Evaluation of Metagenomic and Targeted Next-Generation Sequencing Workflows for Detection of Respiratory Pathogens from Bronchoalveolar Lavage Fluid Specimens

Abstract

Next-generation sequencing (NGS) workflows applied to bronchoalveolar lavage (BAL) fluid specimens could enhance the detection of respiratory pathogens, although optimal approaches are not defined. This study evaluated the performance of the Respiratory Pathogen ID/AMR (RPIP) kit (Illumina, Inc.) with automated Explify bioinformatic analysis (IDbyDNA, Inc.), a targeted NGS workflow enriching specific pathogen sequences and antimicrobial resistance (AMR) markers, and a complementary untargeted metagenomic workflow with in-house bioinformatic analysis. Compared to a composite clinical standard consisting of provider-ordered microbiology testing, chart review, and orthogonal testing, both workflows demonstrated similar performances. The overall agreement for the RPIP targeted workflow was 65.6% (95% confidence interval, 59.2 to 71.5%), with a positive percent agreement (PPA) of 45.9% (36.8 to 55.2%) and a negative percent agreement (NPA) of 85.7% (78.1 to 91.5%). The overall accuracy for the metagenomic workflow was 67.1% (60.9 to 72.9%), with a PPA of 56.6% (47.3 to 65.5%) and an NPA of 77.2% (68.9 to 84.1%). The approaches revealed pathogens undetected by provider-ordered testing (Ureaplasma parvum, Tropheryma whipplei, severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2], rhinovirus, and cytomegalovirus [CMV]), although not all pathogens detected by provider-ordered testing were identified by the NGS workflows. The RPIP targeted workflow required more time and reagents for library preparation but streamlined bioinformatic analysis, whereas the metagenomic assay was less demanding technically but required complex bioinformatic analysis. The results from both workflows were interpreted utilizing standardized criteria, which is necessary to avoid reporting nonpathogenic organisms. The RPIP targeted workflow identified AMR markers associated with phenotypic resistance in some bacteria but incorrectly identified blaOXA genes in Pseudomonas aeruginosa as being associated with carbapenem resistance. These workflows could serve as adjunctive testing with, but not as a replacement for, standard microbiology techniques.

Keywords: diagnostics; lower respiratory tract infection; next-generation sequencing.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

FIG 1

Overview of methods for performance studies. Time estimates included in brackets are based on runs containing 24 samples and reported by those performing the assay steps for this study. Each specimen underwent extraction with or without bead beating prior to the combination of eluates. Eluates from each specimen were processed with the metagenomic and RPIP targeted workflows. Data from each workflow were evaluated using the same conditional reporting guidelines and compared to composite clinical standard results obtained for the specimen. Identical processing and NGS workflows were utilized to establish analytical sensitivity using spiked samples, with comparisons being made to the organism pools rather than a composite clinical standard.

FIG 2

Relative distribution of analytes detected by NGS workflows. Sample counts per number of analytes for the metagenomic NGS workflow are number of analytes (number of samples): 1 (33), 2 (25), 3 (16), 4 (32), 10 to 24 (34), 25 to 50 (13), and >50 (20); 98 analytes were detected in the sample containing the highest number for this workflow. Sample counts per number of analytes for the RPIP targeted NGS workflow are 1 (59), 2 (32), 3 (12), 4 to 9 (15), 10 (5), 25 to 50 (0), and >50 (0); 14 analytes were detected in the sample containing the highest number for this workflow.

FIG 3

Relationship of bacteria quantified by standard methods and those detected by NGS workflows. (A) True-positive (TP) and false-negative (FN) results per workflow. Each data point represents bacteria isolated and quantified from standard aerobic cultures (n = 37). Isolates reported as ≥10,000 CFU/mL were plotted at 10,000 CFU/mL. Bacteria detected by standard culture with semiquantification or without quantification were not included. The metagenomic NGS (mNGS) workflow detected 11 of 13 isolates (84.6%) quantified at >10,000 CFU/mL but did not detect 21 of 24 isolates (87.5%) quantified at <10,000 CFU/mL. Similarly, the RPIP targeted workflow detected 9 of 13 isolates (69.2%) quantified at >10,000 CFU/mL but did not detect 20 of 24 isolates (83.3%) quantified at <10,000 CFU/mL. (B and C) Relationship of NGS quantification methods to relative culture abundance for true-positive samples. Statistical comparisons were made using Mann-Whitney testing (P = 0.02 for mNGS, and P = 0.03 for RPIP targeted NGS). Error bars represent standard deviations. Note the difference in the y axes.