faecium. MethodsTwo datasets of clinical E. faecium MALDI-TOF spectra and their resistance phenotype were analysed: our own Technical University of Munich (TUM) dataset and the publicly available MS-UMG dataset. We tested logistic regression (LR) and LightGBM models on each dataset via nested cross-validation and explored transferability on the respective other dataset. ResultsLightGBM demonstrated slightly better performance than LR in identifying susceptible isolates in the TUM dataset (area under the precision-recall curve (AUPRC) 0.907 {+/-} 0.016 vs 0.902 {+/-} 0.030) as well as in the MS-UMG dataset (AUPRC 0.902 {+/-} 0.029 vs 0.899 {+/-} 0.054). External validation demonstrated good model transferability (AUPRC of 0.784 {+/-} 0.039 when trained on MS-UMG; 0.804 {+/-} 0.013 when trained on TUM). SHAP analysis consistently identified a top-ranked spectral feature corresponding to a peak at an m/z of 5091 in resistant isolate spectra. ConclusionThis study demonstrates that LR and LightGBM models can identify ampicillin-susceptible E. faecium isolates from MALDI-TOF spectra and generalise well to unseen datasets. While clinical implementation currently still requires confirmatory testing, the addition of larger datasets in the future will support the development of more robust machine learning models.

microbiology↗