Metagenomic prediction of antimicrobial resistance in critically ill patients with lower respiratory tract infections

Abstract

Background: Antimicrobial resistance (AMR) is rising at an alarming rate and complicating the management of infectious diseases including lower respiratory tract infections (LRTI). Metagenomic next-generation sequencing (mNGS) is a recently established method for culture-independent LRTI diagnosis, but its utility for predicting AMR has remained unclear. We aimed to assess the performance of mNGS for AMR prediction in bacterial LRTI and demonstrate proof of concept for epidemiological AMR surveillance and rapid AMR gene detection using Cas9 enrichment and nanopore sequencing.

Methods: We studied 88 patients with acute respiratory failure between 07/2013 and 9/2018, enrolled through a previous observational study of LRTI. Inclusion criteria were age ≥ 18, need for mechanical ventilation, and respiratory specimen collection within 72 h of intubation. Exclusion criteria were decline of study participation, unclear LRTI status, or no matched RNA and DNA mNGS data from a respiratory specimen. Patients with LRTI were identified by clinical adjudication. mNGS was performed on lower respiratory tract specimens. The primary outcome was mNGS performance for predicting phenotypic antimicrobial susceptibility and was assessed in patients with LRTI from culture-confirmed bacterial pathogens with clinical antimicrobial susceptibility testing (n = 27 patients, n = 32 pathogens). Secondary outcomes included the association between hospital exposure and AMR gene burden in the respiratory microbiome (n = 88 patients), and AMR gene detection using Cas9 targeted enrichment and nanopore sequencing (n = 10 patients).

Results: Compared to clinical antimicrobial susceptibility testing, the performance of respiratory mNGS for predicting AMR varied by pathogen, antimicrobial, and nucleic acid type sequenced. For gram-positive bacteria, a combination of RNA + DNA mNGS achieved a sensitivity of 70% (95% confidence interval (CI) 47-87%) and specificity of 95% (CI 85-99%). For gram-negative bacteria, sensitivity was 100% (CI 87-100%) and specificity 64% (CI 48-78%). Patients with hospital-onset LRTI had a greater AMR gene burden in their respiratory microbiome versus those with community-onset LRTI (p = 0.00030), or those without LRTI (p = 0.0024). We found that Cas9 targeted sequencing could enrich for low abundance AMR genes by > 2500-fold and enabled their rapid detection using a nanopore platform.

Conclusions: mNGS has utility for the detection and surveillance of resistant bacterial LRTI pathogens.

Fig. 1

Study overview and analysis workflow. A Enrollment flow diagram for the critically ill adult cohort with acute respiratory illnesses that was studied. B Metagenomic next-generation sequencing (mNGS) approach and analysis workflow. The primary analysis assessed the performance of metagenomic next-generation sequencing (mNGS) antimicrobial resistance (AMR) prediction in 27 subjects with LRTI due to 32 culture-confirmed bacterial pathogens. Secondary analyses included mNGS epidemiological assessment of hospital exposure and AMR gene burden in the airway microbiome, and proof of concept assessment of CRISPR/Cas9 targeted mNGS using Illumina and real-time nanopore sequencing

Fig. 2

A AMR genes detected in the lower respiratory microbiome of critically ill patients. Composite results of DNA and RNA mNGS. AMR genes are listed in rows and are grouped by antimicrobial class. Each column represents a patient respiratory sample and is grouped by LRTI status. B AMR gene burden in the respiratory tract, measured by averaging sequencing depth across the AMR allele per million reads sequenced (dpM) in the respiratory microbiome did not differ between LRTI-positive patients and those with non-infectious acute respiratory illnesses. C The burden of AMR genes detected in the lower respiratory tract microbiome was greater in patients with hospital-onset LRTI versus those with either community-onset LRTI or no evidence of LRTI. Legend: depth = average sequencing depth across each AMR gene allele normalized per million reads sequenced. Legend: Bla = beta lactam; AGly = aminoglycoside; Fos = Fosfomycin; Flq = fluoroquinolone; Gly = glycopeptide; Mac/Lin/Str = macrolide, lincosamide, streptogramin; Phe = phenicol; Tet = tetracycline; Tmp-Sul = trimethoprim/sulfamethoxazole; depth = average sequencing depth across each AMR gene allele normalized per million reads sequenced. The horizontal bars in panels B and C indicate mean values

Fig. 3

A FLASH (Finding Low Abundance Sequences by Hybridization) CRISPR/Cas9 targeted Illumina sequencing enriched the detection of culture-confirmed bacterial LRTI pathogen AMR alleles by 46 × to > 2500 × versus DNA-seq alone. B Workflow diagram for FLASH targeted enrichment coupled with nanopore sequencing. Time estimates provided for a single sample. C Real-time detection of AMR genes by FLASH targeted nanopore sequencing was achieved within 10 min following mNGS library preparation. Data from two representative Staphylococcus aureus LRTI cases are highlighted. Case 212 (left panel) highlights a case where detection of BlaZ and MsrA/ErmA genes correlated with phenotypically determined penicillin and macrolide/lincosamide resistance, respectively. Case 288 (right panel) highlights a case where detection of MecA, BlaZ, and MsrA correlated with phenotypically confirmed methicillin, penicillin, and macrolide resistance, respectively