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.

Diagnostic value of a nanopore sequencing assay of bronchoalveolar lavage fluid in smear- negative pulmonary tuberculosis

“…For example, Huang et al employed a sequence-based assay to successfully detect human pathogens in 94.49% of samples from patients with pulmonary infections who had tested negative for pathogens using traditional pathogen detection methods. Their results thus demonstrated that sequence-based assay accuracy and sensitivity exceeded corresponding standard pathogen detection assay performance indicators [23]. Similarly, Wang et al found that their sequence-based assay was more sensitive than traditional methods for detection of mixed pulmonary infections [24], whereas Chen et al obtained sequence-based test results for BALF specimens collected from patients with severe PTB disease that closely aligned with standard culture method-based results [25].…”

“…Second, since our research focused on BALF specimens, the collection of lavage specimens containing numbers of pathogenic bacteria below the detection level of the test may have resulted in false-negative results as a reminder that the nanopore sequencing assay is still an auxiliary tool that should be used in combination with clinical features, radiological imaging ndings, and laboratory test results to diagnose suspected PTB cases. Third, we could not rule out that false-negative and false-positive nanopore sequencing assay results occurred due to (1) a sequence depth that was too low; (2) high host genome background noise and low microbial pathogen biomass; (3) administration of antibiotics to patients prior to testing; and (4) contamination of samples with genomes of environmental microbes or human flora [23]. Fourth, the absence of certain control diseases, such as lymphomas and rheumatoid arthritis (rare diseases in patients of Beijing Chest Hospital), may have biased our results.…”

Combined host depletion and metagenomics sequencing significantly improves the diagnosis of pulmonary tuberculosis.

“…Although, mNGS provided a new strategy to predict resistance genes for inferring antimicrobial susceptibility 28 . Due to the low abundance of pathogen AMRs in respirators or body uids which hampered the AMRs detection by using mNGS 29 and predicted AMRs still remained more limited 25,30−33 . Our data also showed mNGS to detect AMRs was very low for limited coverage and sequencing depth of MTB, which was consistent with previous study 15 .…”

Respiratory metagenomics: route to routine service

“…As clinical and academic teams came together through the unifying pandemic imperative [10], the translational research timeline for pathogen sequencing shortened. Diagnostic accuracy studies are beginning to demonstrate satisfactory performance with pathogen detection [11 ▪ ,12 ▪ ] and there are innovative approaches to detecting AMR elements using nanopore sequencing with cas9 enrichment [13 ▪ ] or predicting AMR using machine learning [14]. There is also increasing focus on clinical laboratory quality requirements including validation, reproducibility, setting limits of detection, and producing positive and negative internal controls and externally distributed reference materials for quality assurance [15].…”