RUPPE, E. | bioRxiv Science

bioRxiv2020

Relationship between the composition of the digestive microbiota and the concentrations of opportunistic pathogens in intensive care patients.

ObjectiveInfections caused by multidrug-resistant Gram-negative bacilli (MDR-GNB) are a major issue in intensive care. The intestinal and oropharyngeal microbiota being the reservoir of MDR-GNB. Our main objective was to assess the link between the composition of the intestinal and tracheal microbiota and colonization by MDR-GNB. MethodsWe performed a 2-month prospective, monocentric cohort study in the medical intensive care unit of our hospital. Patients ventilated >3 days and spontaneously passing faeces were included. A faecal sample and an endotracheal aspiration (EA) were collected twice a week. MDR-GNB but also Enterococcus faecium and yeasts (as potential dysbiosis surrogate markers) were detected by culture methods. The composition of the intestinal and tracheal microbiota was assessed by 16S profiling. ResultsWe collected 62 couples of faeces and EA from 31 patients, including 18 faeces and 9 EA positive for MDR-GNB. We did not observe a link between the diversity and the richness of the intestinal microbiota and the MDR-GNB intestinal relative abundance (RA). Conversely, we observed a negative link between the intestinal diversity and richness and the RA of Enterococcus spp. (p<0.001). ConclusionThe intestinal MDR-GNB RA was not associated to the diversity nor the richness of the intestinal microbiota, but that of Enterococcus spp. was.

microbiology↗

bioRxiv2020

Clinical metagenomics bioinformatics pipeline for the identification of hospital-acquired pneumonia pathogens antibiotic resistance genes from bronchoalveolar lavage samples

BackgroundShortening 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. MethodsThe 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. ResultsIn 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). ConclusionsWe 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.

bioinformatics↗