Background: Shortening the time-to-result for pathogen detection and identification and antibiotic susceptibility testing for patients with Hospital-Acquired and Ventilator-Associated pneumonia (HAP-VAP) is of great interest. For this purpose, clinical metagenomics is a promising non-hypothesis driven alternative to traditional culture-based solutions: when mature, it would allow direct sequencing all microbial genomes present in a BronchoAlveolar Lavage (BAL) sample with the purpose of simultaneously identifying pathogens and Antibiotic Resistance Genes (ARG). In this study, we describe a new bioinformatics method to detect pathogens and their ARG with good accuracy, both in mono-and polymicrobial samples.Methods: The standard approach (hereafter called TBo), that consists in taxonomic binning of metagenomic reads followed by an assembly step, suffers from lack of sensitivity for ARG detection. Thus, we propose a new bioinformatics approach (called TBwDM) with both models and databases optimized for HAP-VAP, that performs reads mapping against ARG reference database in parallel to taxonomic binning, and joint reads assembly.Results: In in-silico simulated monomicrobial samples, the recall for ARG detection increased from 51% with TBo to 97.3% with TBwDM ; in simulated polymicrobial infections, it increased from 41.8% to 82%. In real sequenced BAL samples (mono and polymicrobial), detected pathogens were also confirmed by traditional culture approaches. Moreover, both recall and precision for ARG detection were higher with TBwDM than with TBo (35 points difference for recall, and 7 points difference for precision).Conclusions: We present a new bioinformatics pipeline to identify pathogens and ARG in BAL samples from patients with HAP-VAP, with higher sensitivity for ARG recovery than standard approaches and the ability to link ARG to their host pathogens.
Background The management of ventilator-associated and hospital-acquired pneumonia requires rapid and accurate quantitative detection of the infecting pathogen(s). To do it, we propose a metagenomics next-generation sequencing (mNGS) assay that includes an internal sample processing control (SPC) for the quantitative detection of 20 relevant bacterial species of interest (SOI) from bronchoalveolar lavage (BAL) samples. Results To avoid very major errors in identification of respiratory pathogens due to “false negative” cases, each sample was spiked with Bacillus subtilis, at a precisely defined concentration, using rehydrated BioBall®. This SPC ensured detection and quantification of the pathogen(s) at defined minimum concentrations. In the presented mNGS workflow, absolute quantification of Staphylococcus aureus was as accurate as quantitative PCR. We defined a metagenomics threshold at 5.3E+3 genome equivalent unit per milliliter of the sample for each SOI, to distinguish colonization from higher amounts of pathogens that may be associated with infection. Complete mNGS process and metrics were assessed on 40 clinical samples, showing 100 % sensitivity compared to microbial culture. However, 19 out of the 29 (66 %) SOI detections above the metagenomics threshold were not associated with bacterial growth above classical culture-based clinical thresholds. Taxonomic classification of 7 (37 %) of these “false positive” detections were confirmed by finding specific 16S/MetaPhlAn2 markers, the 12 other (63 %) “false positive” detections did not yield enough reads to check their taxonomic classification. Conclusions Our SPC design and analytical workflow allowed efficient detection and absolute quantification of pathogens from BAL samples, even when the bacterial DNA quantity was largely below manufacturer’s recommendations for NGS. The frequent "false positive" detection suggested the presence of non culturable cells within the tested BAL samples. Finally, mNGS detected mixed infections including bacterial species that were not reported by routine cultures.
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